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Adjusting for misselection using subsampling in epidemiological studies
Author(s) -
Blettner Maria,
Becher Heiko
Publication year - 1990
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.4780091111
Subject(s) - statistics , sample size determination , odds ratio , variance (accounting) , epidemiology , selection (genetic algorithm) , sample (material) , selection bias , population , odds , estimation , econometrics , computer science , mathematics , medicine , environmental health , logistic regression , engineering , chemistry , accounting , systems engineering , chromatography , artificial intelligence , business
In epidemiological research it is sometimes necessary to conduct a case‐control study in a part of the population which is not exposed to certain risk factors. Such a study design may be useful to investigate the risk related to a weak carcinogen in presence of other strong risk factors. It may happen that some individuals are wrongly included in the study group due to misreporting of the true exposure status. This misselection may cause biased odds ratio estimates of the factors of interest. It is therefore recommended that an evaluation study to validate the selection procedure is undertaken. We propose a two‐stage sample design, consisting of a case‐control study and a validation study in a subsample. Misselection rates in cases and control for different exposure groups may be estimated in the subsample and used to correct the odds ratio estimation from the first stage. It is shown how the total sample size should be attributed to the case‐control sample and to the second‐stage sample in order to give an unbiased estimate with minimal variance. The allocation is shown to be dependent on a number of parameters which is illustrated numerically and graphically. Efficiency considerations are also addressed.