[ { "key": "BGIUC5UU", "version": 1407, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/BGIUC5UU", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/BGIUC5UU", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Burke et al.", "parsedDate": "2016-10-16", "numChildren": 0 }, "data": { "key": "BGIUC5UU", "version": 1407, "itemType": "journalArticle", "title": "Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ", "creators": [ { "creatorType": "author", "firstName": "Danielle L.", "lastName": "Burke" }, { "creatorType": "author", "firstName": "Joie", "lastName": "Ensor" }, { "creatorType": "author", "firstName": "Richard D.", "lastName": "Riley" } ], "abstractNote": "Meta-analysis using individual participant data (IPD) obtains and synthesises the raw, participant-level data from a set of relevant studies. The IPD approach is becoming an increasingly popular tool as an alternative to traditional aggregate data meta-analysis, especially as it avoids reliance on published results and provides an opportunity to investigate individual-level interactions, such as treatment-effect modifiers. There are two statistical approaches for conducting an IPD meta-analysis: one-stage and two-stage. The one-stage approach analyses the IPD from all studies simultaneously, for example, in a hierarchical regression model with random effects. The two-stage approach derives aggregate data (such as effect estimates) in each study separately and then combines these in a traditional meta-analysis model. There have been numerous comparisons of the one-stage and two-stage approaches via theoretical consideration, simulation and empirical examples, yet there remains confusion regarding when each approach should be adopted, and indeed why they may differ. In this tutorial paper, we outline the key statistical methods for one-stage and two-stage IPD meta-analyses, and provide 10 key reasons why they may produce different summary results. We explain that most differences arise because of different modelling assumptions, rather than the choice of one-stage or two-stage itself. We illustrate the concepts with recently published IPD meta-analyses, summarise key statistical software and provide recommendations for future IPD meta-analyses. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.", "publicationTitle": "Statistics in Medicine", "publisher": "", "place": "", "date": "Oct 16, 2016", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Stat Med", "DOI": "10.1002/sim.7141", "citationKey": "", "url": "", "accessDate": "", "PMID": "27747915", "PMCID": "", "ISSN": "1097-0258", "archive": "", "archiveLocation": "", "shortTitle": "Meta-analysis using individual participant data", "language": "ENG", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Conceptual Issues" }, { "tag": "Didactic/Good Practice/Recommendation" }, { "tag": "Outcome-binary" }, { "tag": "Outcome-continuous" }, { "tag": "Outcome-survival" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-07T13:26:18Z", "dateModified": "2016-11-07T13:26:18Z" } }, { "key": "ZFP7ID8P", "version": 1398, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/ZFP7ID8P", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/ZFP7ID8P", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Tierney et al.", "parsedDate": "2015-07-21", "numChildren": 0 }, "data": { "key": "ZFP7ID8P", "version": 1398, "itemType": "journalArticle", "title": "Individual Participant Data (IPD) Meta-analyses of Randomised Controlled Trials: Guidance on Their Use", "creators": [ { "creatorType": "author", "firstName": "Jayne F.", "lastName": "Tierney" }, { "creatorType": "author", "firstName": "Claire", "lastName": "Vale" }, { "creatorType": "author", "firstName": "Richard", "lastName": "Riley" }, { "creatorType": "author", "firstName": "Catrin Tudur", "lastName": "Smith" }, { "creatorType": "author", "firstName": "Lesley", "lastName": "Stewart" }, { "creatorType": "author", "firstName": "Mike", "lastName": "Clarke" }, { "creatorType": "author", "firstName": "Maroeska", "lastName": "Rovers" } ], "abstractNote": "", "publicationTitle": "PLOS Medicine", "publisher": "", "place": "", "date": "2015-7-21", "volume": "12", "issue": "7", "section": "", "partNumber": "", "partTitle": "", "pages": "e1001855", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1371/journal.pmed.1001855", "citationKey": "", "url": "http://dx.plos.org/10.1371/journal.pmed.1001855", "accessDate": "2015-08-04T11:33:25Z", "PMID": "", "PMCID": "", "ISSN": "1549-1676", "archive": "", "archiveLocation": "", "shortTitle": "Individual Participant Data (IPD) Meta-analyses of Randomised Controlled Trials", "language": "en", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Conceptual Issues" }, { "tag": "Didactic/Good Practice/Recommendation" }, { "tag": "Heterogeneity" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T10:56:21Z", "dateModified": "2016-11-04T10:56:21Z" } }, { "key": "K3M2B33V", "version": 1407, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/K3M2B33V", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/K3M2B33V", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Verde and Ohmann", "parsedDate": "2015-03", "numChildren": 0 }, "data": { "key": "K3M2B33V", "version": 1407, "itemType": "journalArticle", "title": "Combining randomized and non-randomized evidence in clinical research: a review of methods and applications", "creators": [ { "creatorType": "author", "firstName": "Pablo E.", "lastName": "Verde" }, { "creatorType": "author", "firstName": "Christian", "lastName": "Ohmann" } ], "abstractNote": "Researchers may have multiple motivations for combining disparate pieces of evidence in a meta-analysis, such as generalizing experimental results or increasing the power to detect an effect that a single study is not able to detect. However, while in meta-analysis, the main question may be simple, the structure of evidence available to answer it may be complex. As a consequence, combining disparate pieces of evidence becomes a challenge. In this review, we cover statistical methods that have been used for the evidence-synthesis of different study types with the same outcome and similar interventions. For the methodological review, a literature retrieval in the area of generalized evidence-synthesis was performed, and publications were identified, assessed, grouped and classified. Furthermore real applications of these methods in medicine were identified and described. For these approaches, 39 real clinical applications could be identified. A new classification of methods is provided, which takes into account: the inferential approach, the bias modeling, the hierarchical structure, and the use of graphical modeling. We conclude with a discussion of pros and cons of our approach and give some practical advice.", "publicationTitle": "Research Synthesis Methods", "publisher": "", "place": "", "date": "Mar 2015", "volume": "6", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "45-62", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Res Synth Methods", "DOI": "10.1002/jrsm.1122", "citationKey": "", "url": "", "accessDate": "", "PMID": "26035469", "PMCID": "", "ISSN": "1759-2887", "archive": "", "archiveLocation": "", "shortTitle": "Combining randomized and non-randomized evidence in clinical research", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Cross-design" }, { "tag": "Didactic/Good Practice/Recommendation" }, { "tag": "Ind Comp" }, { "tag": "Literature review" }, { "tag": "Methodology overview" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T10:51:44Z", "dateModified": "2016-11-04T10:51:44Z" } }, { "key": "367R6HC7", "version": 1407, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/367R6HC7", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/367R6HC7", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Verde et al.", "parsedDate": "2015-11-22", "numChildren": 0 }, "data": { "key": "367R6HC7", "version": 1407, "itemType": "journalArticle", "title": "Bayesian evidence synthesis for exploring generalizability of treatment effects: a case study of combining randomized and non-randomized results in diabetes", "creators": [ { "creatorType": "author", "firstName": "Pablo E.", "lastName": "Verde" }, { "creatorType": "author", "firstName": "Christian", "lastName": "Ohmann" }, { "creatorType": "author", "firstName": "Stephan", "lastName": "Morbach" }, { "creatorType": "author", "firstName": "Andrea", "lastName": "Icks" } ], "abstractNote": "In this paper, we present a unified modeling framework to combine aggregated data from randomized controlled trials (RCTs) with individual participant data (IPD) from observational studies. Rather than simply pooling the available evidence into an overall treatment effect, adjusted for potential confounding, the intention of this work is to explore treatment effects in specific patient populations reflected by the IPD. In this way, by collecting IPD, we can potentially gain new insights from RCTs' results, which cannot be seen using only a meta-analysis of RCTs. We present a new Bayesian hierarchical meta-regression model, which combines submodels, representing different types of data into a coherent analysis. Predictors of baseline risk are estimated from the individual data. Simultaneously, a bivariate random effects distribution of baseline risk and treatment effects is estimated from the combined individual and aggregate data. Therefore, given a subgroup of interest, the estimated treatment effect can be calculated through its correlation with baseline risk. We