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Therefore, the aim of our study was to analyze differences between different dietary habit groups in terms of health-related variables. The sample used for this cross-sectional study was taken from the Austrian Health Interview Survey AT-HIS 2006/07. In a first step, subjects were matched according to their age, sex, and socioeconomic status (SES). After matching, the total number of subjects included in the analysis was 1320 (N = 330 for each form of diet – vegetarian, carnivorous diet rich in fruits and vegetables, carnivorous diet less rich in meat, and carnivorous diet rich in meat). Analyses of variance were conducted controlling for lifestyle factors in the following domains: health (self-assessed health, impairment, number of chronic conditions, vascular risk), health care (medical treatment, vaccinations, preventive check-ups), and quality of life. In addition, differences concerning the presence of 18 chronic conditions were analyzed by means of Chi-square tests. Overall, 76.4% of all subjects were female. 40.0% of the individuals were younger than 30 years, 35.4% between 30 and 49 years, and 24.0% older than 50 years. 30.3% of the subjects had a low SES, 48.8% a middle one, and 20.9% had a high SES. Our results revealed that a vegetarian diet is related to a lower BMI and less frequent alcohol consumption. Moreover, our results showed that a vegetarian diet is associated with poorer health (higher incidences of cancer, allergies, and mental health disorders), a higher need for health care, and poorer quality of life. 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prediction in non- and semi-parametric multi-state and competing risks models", "creators": [ { "creatorType": "author", "firstName": "Liesbeth C.", "lastName": "de Wreede" }, { "creatorType": "author", "firstName": "Marta", "lastName": "Fiocco" }, { "creatorType": "author", "firstName": "Hein", "lastName": "Putter" } ], "abstractNote": "In recent years, multi-state models have been studied widely in survival analysis. Despite their clear advantages, their use in biomedical and other applications has been rather limited so far. An important reason for this is the lack of flexible and user-friendly software for multi-state models.\nThis paper introduces a package in R, called [`]mstate', for each of the steps of the analysis of multi-state models. It can be applied to non- and semi-parametric models. The package contains functions to facilitate data preparation and flexible estimation of different types of covariate effects in the context of Cox regression models, functions to estimate patient-specific transition intensities, dynamic prediction probabilities and their associated standard errors (both Greenwood and Aalen-type). Competing risks models can also be analyzed by means of mstate, as they are a special type of multi-state models. The package is available from the R homepage http://cran.r-project.org.\nWe give a self-contained account of the underlying mathematical theory, including a new asymptotic result for the cumulative hazard function and new recursive formulas for the calculation of the estimated standard errors of the estimated transition probabilities, and we illustrate the use of the key functions of the mstate package by the analysis of a reversible multi-state model describing survival of liver cirrhosis patients.", "publicationTitle": "Computer Methods and Programs in Biomedicine", "publisher": "", "place": "", "date": "", "volume": "In Press, Corrected Proof", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.cmpb.2010.01.001", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6T5J-4YM4F5G-1/2/c59e20e1a4315997a4bf2d41dec93c83", "accessDate": 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