Global sensitivity analysis for repeated measures studies with informative drop‐out: A semi‐parametric approach. Issue 1 (23rd May 2017) - Scharfstein, Daniel; McDermott, Aidan; Díaz, Iván; Carone, Marco; Lunardon, Nicola; Turkoz, Ibrahim
Summary: 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.