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.
Original language | English (US) |
---|---|
Pages (from-to) | 207-219 |
Number of pages | 13 |
Journal | Biometrics |
Volume | 74 |
Issue number | 1 |
DOIs | |
State | Published - Mar 2018 |
Keywords
- Bootstrap
- Cross-validation
- Exponential tilting
- Identifiability
- Jackknife
- One-step estimator
- Plug-in estimator
- Selection bias
ASJC Scopus subject areas
- Statistics and Probability
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Agricultural and Biological Sciences(all)
- Applied Mathematics