highlight different types of model parameters: those that are the focus of inference (e.g., treatment effect in a subgroup of patients) and those that are used to adjust for biases introduced by data collection processes (e.g., internal or external validity). The model is applied to a case study where RCTs' results, investigating efficacy in the treatment of diabetic foot problems, are extrapolated to groups of patients treated in medical routine and who were enrolled in a prospective cohort study. Copyright © 2015 John Wiley & Sons, Ltd.", "publicationTitle": "Statistics in Medicine", "publisher": "", "place": "", "date": "Nov 22, 2015", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Stat Med", "DOI": "10.1002/sim.6809", "citationKey": "", "url": "", "accessDate": "", "PMID": "26593632", "PMCID": "", "ISSN": "1097-0258", "archive": "", "archiveLocation": "", "shortTitle": "Bayesian evidence synthesis for exploring generalizability of treatment effects", "language": "ENG", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Cross-design" }, { "tag": "Empirical Assessment" }, { "tag": "IPD+AD" }, { "tag": "Methodology development" }, { "tag": "Outcome-binary" }, { "tag": "Software-BUGS" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T10:47:57Z", "dateModified": "2016-11-04T10:47:57Z" } }, { "key": "MXZ4G6DK", "version": 1407, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/MXZ4G6DK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/MXZ4G6DK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Riley et al.", "parsedDate": "2015-06", "numChildren": 0 }, "data": { "key": "MXZ4G6DK", "version": 1407, "itemType": "journalArticle", "title": "Multivariate meta-analysis using individual participant data", "creators": [ { "creatorType": "author", "firstName": "R. D.", "lastName": "Riley" }, { "creatorType": "author", "firstName": "M. J.", "lastName": "Price" }, { "creatorType": "author", "firstName": "D.", "lastName": "Jackson" }, { "creatorType": "author", "firstName": "M.", "lastName": "Wardle" }, { "creatorType": "author", "firstName": "F.", "lastName": "Gueyffier" }, { "creatorType": "author", "firstName": "J.", "lastName": "Wang" }, { "creatorType": "author", "firstName": "J. A.", "lastName": "Staessen" }, { "creatorType": "author", "firstName": "I. R.", "lastName": "White" } ], "abstractNote": "When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.", "publicationTitle": "Research Synthesis Methods", "publisher": "", "place": "", "date": "Jun 2015", "volume": "6", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "157-174", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Res Synth Methods", "DOI": "10.1002/jrsm.1129", "citationKey": "", "url": "", "accessDate": "", "PMID": "26099484", "PMCID": "", "ISSN": "1759-2887", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Conceptual Issues" }, { "tag": "Didactic/Good Practice/Recommendation" }, { "tag": "Heterogeneity" }, { "tag": "Ind Comp" }, { "tag": "Methodology overview" }, { "tag": "Outcome-binary" }, { "tag": "Outcome-continuous" }, { "tag": "Outcome-other" }, { "tag": "Outcome-survival" }, { "tag": "Software-BUGS" }, { "tag": "Software-STATA" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T10:45:14Z", "dateModified": "2016-11-04T10:45:14Z" } }, { "key": "8IEBQEKA", "version": 1407, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/8IEBQEKA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/8IEBQEKA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Quartagno and Carpenter", "parsedDate": "2015-12-17", "numChildren": 0 }, "data": { "key": "8IEBQEKA", "version": 1407, "itemType": "journalArticle", "title": "Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariates", "creators": [ { "creatorType": "author", "firstName": "M.", "lastName": "Quartagno" }, { "creatorType": "author", "firstName": "J. R.", "lastName": "Carpenter" } ], "abstractNote": "Recently, multiple imputation has been proposed as a tool for individual patient data meta-analysis with sporadically missing observations, and it has been suggested that within-study imputation is usually preferable. However, such within study imputation cannot handle variables that are completely missing within studies. Further, if some of the contributing studies are relatively small, it may be appropriate to share information across studies when imputing. In this paper, we develop and evaluate a joint modelling approach to multiple imputation of individual patient data in meta-analysis, with an across-study probability distribution for the study specific covariance matrices. This retains the flexibility to allow for between-study heterogeneity when imputing while allowing (i) sharing information on the covariance matrix across studies when this is appropriate, and (ii) imputing variables that are wholly missing from studies. Simulation results show both equivalent performance to the within-study imputation approach where this is valid, and good results in more general, practically relevant, scenarios with studies of very different sizes, non-negligible between-study heterogeneity and wholly missing variables. We illustrate our approach using data from an individual patient data meta-analysis of hypertension trials. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.", "publicationTitle": "Statistics in Medicine", "publisher": "", "place": "", "date": "Dec 17, 2015", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Stat Med", "DOI": "10.1002/sim.6837", "citationKey": "", "url": "", "accessDate": "", "PMID": "26681666", "PMCID": "", "ISSN": "1097-0258", "archive": "", "archiveLocation": "", "shortTitle": "Multiple imputation for IPD meta-analysis", "language": "ENG", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Methodology development" }, { "tag": "Missing Data" }, { "tag": "Outcome-continuous" }, { "tag": "Simulation" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T10:42:07Z", "dateModified": "2016-11-04T10:42:07Z" } }, { "key": "AXGEAA57", "version": 1407, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/AXGEAA57", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/AXGEAA57", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Kline et al.", "parsedDate": "2015-12-17", "numChildren": 0 }, "data": { "key": "AXGEAA57", "version": 1407, "itemType": "journalArticle", "title": "Comparing multiple imputation methods for systematically missing subject-level data", "creators": [ { "creatorType": "author", "firstName": "David", "lastName": "Kline" }, { "creatorType": "author", "firstName": "Rebecca", "lastName": "Andridge" }, { "creatorType": "author", "firstName": "Eloise", "lastName": "Kaizar" } ], "abstractNote": "When conducting research synthesis, the collection of studies that will be combined often do not measure the same set of variables, which creates missing data. When the studies to combine are longitudinal, missing data can occur on the observation-level (time-varying) or the subject-level (non-time-varying). Traditionally, the focus of missing data methods for longitudinal data has been on missing observation-level variables. In this paper, we focus on missing subject-level variables and compare two multiple imputation approaches: a joint modeling approach and a sequential conditional modeling approach. We find the joint modeling approach to be preferable to the sequential conditional approach, except when the covariance structure of the repeated outcome for each individual has homogenous variance and exchangeable correlation. Specifically, the regression coefficient estimates from an analysis incorporating imputed values based on the sequential conditional method are attenuated and less efficient than those from the joint method. Remarkably, the estimates from the sequential conditional method are often less efficient than a complete case analysis, which, in the context of research synthesis, implies that we lose efficiency by combining studies. Copyright © 2015 John Wiley & Sons, Ltd.", "publicationTitle": "Research Synthesis Methods", "publisher": "", "place": "", "date": "Dec 17, 2015", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Res Synth Methods", "DOI": "10.1002/jrsm.1192", "citationKey": "", "url": "", "accessDate": "", "PMID": "26679326", "PMCID": "", "ISSN": "1759-2887", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "ENG", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Empirical Assessment" }, { "tag": "Methodology overview" }, { "tag": "Missing Data" }, { "tag": "Outcome-survival" }, { "tag": "Simulation" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T10:39:15Z", "dateModified": "2016-11-04T10:39:15Z" } }, { "key": "QJG2CK66", "version": 1407, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/QJG2CK66", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/QJG2CK66", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Vale et al.", "parsedDate": "2015", "numChildren": 0 }, "data": { "key": "QJG2CK66", "version": 1407, "itemType": "journalArticle", "title": "Uptake of systematic reviews and meta-analyses based on individual participant data in clinical practice guidelines: descriptive study", "creators": [ { "creatorType": "author", "firstName": "Claire L.", "lastName": "Vale" }, { "creatorType": "author", "firstName": "Larysa H. M.", "lastName": "Rydzewska" }, { "creatorType": "author", "firstName": "Maroeska M.", "lastName": "Rovers" }, { "creatorType": "author", "firstName": "Jonathan R.", "lastName": "Emberson" }, { "creatorType": "author", "firstName": "François", "lastName": "Gueyffier" }, { "creatorType": "author", "firstName": "Lesley A.", "lastName": "Stewart" }, { "creatorType": "author", "name": "Cochrane IPD Meta-analysis Methods Group" } ], "abstractNote": "OBJECTIVE: To establish the extent to which systematic reviews and meta-analyses of individual participant data (IPD) are being used to inform the recommendations included in published clinical guidelines.