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Jinbo Chen, University of Pennsylvania

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A Novel Goodness-of-Fit Based Two-Phase Sampling Design for Studying Binary Outcomes
When
28 March 2019 from 3:30 PM to 4:30 PM
Where
201 Thomas Building
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In a biomedical cohort study for assessing the association between an outcome variable and a set of covariates, it is common that a subset of covariates can only be measured on a subgroup of study subjects. An important design question is which subjects to select into the subgroup towards increased statistical efficiency for association analyses. When the outcome is binary, one may adopt a case-control sampling design or a balanced case-control design where cases and controls are further matched on a small number of discrete covariates whose values are available for all subjects. While the latter achieves success in estimating odds ratio (OR) parameters for the matching covariates, to our best knowledge, to date, similar two-phase design options have not been explored for increasing statistical efficiency for assessing the remaining covariates, particularly the incompletely collected ones. To this end, assuming that an external model is available relating the outcome and complete covariates, we proposed a novel sampling scheme that over-samples cases and controls who have poorer goodness-of-fit based on the  external model and at the same time matches cases and controls on complete covariates similar as the balanced design. We developed an accompanying pseudo-likelihood method for OR parameter estimation, which can be performed using existing software package. Through extensive simulation studies and explorations in a real cohort study setting, we found that our design generally leads to a reduction in asymptotic variances of the estimated OR parameters to a similar extent for both the incomplete and complete covariates, and the reduction for the matching covariates was comparable to that of the balanced design.

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