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Survival Modelling For Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis using Electronic Health Records.
McVittie, James H; Best, Ana F; Wolfson, David B; Stephens, David A; Wolfson, Julian; Buckeridge, David L; Gadalla, Shahinaz M.
Afiliação
  • McVittie JH; Department of Mathematics and Statistics, McGill University.
  • Best AF; Biostatistics Branch, Biometrics Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health.
  • Wolfson DB; Department of Mathematics and Statistics, McGill University.
  • Stephens DA; Department of Mathematics and Statistics, McGill University.
  • Wolfson J; School of Public Health, Division of Biostatistics, University of Minnesota.
  • Buckeridge DL; Department of Epidemiology, Biostatistics and Occupational Health, McGill University.
  • Gadalla SM; Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health.
Int Stat Rev ; 91(1): 72-87, 2023 Apr.
Article em En | MEDLINE | ID: mdl-37193196
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 analyze 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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Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Int Stat Rev Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Int Stat Rev Ano de publicação: 2023 Tipo de documento: Article