\nDESIGN: Descriptive study.\nSETTING: Database maintained by the Cochrane IPD Meta-analysis Methods Group, supplemented by records of published IPD meta-analyses held in a separate database.\nPOPULATION: A test sample of systematic reviews of randomised controlled trials that included a meta-analysis of IPD, and a separate sample of clinical guidelines, matched to the IPD meta-analyses according to medical condition, interventions, populations, and dates of publication.\nDATA EXTRACTION: Descriptive information on each guideline was extracted along with evidence showing use or critical appraisal, or both, of the IPD meta-analysis within the guideline; recommendations based directly on its findings and the use of other systematic reviews in the guideline.\nRESULTS: Based on 33 IPD meta-analyses and 177 eligible, matched clinical guidelines there was evidence that IPD meta-analyses were being under-utilised. Only 66 guidelines (37%) cited a matched IPD meta-analysis. Around a third of these (n=22, 34%) had critically appraised the IPD meta-analysis. Recommendations based directly on the matched IPD meta-analyses were identified for only 18 of the 66 guidelines (27%). For the guidelines that did not cite a matched IPD meta-analysis (n=111, 63%), search dates had preceded the publication of the IPD meta-analysis in 23 cases (21%); however, for the remainder, there was no obvious reasons why the IPD meta-analysis had not been cited.\nCONCLUSIONS: Our results indicate that systematic reviews and meta-analyses based on IPD are being under-utilised. Guideline developers should routinely seek good quality and up to date IPD meta-analyses to inform guidelines. Increased use of IPD meta-analyses could lead to improved guidelines ensuring that routine patient care is based on the most reliable evidence available.", "publicationTitle": "BMJ (Clinical research ed.)", "publisher": "", "place": "", "date": "2015", "volume": "350", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "h1088", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "BMJ", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "25747860", "PMCID": "PMC4353308", "ISSN": "1756-1833", "archive": "", "archiveLocation": "", "shortTitle": "Uptake of systematic reviews and meta-analyses based on individual participant data in clinical practice guidelines", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Literature review" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T10:36:24Z", "dateModified": "2016-11-04T10:36:24Z" } }, { "key": "W767X75U", "version": 1398, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/W767X75U", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/W767X75U", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Tierney et al.", "parsedDate": "2015", "numChildren": 0 }, "data": { "key": "W767X75U", "version": 1398, "itemType": "journalArticle", "title": "Appraising individual participant data (IPD) meta-analyses of randomised controlled trials", "creators": [ { "creatorType": "author", "firstName": "Jayne F", "lastName": "Tierney" }, { "creatorType": "author", "firstName": "C", "lastName": "Vale" }, { "creatorType": "author", "firstName": "Richard D", "lastName": "Riley" }, { "creatorType": "author", "firstName": "Catrin", "lastName": "Tudur Smith" }, { "creatorType": "author", "firstName": "Lesley", "lastName": "Stewart" }, { "creatorType": "author", "firstName": "M", "lastName": "Clarke" }, { "creatorType": "author", "firstName": "Maroeska", "lastName": "Rovers" } ], "abstractNote": "", "publicationTitle": "PLoS Medicine", "publisher": "", "place": "", "date": "2015", "volume": "12", "issue": "7", "section": "", "partNumber": "", "partTitle": "", "pages": "e1001855", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "PLoS Med.", "DOI": "10.1371/journal.pmed.1001855", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Conceptual Issues" }, { "tag": "Didactic/Good Practice/Recommendation" }, { "tag": "Heterogeneity" }, { "tag": "Risk of bias" }, { "tag": "Update 2016" } ], "collections": [], "relations": {}, "dateAdded": "2016-11-04T10:33:50Z", "dateModified": "2016-11-04T10:33:50Z" } }, { "key": "7T9ZUP2E", "version": 1407, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/7T9ZUP2E", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/7T9ZUP2E", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Stewart et al.", "parsedDate": "2015-04-28", "numChildren": 0 }, "data": { "key": "7T9ZUP2E", "version": 1407, "itemType": "journalArticle", "title": "Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data: the PRISMA-IPD Statement", "creators": [ { "creatorType": "author", "firstName": "Lesley A.", "lastName": "Stewart" }, { "creatorType": "author", "firstName": "Mike", "lastName": "Clarke" }, { "creatorType": "author", "firstName": "Maroeska", "lastName": "Rovers" }, { "creatorType": "author", "firstName": "Richard D.", "lastName": "Riley" }, { "creatorType": "author", "firstName": "Mark", "lastName": "Simmonds" }, { "creatorType": "author", "firstName": "Gavin", "lastName": "Stewart" }, { "creatorType": "author", "firstName": "Jayne F.", "lastName": "Tierney" }, { "creatorType": "author", "name": "PRISMA-IPD Development Group" } ], "abstractNote": "IMPORTANCE: Systematic reviews and meta-analyses of individual participant data (IPD) aim to collect, check, and reanalyze individual-level data from all studies addressing a particular research question and are therefore considered a gold standard approach to evidence synthesis. They are likely to be used with increasing frequency as current initiatives to share clinical trial data gain momentum and may be particularly important in reviewing controversial therapeutic areas.\nOBJECTIVE: To develop PRISMA-IPD as a stand-alone extension to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement, tailored to the specific requirements of reporting systematic reviews and meta-analyses of IPD. Although developed primarily for reviews of randomized trials, many items will apply in other contexts, including reviews of diagnosis and prognosis.\nDESIGN: Development of PRISMA-IPD followed the EQUATOR Network framework guidance and used the existing standard PRISMA Statement as a starting point to draft additional relevant material. A web-based survey informed discussion at an international workshop that included researchers, clinicians, methodologists experienced in conducting systematic reviews and meta-analyses of IPD, and journal editors. The statement was drafted and iterative refinements were made by the project, advisory, and development groups. The PRISMA-IPD Development Group reached agreement on the PRISMA-IPD checklist and flow diagram by consensus.\nFINDINGS: Compared with standard PRISMA, the PRISMA-IPD checklist includes 3 new items that address (1) methods of checking the integrity of the IPD (such as pattern of randomization, data consistency, baseline imbalance, and missing data), (2) reporting any important issues that emerge, and (3) exploring variation (such as whether certain types of individual benefit more from the intervention than others). A further additional item was created by reorganization of standard PRISMA items relating to interpreting results. Wording was modified in 23 items to reflect the IPD approach.\nCONCLUSIONS AND RELEVANCE: PRISMA-IPD provides guidelines for reporting systematic reviews and meta-analyses of IPD.", "publicationTitle": "JAMA", "publisher": "", "place": "", "date": "Apr 28, 2015", "volume": "313", "issue": "16", "section": "", "partNumber": "", "partTitle": "", "pages": "1657-1665", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "JAMA", "DOI": "10.1001/jama.2015.3656", "citationKey": "", "url": "", "accessDate": "", "PMID": "25919529", "PMCID": "", "ISSN": "1538-3598", "archive": "", "archiveLocation": "", "shortTitle": "Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Didactic/Good Practice/Recommendation" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T10:32:59Z", "dateModified": "2016-11-04T10:32:59Z" } }, { "key": "NTDXCCU7", "version": 1407, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/NTDXCCU7", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/NTDXCCU7", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Gomes et al.", "parsedDate": "2016-09-20", "numChildren": 0 }, "data": { "key": "NTDXCCU7", "version": 1407, "itemType": "journalArticle", "title": "Handling incomplete correlated continuous and binary outcomes in meta-analysis of individual participant data", "creators": [ { "creatorType": "author", "firstName": "Manuel", "lastName": "Gomes" }, { "creatorType": "author", "firstName": "Laura", "lastName": "Hatfield" }, { "creatorType": "author", "firstName": "Sharon-Lise", "lastName": "Normand" } ], "abstractNote": "Meta-analysis of individual participant data (IPD) is increasingly utilised to improve the estimation of treatment effects, particularly among different participant subgroups. An important concern in IPD meta-analysis relates to partially or completely missing outcomes for some studies, a problem exacerbated when interest is on multiple discrete and continuous outcomes. When leveraging information from incomplete correlated outcomes across studies, the fully observed outcomes may provide important information about the incompleteness of the other outcomes. In this paper, we compare two models for handling incomplete continuous and binary outcomes in IPD meta-analysis: a joint hierarchical model and a sequence of full conditional mixed models. We illustrate how these approaches incorporate the correlation across the multiple outcomes and the between-study heterogeneity when addressing the missing data. Simulations characterise the performance of the methods across a range of scenarios which differ according to the proportion and type of missingness, strength of correlation between outcomes and the number of studies. The joint model provided confidence interval coverage consistently closer to nominal levels and lower mean squared error compared with the fully conditional approach across the scenarios considered. Methods are illustrated in a meta-analysis of randomised controlled trials comparing the effectiveness of implantable cardioverter-defibrillator devices alone to implantable cardioverter-defibrillator combined with cardiac resynchronisation therapy for treating patients with chronic heart failure. