Abstract:
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Retrospective, outcome dependent sampling (ODS) designs are efficient compared to standard designs because sampling is targeted towards those who are particularly informative for the estimation target. In the longitudinal data setting, one may exploit outcome vector, and possible covariate data, from an existing resource such as an electronic medical record to identify those whose expensive to ascertain and sample size limiting biomarker / exposure should be collected. Who is most informative is reasonably predictable and will depend upon the target of inference. In this talk we will discuss new innovations in, and extensions of, ODS designs for longitudinal data. We will describe the class of designs, examine finite sampling operating characteristics, and apply the designs to an exemplar study on the impact of a single nucleotide polymorphism on cholesterol levels in patients taking statins at our institution.
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