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Secondary Analysis under Cohort Sampling Designs Using Conditional Likelihood
Author(s) -
Olli Saarela,
Sangita Kulathinal,
Juha Karvanen
Publication year - 2012
Publication title -
journal of probability and statistics
Language(s) - English
Resource type - Journals
eISSN - 1687-9538
pISSN - 1687-952X
DOI - 10.1155/2012/931416
Subject(s) - covariate , inverse probability weighting , statistics , event (particle physics) , cohort , mathematics , sampling (signal processing) , econometrics , weighting , inverse probability , estimator , computer science , medicine , bayesian probability , posterior probability , physics , filter (signal processing) , quantum mechanics , computer vision , radiology
Under cohort sampling designs, additional covariate data are collected on cases of a specific type and a randomly selected subset of noncases, primarily for the purpose of studying associations with a time-to-event response of interest. With such data available, an interest may arise to reuse them for studying associations between the additional covariate data and a secondary non-time-to-event response variable, usually collected for the whole study cohort at the outset of the study. Following earlier literature, we refer to such a situation as secondary analysis. We outline a general conditional likelihood approach for secondary analysis under cohort sampling designs and discuss the specific situations of case-cohort and nested case-control designs. We also review alternative methods based on full likelihood and inverse probability weighting. We compare the alternative methods for secondary analysis in two simulated settings and apply them in a real-data example

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