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.", "publicationTitle": "Statistics in Medicine", "publisher": "", "place": "", "date": "Sep 20, 2016", "volume": "35", "issue": "21", "section": "", "partNumber": "", "partTitle": "", "pages": "3676-3689", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Stat Med", "DOI": "10.1002/sim.6969", "citationKey": "", "url": "", "accessDate": "", "PMID": "27090498", "PMCID": "", "ISSN": "1097-0258", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Empirical Assessment" }, { "tag": "Missing Data" }, { "tag": "Outcome-binary" }, { "tag": "Outcome-continuous" }, { "tag": "Simulation" }, { "tag": "Software-BUGS" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T09:45:14Z", "dateModified": "2016-11-04T09:45:14Z" } }, { "key": "MKHZMZTA", "version": 1407, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/MKHZMZTA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/MKHZMZTA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Debray et al.", "parsedDate": "2015-08-19", "numChildren": 0 }, "data": { "key": "MKHZMZTA", "version": 1407, "itemType": "journalArticle", "title": "Get real in individual participant data (IPD) meta-analysis: a review of the methodology", "creators": [ { "creatorType": "author", "firstName": "Thomas P. A.", "lastName": "Debray" }, { "creatorType": "author", "firstName": "Karel G. M.", "lastName": "Moons" }, { "creatorType": "author", "firstName": "Gert", "lastName": "van Valkenhoef" }, { "creatorType": "author", "firstName": "Orestis", "lastName": "Efthimiou" }, { "creatorType": "author", "firstName": "Noemi", "lastName": "Hummel" }, { "creatorType": "author", "firstName": "Rolf H. H.", "lastName": "Groenwold" }, { "creatorType": "author", "firstName": "Johannes B.", "lastName": "Reitsma" }, { "creatorType": "author", "name": "GetReal methods review group" } ], "abstractNote": "Individual participant data (IPD) meta-analysis is an increasingly used approach for synthesizing and investigating treatment effect estimates. Over the past few years, numerous methods for conducting an IPD meta-analysis (IPD-MA) have been proposed, often making different assumptions and modeling choices while addressing a similar research question. We conducted a literature review to provide an overview of methods for performing an IPD-MA using evidence from clinical trials or non-randomized studies when investigating treatment efficacy. With this review, we aim to assist researchers in choosing the appropriate methods and provide recommendations on their implementation when planning and conducting an IPD-MA. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.", "publicationTitle": "Research Synthesis Methods", "publisher": "", "place": "", "date": "Aug 19, 2015", "volume": "6", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "239-309", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Res Synth Methods", "DOI": "10.1002/jrsm.1160", "citationKey": "", "url": "", "accessDate": "", "PMID": "26287812", "PMCID": "", "ISSN": "1759-2887", "archive": "", "archiveLocation": "", "shortTitle": "Get real in individual participant data (IPD) meta-analysis", "language": "ENG", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Conceptual Issues" }, { "tag": "Cross-design" }, { "tag": "Didactic/Good Practice/Recommendation" }, { "tag": "Heterogeneity" }, { "tag": "IPD+AD" }, { "tag": "Ind Comp" }, { "tag": "Literature review" }, { "tag": "Methodology overview" }, { "tag": "Missing Data" }, { "tag": "Outcome-binary" }, { "tag": "Outcome-continuous" }, { "tag": "Outcome-count" }, { "tag": "Outcome-ordinal" }, { "tag": "Outcome-other" }, { "tag": "Outcome-survival" }, { "tag": "Software-R" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T09:41:16Z", "dateModified": "2016-11-04T09:41:16Z" } }, { "key": "E8ZREBRR", "version": 1408, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/E8ZREBRR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/E8ZREBRR", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Dagne et al.", "parsedDate": "2016-02-02", "numChildren": 0 }, "data": { "key": "E8ZREBRR", "version": 1408, "itemType": "journalArticle", "title": "Testing moderation in network meta-analysis with individual participant data", "creators": [ { "creatorType": "author", "firstName": "Getachew A.", "lastName": "Dagne" }, { "creatorType": "author", "firstName": "C. Hendricks", "lastName": "Brown" }, { "creatorType": "author", "firstName": "George", "lastName": "Howe" }, { "creatorType": "author", "firstName": "Sheppard G.", "lastName": "Kellam" }, { "creatorType": "author", "firstName": "Lei", "lastName": "Liu" } ], "abstractNote": "Meta-analytic methods for combining data from multiple intervention trials are commonly used to estimate the effectiveness of an intervention. They can also be extended to study comparative effectiveness, testing which of several alternative interventions is expected to have the strongest effect. This often requires network meta-analysis (NMA), which combines trials involving direct comparison of two interventions within the same trial and indirect comparisons across trials. In this paper, we extend existing network methods for main effects to examining moderator effects, allowing for tests of whether intervention effects vary for different populations or when employed in different contexts. In addition, we study how the use of individual participant data may increase the sensitivity of NMA for detecting moderator effects, as compared with aggregate data NMA that employs study-level effect sizes in a meta-regression framework. A new NMA diagram is proposed. We also develop a generalized multilevel model for NMA that takes into account within-trial and between-trial heterogeneity and can include participant-level covariates. Within this framework, we present definitions of homogeneity and consistency across trials. A simulation study based on this model is used to assess effects on power to detect both main and moderator effects. Results show that power to detect moderation is substantially greater when applied to individual participant data as compared with study-level effects. We illustrate the use of this method by applying it to data from a classroom-based randomized study that involved two sub-trials, each comparing interventions that were contrasted with separate control groups. Copyright © 2016 John Wiley & Sons, Ltd.", "publicationTitle": "Statistics in Medicine", "publisher": "", "place": "", "date": "Feb 2, 2016", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Stat Med", "DOI": "10.1002/sim.6883", "citationKey": "", "url": "", "accessDate": "", "PMID": "26841367", "PMCID": "", "ISSN": "1097-0258", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "ENG", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Empirical Assessment" }, { "tag": "Heterogeneity" }, { "tag": "Ind Comp" }, { "tag": "Methodology development" }, { "tag": "Simulation" }, { "tag": "Software-SAS" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T09:33:53Z", "dateModified": "2016-11-04T09:33:53Z" } }, { "key": "7VGRVDIK", "version": 1408, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/7VGRVDIK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/7VGRVDIK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Debray et al.", "parsedDate": "2016-08-02", "numChildren": 0 }, "data": { "key": "7VGRVDIK", "version": 1408, "itemType": "journalArticle", "title": "An overview of methods for network meta-analysis using individual participant data: when do benefits arise?", "creators": [ { "creatorType": "author", "firstName": "Thomas Pa", "lastName": "Debray" }, { "creatorType": "author", "firstName": "Ewoud", "lastName": "Schuit" }, { "creatorType": "author", "firstName": "Orestis", "lastName": "Efthimiou" }, { "creatorType": "author", "firstName": "Johannes B.", "lastName": "Reitsma" }, { "creatorType": "author", "firstName": "John Pa", "lastName": "Ioannidis" }, { "creatorType": "author", "firstName": "Georgia", "lastName": "Salanti" }, { "creatorType": "author", "firstName": "Karel Gm", "lastName": "Moons" }, { "creatorType": "author", "name": "GetReal Workpackage" } ], "abstractNote": "Network meta-analysis (NMA) is a common approach to summarizing relative treatment effects from randomized trials with different treatment comparisons. Most NMAs are based