Global sensitivity analysis for repeated measures studies with informative drop†out: A semi†parametric approach

Daniel Scharfstein, Aidan McDermott, Iván Díaz, Marco Carone, Nicola Lunardon and Ibrahim Turkoz

Biometrics, 2018, vol. 74, issue 1, 207-219

Abstract: In practice, both testable and untestable assumptions are generally required to draw inference about the mean outcome measured at the final scheduled visit in a repeated measures study with drop†out. Scharfstein et al. (2014) proposed a sensitivity analysis methodology to determine the robustness of conclusions within a class of untestable assumptions. In their approach, the untestable and testable assumptions were guaranteed to be compatible; their testable assumptions were based on a fully parametric model for the distribution of the observable data. While convenient, these parametric assumptions have proven especially restrictive in empirical research. Here, we relax their distributional assumptions and provide a more flexible, semi†parametric approach. We illustrate our proposal in the context of a randomized trial for evaluating a treatment of schizoaffective disorder.

Date: 2018
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