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Title: Two-wave outcome-dependent sampling designs with applications to longitudinal binary data Authors:  Ran Tao - Vanderbilt University Medical Center (United States) [presenting]
Abstract: Outcome-dependent sampling (ODS) designs are useful when resource constraints prohibit expensive exposure ascertainment on all study subjects. One class of ODS designs for longitudinal binary data stratifies subjects into three strata according to those who experience the event at none, some, or all follow-up times. For time-varying covariate effects, exclusively selecting subjects with response variation can yield highly efficient estimates. However, if interest lies in the association of a time-invariant covariate or the joint associations of time-varying and time-invariant covariates with the outcome, then the optimal design is unknown. Therefore, we propose a class of two-wave two-phase ODS designs for longitudinal binary data. We split the second-phase sample selection into two waves, between which an interim design evaluation analysis is conducted. The interim design evaluation analysis uses first-wave data to conduct a simulation-based search for the optimal second-wave design that will improve the likelihood of study success. We believe the proposed designs can be useful in settings where 1) the second-phase sample size is fixed, and one must tailor relative sampling proportions among the strata to maximize estimation efficiency, or 2) the relative sampling proportions are fixed, and one must tailor the sample size to achieve the desired precision.