on published aggregate data (AD) and have limited possibilities for investigating the extent of network consistency and between-study heterogeneity. Given that individual participant data (IPD) are considered the gold standard in evidence synthesis, we explored statistical methods for IPD-NMA and investigated their potential advantages and limitations, compared with AD-NMA. We discuss several one-stage random-effects NMA models that account for within-trial imbalances, treatment effect modifiers, missing response data and longitudinal responses. We illustrate all models in a case study of 18 antidepressant trials with a continuous endpoint (the Hamilton Depression Score). All trials suffered from drop-out; missingness of longitudinal responses ranged from 21 to 41% after 6 weeks follow-up. Our results indicate that NMA based on IPD may lead to increased precision of estimated treatment effects. Furthermore, it can help to improve network consistency and explain between-study heterogeneity by adjusting for participant-level effect modifiers and adopting more advanced models for dealing with missing response data. We conclude that implementation of IPD-NMA should be considered when trials are affected by substantial drop-out rate, and when treatment effects are potentially influenced by participant-level covariates.", "publicationTitle": "Statistical Methods in Medical Research", "publisher": "", "place": "", "date": "Aug 2, 2016", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Stat Methods Med Res", "DOI": "10.1177/0962280216660741", "citationKey": "", "url": "", "accessDate": "", "PMID": "27487843", "PMCID": "", "ISSN": "1477-0334", "archive": "", "archiveLocation": "", "shortTitle": "An overview of methods for network meta-analysis using individual participant data", "language": "ENG", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Empirical Assessment" }, { "tag": "Heterogeneity" }, { "tag": "Ind Comp" }, { "tag": "Methodology overview" }, { "tag": "Missing Data" }, { "tag": "Outcome-continuous" }, { "tag": "Outcome-other" }, { "tag": "Software-BUGS" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T09:31:02Z", "dateModified": "2016-11-04T09:31:02Z" } }, { "key": "EMH4DAGE", "version": 1408, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/EMH4DAGE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/EMH4DAGE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Hollis et al.", "parsedDate": "2016-07-08", "numChildren": 0 }, "data": { "key": "EMH4DAGE", "version": 1408, "itemType": "journalArticle", "title": "Best practice for analysis of shared clinical trial data", "creators": [ { "creatorType": "author", "firstName": "Sally", "lastName": "Hollis" }, { "creatorType": "author", "firstName": "Christine", "lastName": "Fletcher" }, { "creatorType": "author", "firstName": "Frances", "lastName": "Lynn" }, { "creatorType": "author", "firstName": "Hans-Joerg", "lastName": "Urban" }, { "creatorType": "author", "firstName": "Janice", "lastName": "Branson" }, { "creatorType": "author", "firstName": "Hans-Ulrich", "lastName": "Burger" }, { "creatorType": "author", "firstName": "Catrin", "lastName": "Tudur Smith" }, { "creatorType": "author", "firstName": "Matthew R.", "lastName": "Sydes" }, { "creatorType": "author", "firstName": "Christoph", "lastName": "Gerlinger" } ], "abstractNote": "BACKGROUND: Greater transparency, including sharing of patient-level data for further research, is an increasingly important topic for organisations who sponsor, fund and conduct clinical trials. This is a major paradigm shift with the aim of maximising the value of patient-level data from clinical trials for the benefit of future patients and society. We consider the analysis of shared clinical trial data in three broad categories: (1) reanalysis - further investigation of the efficacy and safety of the randomized intervention, (2) meta-analysis, and (3) supplemental analysis for a research question that is not directly assessing the randomized intervention.\nDISCUSSION: In order to support appropriate interpretation and limit the risk of misleading findings, analysis of shared clinical trial data should have a pre-specified analysis plan. However, it is not generally possible to limit bias and control multiplicity to the extent that is possible in the original trial design, conduct and analysis, and this should be acknowledged and taken into account when interpreting results. We highlight a number of areas where specific considerations arise in planning, conducting, interpreting and reporting analyses of shared clinical trial data. A key issue is that that these analyses essentially share many of the limitations of any post hoc analyses beyond the original specified analyses. The use of individual patient data in meta-analysis can provide increased precision and reduce bias. Supplemental analyses are subject to many of the same issues that arise in broader epidemiological analyses. Specific discussion topics are addressed within each of these areas. Increased provision of patient-level data from industry and academic-led clinical trials for secondary research can benefit future patients and society. Responsible data sharing, including transparency of the research objectives, analysis plans and of the results will support appropriate interpretation and help to address the risk of misleading results and avoid unfounded health scares.", "publicationTitle": "BMC medical research methodology", "publisher": "", "place": "", "date": "Jul 08, 2016", "volume": "16 Suppl 1", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "76", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "BMC Med Res Methodol", "DOI": "10.1186/s12874-016-0170-y", "citationKey": "", "url": "", "accessDate": "", "PMID": "27410240", "PMCID": "PMC4943488", "ISSN": "1471-2288", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "ENG", "libraryCatalog": "PubMed", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Conceptual Issues" }, { "tag": "Didactic/Good Practice/Recommendation" }, { "tag": "Update 2016" } ], "collections": [ "XZSGQBQ4" ], "relations": {}, "dateAdded": "2016-11-04T09:29:19Z", "dateModified": "2016-11-04T09:29:19Z" } }, { "key": "3V8JCDVU", "version": 1408, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/3V8JCDVU", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/3V8JCDVU", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Thomas et al.", "parsedDate": "2014", "numChildren": 0 }, "data": { "key": "3V8JCDVU", "version": 1408, "itemType": "journalArticle", "title": "Systematic review of methods for individual patient data meta-analysis with binary outcomes", "creators": [ { "creatorType": "author", "firstName": "Doneal", "lastName": "Thomas" }, { "creatorType": "author", "firstName": "Sanyath", "lastName": "Radji" }, { "creatorType": "author", "firstName": "Andrea", "lastName": "Benedetti" } ], "abstractNote": "BACKGROUND: Meta-analyses (MA) based on individual patient data (IPD) are regarded as the gold standard for meta-analyses and are becoming increasingly common, having several advantages over meta-analyses of summary statistics. These analyses are being undertaken in an increasing diversity of settings, often having a binary outcome. In a previous systematic review of articles published between 1999-2001, the statistical approach was seldom reported in sufficient detail, and the outcome was binary in 32% of the studies considered. Here, we explore statistical methods used for IPD-MA of binary outcomes only, a decade later.\nMETHODS: We selected 56 articles, published in 2011 that presented results from an individual patient data meta-analysis. Of these, 26 considered a binary outcome. Here, we review 26 IPD-MA published during 2011 to consider: the goal of the study and reason for conducting an IPD-MA, whether they obtained all the data they sought, the approach used in their analysis, for instance, a two-stage or a one stage model, and the assumption of fixed or random effects. We also investigated how heterogeneity across studies was described and how studies investigated the effects of covariates.\nRESULTS: 19 of the 26 IPD-MA used a one-stage approach. 9 IPD-MA used a one-stage random treatment-effect logistic regression model, allowing the treatment effect to vary across studies. Twelve IPD-MA presented some form of statistic to measure heterogeneity across studies, though these were usually calculated using two-stage approach. Subgroup analyses were undertaken in all IPD-MA that aimed to estimate a treatment effect or safety of a treatment,. Sixteen meta-analyses obtained 90% or more of the patients sought.