Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records |
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| Authors: | James H McVittie Ana F Best David B Wolfson David A Stephens Julian Wolfson David L Buckeridge Shahinaz M Gadalla |
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| Institution: | 1. Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada;2. Biostatistics Branch, Biometrics Research Program Division of Cancer Treatment and Diagnosis National Cancer Institute, National Institutes of Health, Bethesda, MD, USA;3. School of Public Health, Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA;4. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada;5. Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics National Cancer Institute, National Institutes of Health, Bethesda, MD, USA |
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| Abstract: | Non-parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, including the ways in which the data may be censored and/or truncated. For data arising from a single source or collected from a single cohort, a wide range of estimators have been proposed and compared in the literature. Often, however, it may be possible, and indeed advantageous, to combine and then analyse survival data that have been collected under different study designs. We review non-parametric survival analysis for data obtained by combining the most common types of cohort. We have two main goals: (i) to clarify the differences in the model assumptions and (ii) to provide a single lens through which some of the proposed estimators may be viewed. Our discussion is relevant to the meta-analysis of survival data obtained from different types of study, and to the modern era of electronic health records. |
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| Keywords: | Censoring EM algorithm incident cohort length bias prevalent cohort |
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