Global sensitivity analysis for repeated measures studies with informative drop‐out: A semi‐parametric approach. Issue 1 (23rd May 2017)

• Record Type: Journal Article
• Authors: Scharfstein, Daniel; McDermott, Aidan; Díaz, Iván; Carone, Marco; Lunardon, Nicola; Turkoz, Ibrahim
• Is Part Of: Biometrics. Volume 74:Issue 1(2018)
• Page Start: 207
• Page End: 219
• Publication Date: 2017-05-23
• Subjects: Bootstrap -- Cross‐validation -- Exponential tilting -- Identifiability -- Jackknife -- One‐step estimator -- Plug‐in estimator -- Selection bias; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12729
• Languages: English
• ISSNs: 0006-341X

Abstract

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.