\nCONCLUSION: Evidence from this systematic review shows that the use of binary outcomes in assessing the effects of health care problems has increased, with random effects logistic regression the most common method of analysis. Methods are still often not reported in enough detail. Results also show that heterogeneity of treatment effects is discussed in most applications.", "publicationTitle": "BMC medical research methodology", "publisher": "", "place": "", "date": "2014", "volume": "14", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "79", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "BMC Med Res Methodol", "DOI": "10.1186/1471-2288-14-79", "citationKey": "", "url": "", "accessDate": "", "PMID": "24943877", "PMCID": "PMC4074845", "ISSN": "1471-2288", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "Source: Suggestion by Debray T", "tags": [ { "tag": "Literature review" }, { "tag": "Methodology overview" }, { "tag": "Outcome-binary" } ], "collections": [ "3EF26T8S" ], "relations": {}, "dateAdded": "2014-11-11T12:31:33Z", "dateModified": "2016-03-14T09:40:10Z" } }, { "key": "I7QSCRN6", "version": 1408, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/I7QSCRN6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/I7QSCRN6", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Ravva et al.", "parsedDate": "2014-02-02", "numChildren": 0 }, "data": { "key": "I7QSCRN6", "version": 1408, "itemType": "journalArticle", "title": "A linearization approach for the model-based analysis of combined aggregate and individual patient data", "creators": [ { "creatorType": "author", "firstName": "Patanjali", "lastName": "Ravva" }, { "creatorType": "author", "firstName": "Mats O", "lastName": "Karlsson" }, { "creatorType": "author", "firstName": "Jonathan L", "lastName": "French" } ], "abstractNote": "The application of model-based meta-analysis in drug development has gained prominence recently, particularly for characterizing dose-response relationships and quantifying treatment effect sizes of competitor drugs. The models are typically nonlinear in nature and involve covariates to explain the heterogeneity in summary-level literature (or aggregate data (AD)). Inferring individual patient-level relationships from these nonlinear meta-analysis models leads to aggregation bias. Individual patient-level data (IPD) are indeed required to characterize patient-level relationships but too often this information is limited. Since combined analyses of AD and IPD allow advantage of the information they share to be taken, the models developed for AD must be derived from IPD models; in the case of linear models, the solution is a closed form, while for nonlinear models, closed form solutions do not exist. Here, we propose a linearization method based on a second order Taylor series approximation for fitting models to AD alone or combined AD and IPD. The application of this method is illustrated by an analysis of a continuous landmark endpoint, i.e., change from baseline in HbA1c at week 12, from 18 clinical trials evaluating the effects of DPP-4 inhibitors on hyperglycemia in diabetic patients. The performance of this method is demonstrated by a simulation study where the effects of varying the degree of nonlinearity and of heterogeneity in covariates (as assessed by the ratio of between-trial to within-trial variability) were studied. A dose-response relationship using an Emax model with linear and nonlinear effects of covariates on the emax parameter was used to simulate data. The simulation results showed that when an IPD model is simply used for modeling AD, the bias in the emax parameter estimate increased noticeably with an increasing degree of nonlinearity in the model, with respect to covariates. When using an appropriately derived AD model, the linearization method adequately corrected for bias. It was also noted that the bias in the model parameter estimates decreased as the ratio of between-trial to within-trial variability in covariate distribution increased. Taken together, the proposed linearization approach allows addressing the issue of aggregation bias in the particular case of nonlinear models of aggregate data. Copyright © 2013 John Wiley & Sons, Ltd.", "publicationTitle": "Statistics in medicine", "publisher": "", "place": "", "date": "Feb 2, 2014", "volume": "33", "issue": "9", "section": "", "partNumber": "", "partTitle": "", "pages": "1460-1476", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Stat Med", "DOI": "10.1002/sim.6045", "citationKey": "", "url": "", "accessDate": "", "PMID": "24488864", "PMCID": "", "ISSN": "1097-0258", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "ENG", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "Source: Suggestion by Efthimiou O", "tags": [ { "tag": "Conceptual Issues" }, { "tag": "Empirical Assessment" }, { "tag": "Heterogeneity" }, { "tag": "IPD+AD" }, { "tag": "Methodology development" }, { "tag": "Outcome-continuous" }, { "tag": "Simulation" }, { "tag": "Software-NONNEM" } ], "collections": [ "3EF26T8S" ], "relations": {}, "dateAdded": "2014-02-10T13:27:08Z", "dateModified": "2015-05-15T13:28:15Z" } }, { "key": "GRZMKX5J", "version": 1358, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/GRZMKX5J", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/GRZMKX5J", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Bowden et al.", "parsedDate": "2011", "numChildren": 0 }, "data": { "key": "GRZMKX5J", "version": 1358, "itemType": "journalArticle", "title": "Individual patient data meta-analysis of time-to-event outcomes: one-stage versus two-stage approaches for estimating the hazard ratio under a random effects model", "creators": [ { "creatorType": "author", "firstName": "Jack", "lastName": "Bowden" }, { "creatorType": "author", "firstName": "Jayne F", "lastName": "Tierney" }, { "creatorType": "author", "firstName": "Mark", "lastName": "Simmonds" }, { "creatorType": "author", "firstName": "Andrew J", "lastName": "Copas" }, { "creatorType": "author", "firstName": "Julian PT", "lastName": "Higgins" } ], "abstractNote": "Meta-analyses of individual patient data (IPD) provide a strong and authoritative basis for evidence synthesis. IPD are particularly useful when the outcome of interest is the time to an event. Methodological developments now enable the meta-analysis of time-to-event IPD using a single model, allowing treatment effect and across-trial heterogeneity parameters to be estimated simultaneously. This differs from the standard approaches used with aggregate data, and also predominantly with IPD. Facilitated by a simulation study, we investigate what these new ‘one-stage’ random-effects models offer over standard ‘two-stage’ approaches. We find that two-stage approaches represent a robust, reliable and easily implementable way to estimate treatment effects and account for heterogeneity. Nevertheless, one-stage models can be used to provide a deeper insight into the data. Software for fitting one-stage Cox models with random effects using Restricted Maximum Likelihood methodology is made available, and its use demonstrated on an IPD meta-analysis assessing post-operative radio therapy for patients with non-small cell lung cancer.", "publicationTitle": "Research Synthesis Methods", "publisher": "", "place": "", "date": "09/2011", "volume": "2", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "150-162", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Res Synth Methods", "DOI": "10.1002/jrsm.45", "citationKey": "", "url": "http://doi.wiley.com/10.1002/jrsm.45", "accessDate": "2014-02-10T09:51:55Z", "PMID": "", "PMCID": "", "ISSN": "17592879", "archive": "", "archiveLocation": "", "shortTitle": "Individual patient data meta-analysis of time-to-event outcomes", "language": "eng", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "Source: Suggestion by Debray T", "tags": [ { "tag": "Conceptual Issues" }, { "tag": "Empirical Assessment" }, { "tag": "Methodology overview" }, { "tag": "Outcome-survival" }, { "tag": "Simulation" }, { "tag": "Software-R" } ], "collections": [ "3EF26T8S" ], "relations": {}, "dateAdded": "2014-02-10T09:51:55Z", "dateModified": "2015-05-15T13:25:54Z" } }, { "key": "9D6C3X5T", "version": 1357, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/9D6C3X5T", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/9D6C3X5T", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Wells et al.", "parsedDate": "2013", "numChildren": 0 }, "data": { "key": "9D6C3X5T", "version": 1357, "itemType": "journalArticle", "title": "Checklists of methodological issues for review authors to consider when including non-randomized studies in systematic reviews", "creators": [ { "creatorType": "author", "firstName": "George A", "lastName": "Wells" }, { "creatorType": "author", "firstName": "Beverley", "lastName": "Shea" }, { "creatorType": "author", "firstName": "Julian PT", "lastName": "Higgins" }, { "creatorType": "author", "firstName": "Jonathan", "lastName": "Sterne" }, { "creatorType": "author", "firstName": "Peter", "lastName": "Tugwell" }, { "creatorType": "author", "firstName": "Barnaby C", "lastName": "Reeves" } ], "abstractNote": "Background: There is increasing interest from review authors about including non-randomized studies (NRS) in their systematic reviews of health care interventions. This series from the Ottawa Non-Randomized Studies Workshop consists of six papers identifying methodological issues when doing this.\n\nAim: To format the guidance from the preceding papers on study design and bias, confounding and meta-analysis, selective reporting, and applicability/directness into checklists of issues for review authors to consider when including NRS in a systematic review.\n\nChecklists: Checklists were devised providing frameworks to describe/assess: (1) study designs based on study design features; (2) risk of residual confounding and when to consider meta-analysing data from NRS; (3) risk of selective reporting based on the Cochrane framework for detecting selective outcome reporting in trials but extended to selective reporting of analyses; and (4) directness of evidence contributed by a study to aid integration of NRS findings into summary of findings tables.