Survival Modelling for Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis Using Electronic Health Records

Summary 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 rang...

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Bibliographic Details
Published in:International statistical review 2023-04, Vol.91 (1), p.72-87
Main Authors: McVittie, James H., Best, Ana F., Wolfson, David B., Stephens, David A., Wolfson, Julian, Buckeridge, David L., Gadalla, Shahinaz M.
Format: Article
Language:English
Subjects:
Censoring
Data analysis
Electronic health records
EM algorithm
Estimators
Failure times
incident cohort
length bias
Parameter estimation
prevalent cohort
Survival
Survival analysis
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Summary:Summary 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.
ISSN:0306-7734
1751-5823
DOI:10.1111/insr.12510