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Tracing studies in cohorts with attrition: Selection models for efficient sampling
Author(s) -
Moon Nathalie C.,
Zeng Leilei,
Cook Richard J.
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7646
Subject(s) - cohort , estimator , statistics , sampling (signal processing) , attrition , tracing , selection (genetic algorithm) , simple random sample , computer science , medicine , cohort study , econometrics , mathematics , machine learning , environmental health , population , dentistry , filter (signal processing) , computer vision , operating system
Cohort studies of chronic diseases involve recruitment and longitudinal follow‐up of affected individuals with a view to studying the effect of risk factors on disease progression and death. When the time to withdrawal from the cohort is conditionally independent of the disease process the primary consequence is a loss of information on the parameters of interest. This loss can sometimes be mitigated through the conduct of tracing studies in which a subsample of those lost to follow up are contacted and some information is obtained on their disease and survival status. We describe the use of selection models to sample individuals for tracing who will yield more efficient estimators than those obtained by simple random sampling. Efficient sampling schemes featuring cost constraints are also developed and shown to perform well. An application to data from the University of Toronto Psoriatic Arthritis Cohort illustrates how to apply the method in a real setting.