\n\nSummary: The checklists described will allow review groups to operationalize the inclusion of NRS in systematic reviews in a more consistent way. The next major step is extending the existing Cochrane Risk of Bias tool so that it can assess the risk of bias to NRS included in a review.", "publicationTitle": "Research Synthesis Methods", "publisher": "", "place": "", "date": "03/2013", "volume": "4", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "63-77", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Res Synth Methods", "DOI": "10.1002/jrsm.1077", "citationKey": "", "url": "http://doi.wiley.com/10.1002/jrsm.1077", "accessDate": "2014-02-09T14:53:34Z", "PMID": "", "PMCID": "", "ISSN": "17592879", "archive": "", "archiveLocation": "", "shortTitle": "Checklists of methodological issues for review authors to consider when including non-randomized studies in systematic reviews", "language": "eng", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "Source: RSM", "tags": [ { "tag": "Cross-design" }, { "tag": "Didactic/Good Practice/Recommendation" } ], "collections": [ "3EF26T8S" ], "relations": {}, "dateAdded": "2014-02-09T14:53:34Z", "dateModified": "2015-05-15T13:25:24Z" } }, { "key": "GDQWTRJI", "version": 1356, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/GDQWTRJI", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/GDQWTRJI", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Riley and Steyerberg", "parsedDate": "2010", "numChildren": 0 }, "data": { "key": "GDQWTRJI", "version": 1356, "itemType": "journalArticle", "title": "Meta-analysis of a binary outcome using individual participant data and aggregate data", "creators": [ { "creatorType": "author", "firstName": "Richard D.", "lastName": "Riley" }, { "creatorType": "author", "firstName": "Ewout W.", "lastName": "Steyerberg" } ], "abstractNote": "In this paper, we develop meta-analysis models that synthesize a binary outcome from health-care studies while accounting for participant-level covariates. In particular, we show how to synthesize the observed event-risk across studies while accounting for the within-study association between participant-level covariates and individual event probability. The models are adapted for situations where studies provide individual participant data (IPD), or a mixture of IPD and aggregate data. We show that the availability of IPD is crucial in at least some studies; this allows one to model potentially complex within-study associations and separate them from across-study associations, so as to account for potential ecological bias and study-level confounding. The models can produce pertinent population-level and individual-level results, such as the pooled event-risk and the covariate-specific event probability for an individual. Application is made to 14 studies of traumatic brain injury, where IPD are available for four studies and the six-month mortality risk is synthesized in relation to individual age. The results show that as individual age increases the probability of six-month mortality also increases; further, the models reveal clear evidence of ecological bias, with the mean age in each study additionally influencing an individual's mortality probability.", "publicationTitle": "Research Synthesis Methods", "publisher": "", "place": "", "date": "01/2010", "volume": "1", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "2-19", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Res Synth Methods", "DOI": "10.1002/jrsm.4", "citationKey": "", "url": "http://doi.wiley.com/10.1002/jrsm.4", "accessDate": "2014-02-07T10:41:51Z", "PMID": "", "PMCID": "", "ISSN": "17592879, 17592887", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "Source: RSM", "tags": [ { "tag": "Conceptual Issues" }, { "tag": "Empirical Assessment" }, { "tag": "Heterogeneity" }, { "tag": "IPD+AD" }, { "tag": "Methodology development" }, { "tag": "Outcome-binary" }, { "tag": "Software-BUGS" }, { "tag": "Software-SAS" }, { "tag": "Software-STATA" } ], "collections": [ "3EF26T8S" ], "relations": {}, "dateAdded": "2014-02-07T10:41:51Z", "dateModified": "2015-05-15T13:23:45Z" } }, { "key": "2CBIMB5B", "version": 1355, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/2CBIMB5B", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/2CBIMB5B", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Reeves et al.", "parsedDate": "2013", "numChildren": 0 }, "data": { "key": "2CBIMB5B", "version": 1355, "itemType": "journalArticle", "title": "An introduction to methodological issues when including non-randomised studies in systematic reviews on the effects of interventions", "creators": [ { "creatorType": "author", "firstName": "Barnaby C.", "lastName": "Reeves" }, { "creatorType": "author", "firstName": "Julian P. T.", "lastName": "Higgins" }, { "creatorType": "author", "firstName": "Craig", "lastName": "Ramsay" }, { "creatorType": "author", "firstName": "Beverley", "lastName": "Shea" }, { "creatorType": "author", "firstName": "Peter", "lastName": "Tugwell" }, { "creatorType": "author", "firstName": "George A.", "lastName": "Wells" } ], "abstractNote": "Background: Methods need to be further developed to include non-randomised studies (NRS) in systematic reviews of the effects of health care interventions. NRS are often required to answer questions about harms and interventions for which evidence from randomised controlled trials (RCTs) is not available. Methods used to review randomised controlled trials may be inappropriate or insufficient for NRS.\n\nAim and methods: A workshop was convened to discuss relevant methodological issues. Participants were invited from important stakeholder constituencies, including methods and review groups of the Cochrane and Campbell Collaborations, the Cochrane Editorial Unit and organisations that commission reviews and make health policy decisions. The aim was to discuss methods for reviewing evidence when including NRS and to formulate methodological guidance for review authors.\n\nWorkshop format: The workshop was structured around four sessions on topics considered in advance to be most critical: (i) study designs and bias; (ii) confounding and meta-analysis; (iii) selective reporting; and (iv) applicability. These sessions were scheduled between introductory and concluding sessions.\n\nSummary: This is the first of six papers and provides an overview. Subsequent papers describe the discussions and conclusions from the four main sessions (papers 2 to 5) and summarise the proposed guidance into lists of issues for review authors to consider (paper 6).", "publicationTitle": "Research Synthesis Methods", "publisher": "", "place": "", "date": "03/2013", "volume": "4", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "1-11", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Res Synth Methods", "DOI": "10.1002/jrsm.1068", "citationKey": "", "url": "http://doi.wiley.com/10.1002/jrsm.1068", "accessDate": "2014-02-18T11:20:19Z", "PMID": "", "PMCID": "", "ISSN": "17592879", "archive": "", "archiveLocation": "", "shortTitle": "An introduction to methodological issues when including non-randomised studies in systematic reviews on the effects of interventions", "language": "eng", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "Source: Suggestion by Belger M", "tags": [ { "tag": "Cross-design" }, { "tag": "Didactic/Good Practice/Recommendation" } ], "collections": [ "3EF26T8S" ], "relations": {}, "dateAdded": "2014-02-18T11:20:19Z", "dateModified": "2015-05-15T13:22:45Z" } }, { "key": "WBRKP9HV", "version": 1354, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/WBRKP9HV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/WBRKP9HV", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Jansen", "parsedDate": "2012", "numChildren": 0 }, "data": { "key": "WBRKP9HV", "version": 1354, "itemType": "journalArticle", "title": "Network meta-analysis of individual and aggregate level data", "creators": [ { "creatorType": "author", "firstName": "Jeroen P.", "lastName": "Jansen" } ], "abstractNote": "Network meta-analysis is often performed with aggregate-level data (AgD). A challenge in using AgD is that the association between a patient-level covariate and treatment effects at the study level may not reflect the individual-level effect modification. In this paper, non-linear network meta-analysis models for combining individual patient data (IPD) and AgD are presented to reduce bias and uncertainty of direct and indirect treatment effects in the presence of heterogeneity. The first method uses the same model form for IPD and AgD. With the second method, the model for AgD is obtained by integrating an underlying IPD model over the joint within-study distribution of covariates, in line with the method by Jackson et al. for ecological inferences. With simulated examples, the models are illustrated. Having IPD for a subset of studies improves estimation of treatment effects in the presence of patient-level heterogeneity. Of the two proposed non-linear models for combining IPD and AgD, the second seems less affected by bias in situations with large treatment-by-patient-level-covariate interactions, probably at the cost of greater uncertainty. Additional studies are needed to better understand when one model is favorable over the other. For network meta-analysis, it is recommended to use IPD when available.", "publicationTitle": "Research Synthesis Methods", "publisher": "", "place": "", "date": "06/2012", "volume": "3", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "177-190", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Res Synth Methods", "DOI": "10.1002/jrsm.1048", "citationKey": "", "url": "http://doi.wiley.com/10.1002/jrsm.1048", "accessDate": "2014-02-07T19:27:40Z", "PMID": "", "PMCID": "", "ISSN": "17592879", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "Source: