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arXiv · 2211.05937

Optimal Designs of Two-Phase Case-Control Studies for General Predictor Effects

Jingjing Zou·Lori B. Daniels·Karen Messer·Daniel Rabinowitz
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Abstract

Under two-phase designs, the outcome and several covariates and confounders are measured in the first phase, and a new predictor of interest, which may be costly to collect, can be measured on a subsample in the second phase, without incurring the costs of recruiting subjects. By using the information gathered in the first phase, the second-phase subsample can be selected to enhance the efficiency of testing and estimating the effect of the new predictor on the outcome. Past studies have focused on optimal two-phase sampling schemes for statistical inference on local ($\beta = o(1)$) effects of the predictor of interest. In this study, we propose an extension of the two-phase designs that employs an optimal sampling scheme for estimating predictor effects with pseudo conditional likelihood estimators in case-control studies. This approach is applicable to both local and non-local effects. We demonstrate the effectiveness of the proposed sampling scheme through simulation studies and analysis of data from 170 patients hospitalized for treatment of COVID-19. The results show a significant improvement in the estimation of the parameter of interest.

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Jingjing Zou, Lori B. Daniels, Karen Messer, Daniel Rabinowitz. 2022-11-11. Optimal Designs of Two-Phase Case-Control Studies for General Predictor Effects. https://arxiv.org/abs/2211.05937

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