Model misspecification and robust analysis for outcome‐dependent sampling designs under generalized linear models
Sign Up to like & getrecommendations! Published in 2023 at "Statistics in Medicine"
DOI: 10.1002/sim.9673
Abstract: Outcome‐dependent sampling (ODS) is a commonly used class of sampling designs to increase estimation efficiency in settings where response information (and possibly adjuster covariates) is available, but the exposure is expensive and/or cumbersome to collect.… read more here.
Keywords: likelihood; dependent sampling; analysis; phase ... See more keywords
The effects of missing data due to study dropout on longitudinal analysis inference using outcome-dependent sampling.
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DOI: 10.1093/ije/dyaf150
Abstract: BACKGROUND Existing longitudinal cohort study data and associated biospecimen libraries provide abundant opportunities to efficiently examine new hypotheses through retrospective specimen testing. Outcome-dependent sampling (ODS) methods offer a powerful alternative to random sampling when testing… read more here.
Keywords: mar; random; mnar; dependent sampling ... See more keywords
Improving estimation efficiency for two-phase, outcome-dependent sampling studies
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DOI: 10.1214/23-ejs2124
Abstract: Two-phase outcome dependent sampling (ODS) is widely used in many fields, especially when certain covariates are expensive and/or difficult to measure. For two-phase ODS, the conditional maximum likelihood (CML) method is very attractive because it… read more here.
Keywords: phase outcome; dependent sampling; outcome dependent; phase ... See more keywords