Suggestion by Debray T", "tags": [ { "tag": "Empirical Assessment" }, { "tag": "Heterogeneity" }, { "tag": "IPD+AD" }, { "tag": "Ind Comp" }, { "tag": "Methodology development" }, { "tag": "Outcome-binary" }, { "tag": "Simulation" } ], "collections": [ "3EF26T8S" ], "relations": {}, "dateAdded": "2014-02-07T19:27:40Z", "dateModified": "2015-05-15T13:19:41Z" } }, { "key": "MUWHB496", "version": 1406, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/MUWHB496", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/MUWHB496", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Jolani et al.", "parsedDate": "2015", "numChildren": 0 }, "data": { "key": "MUWHB496", "version": 1406, "itemType": "journalArticle", "title": "Multiple imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE", "creators": [ { "creatorType": "author", "firstName": "Shahab", "lastName": "Jolani" }, { "creatorType": "author", "firstName": "Thomas P. A.", "lastName": "Debray" }, { "creatorType": "author", "firstName": "Hendrik", "lastName": "Koffijberg" }, { "creatorType": "author", "firstName": "Stef", "lastName": "van Buuren" }, { "creatorType": "author", "firstName": "Karel G.", "lastName": "Moons" } ], "abstractNote": "", "publicationTitle": "Statistics in Medicine", "publisher": "", "place": "", "date": "2015", "volume": "34", "issue": "11", "section": "", "partNumber": "", "partTitle": "", "pages": "1841–1863", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Stat Med", "DOI": "10.1002/sim.6451", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Suggestion by Debray T", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Empirical Assessment" }, { "tag": "Methodology development" }, { "tag": "Missing Data" }, { "tag": "Outcome-binary" }, { "tag": "Simulation" }, { "tag": "Software-R" } ], "collections": [ "3EF26T8S" ], "relations": {}, "dateAdded": "2014-11-11T11:02:11Z", "dateModified": "2015-05-15T13:17:33Z" } }, { "key": "JZWR58ID", "version": 1408, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/JZWR58ID", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/JZWR58ID", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Schmitz et al.", "parsedDate": "2013-07-30", "numChildren": 0 }, "data": { "key": "JZWR58ID", "version": 1408, "itemType": "journalArticle", "title": "Incorporating data from various trial designs into a mixed treatment comparison model", "creators": [ { "creatorType": "author", "firstName": "Susanne", "lastName": "Schmitz" }, { "creatorType": "author", "firstName": "Roisin", "lastName": "Adams" }, { "creatorType": "author", "firstName": "Cathal", "lastName": "Walsh" } ], "abstractNote": "Estimates of relative efficacy between alternative treatments are crucial for decision making in health care. Bayesian mixed treatment comparison models provide a powerful methodology to obtain such estimates when head-to-head evidence is not available or insufficient. In recent years, this methodology has become widely accepted and applied in economic modelling of healthcare interventions. Most evaluations only consider evidence from randomized controlled trials, while information from other trial designs is ignored. In this paper, we propose three alternative methods of combining data from different trial designs in a mixed treatment comparison model. Naive pooling is the simplest approach and does not differentiate between-trial designs. Utilizing observational data as prior information allows adjusting for bias due to trial design. The most flexible technique is a three-level hierarchical model. Such a model allows for bias adjustment while also accounting for heterogeneity between-trial designs. These techniques are illustrated using an application in rheumatoid arthritis.", "publicationTitle": "Statistics in Medicine", "publisher": "", "place": "", "date": "Jul 30, 2013", "volume": "32", "issue": "17", "section": "", "partNumber": "", "partTitle": "", "pages": "2935-2949", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Stat Med", "DOI": "10.1002/sim.5764", "citationKey": "", "url": "", "accessDate": "", "PMID": "23440610", "PMCID": "", "ISSN": "1097-0258", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "eng", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "Source: Suggestion by Groenwold R", "tags": [ { "tag": "Cross-design" }, { "tag": "Empirical Assessment" }, { "tag": "Ind Comp" }, { "tag": "Methodology overview" }, { "tag": "Outcome-continuous" } ], "collections": [ "3EF26T8S" ], "relations": {}, "dateAdded": "2015-01-06T08:54:38Z", "dateModified": "2015-01-06T08:55:12Z" } }, { "key": "NMKAS7W8", "version": 1408, "library": { "type": "group", "id": 240746, "name": "WP4 - IPD meta-analysis", "links": { "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/240746/items/NMKAS7W8", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/wp4_-_ipd_meta-analysis/items/NMKAS7W8", "type": "text/html" } }, "meta": { "createdByUser": { "id": 1250496, "username": "NightlordTW", "name": "Thomas Debray", "links": { "alternate": { "href": "https://www.zotero.org/nightlordtw", "type": "text/html" } } }, "creatorSummary": "Li et al.", "parsedDate": "2011", "numChildren": 0 }, "data": { "key": "NMKAS7W8", "version": 1408, "itemType": "journalArticle", "title": "Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes", "creators": [ { "creatorType": "author", "firstName": "Baoyue", "lastName": "Li" }, { "creatorType": "author", "firstName": "Hester F.", "lastName": "Lingsma" }, { "creatorType": "author", "firstName": "Ewout W.", "lastName": "Steyerberg" }, { "creatorType": "author", "firstName": "Emmanuel", "lastName": "Lesaffre" } ], "abstractNote": "BACKGROUND: Logistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models.\nMETHODS: We used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI) enrolled in eight Randomized Controlled Trials (RCTs) and three observational studies. We fitted logistic random effects regression models with the 5-point Glasgow Outcome Scale (GOS) as outcome, both dichotomized as well as ordinal, with center and/or trial as random effects, and as covariates age, motor score, pupil reactivity or trial. We then compared the implementations of frequentist and Bayesian methods to estimate the fixed and random effects. Frequentist approaches included R (lme4), Stata (GLLAMM), SAS (GLIMMIX and NLMIXED), MLwiN ([R]IGLS) and MIXOR, Bayesian approaches included WinBUGS, MLwiN (MCMC), R package MCMCglmm and SAS experimental procedure MCMC.Three data sets (the full data set and two sub-datasets) were analysed using basically two logistic random effects models with either one random effect for the center or two random effects for center and trial. For the ordinal outcome in the full data set also a proportional odds model with a random center effect was fitted.\nRESULTS: The packages gave similar parameter estimates for both the fixed and random effects and for the binary (and ordinal) models for the main study and when based on a relatively large number of level-1 (patient level) data compared to the number of level-2 (hospital level) data. However, when based on relatively sparse data set, i.e. when the numbers of level-1 and level-2 data units were about the same, the frequentist and Bayesian approaches showed somewhat different results. The software implementations differ considerably in flexibility, computation time, and usability. There are also differences in the availability of additional tools for model evaluation, such as diagnostic plots. The experimental SAS (version 9.2) procedure MCMC appeared to be inefficient.\nCONCLUSIONS: On relatively large data sets, the different software implementations of logistic random effects regression models produced similar results. Thus, for a large data set there seems to be no explicit preference (of course if there is no preference from a philosophical point of view) for either a frequentist or Bayesian approach (if based on vague priors). The choice for a particular implementation may largely depend on the desired flexibility, and the usability of the package. For small data sets the random effects variances are difficult to estimate. In the frequentist approaches the MLE of this variance was often estimated zero with a standard error that is either zero or could not be determined, while for Bayesian methods the estimates could depend on the chosen \"non-informative\" prior of the variance parameter. The starting value for the variance parameter may be also critical for the convergence of the Markov chain.", "publicationTitle": "BMC medical research methodology", "publisher": "", "place": "", "date": "2011", "volume": "11", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "77", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "BMC Med Res Methodol", "DOI": "10.1186/1471-2288-11-77", "citationKey": "", "url": "", "accessDate": "", "PMID": "21605357", "PMCID": "PMC3112198", "ISSN": "1471-2288", "archive": "", "archiveLocation": "", "shortTitle": "Logistic random effects regression models", "language": "eng", "libraryCatalog": "NCBI PubMed", "callNumber": "", "rights": "", "extra": "Source: cross-reference check", "tags": [ { "tag": "Empirical Assessment" }, { "tag": "Outcome-binary" }, { "tag": "Outcome-ordinal" }, { "tag": "Software-BUGS" }, { "tag": "Software-MLwiN" }, { "tag": "Software-R" }, { "tag": "Software-SAS" }, { "tag": "Software-STATA" } ], "collections": [ "3EF26T8S" ], "relations": {}, "dateAdded": "2014-11-11T11:06:30Z", "dateModified": "2014-11-11T11:07:02Z" } } ]