151. Global sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric approach.
- Author
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Scharfstein D, McDermott A, Díaz I, Carone M, Lunardon N, and Turkoz I
- Subjects
- Humans, Psychotic Disorders therapy, Randomized Controlled Trials as Topic, Reproducibility of Results, Models, Statistical, Research Design statistics & numerical data, Statistical Distributions, Treatment Outcome
- 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., (© 2017, The International Biometric Society.)
- Published
- 2018
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