2005
DOI: 10.1002/sim.2398
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Re‐use of case–control data for analysis of new outcome variables

Abstract: Case-control studies are usually defined to investigate risk factors for a single disease of interest. However, subsequent to data collection, investigators may wish to examine as an 'outcome' a variable that was an exposure in the original study. A naive analysis that disregards the sampling strategy that gave rise to the data is clearly prone to bias. We present here a simple approach to the analysis of such data using an appropriately weighted regression model. Viewing the problem as a two-stage design prov… Show more

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Cited by 25 publications
(37 citation statements)
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“…A limitation of this study was the use of data from a case-control study not originally designed to address questions of frequency of tooth decay. We chose to account for the sampling design of the original study by performing a cross-sectional analysis of the controls only [22]. A naive analysis disregarding the sampling strategy that gave rise to the data would be prone to bias, through an over-representation of cases.…”
Section: Discussionmentioning
confidence: 99%
“…A limitation of this study was the use of data from a case-control study not originally designed to address questions of frequency of tooth decay. We chose to account for the sampling design of the original study by performing a cross-sectional analysis of the controls only [22]. A naive analysis disregarding the sampling strategy that gave rise to the data would be prone to bias, through an over-representation of cases.…”
Section: Discussionmentioning
confidence: 99%
“…Because the participants of the 2 nested case-control studies were not a random sample of the WHI-OS population, ordinary regression models may introduce bias (22). To obtain estimates representative of the WHI-OS population, inverse probability weights were applied, including matching factors (age, race/ethnicity, date of blood draw), region (Northeast, South, Midwest, and West), and case status (fracture history) in the model for the weights (23,24). Unweighted and weighted distributions of participant characteristics enrolled in the case-control studies (n = 2419) were compared with the entire WHI-OS (n = 93,676) to evaluate the acceptability of the model for the weights.…”
Section: Methodsmentioning
confidence: 99%
“…As an alternative verification for correct prevalence adjustment, we performed binomial regression with sample weighting. 24 Receiver operating characteristics were tabulated to determine the area under the curve for both Monte Carlo simulations and sample weighting results.…”
Section: Methodsmentioning
confidence: 99%