Semi-Parametric Sensitivity Analysis for Trials with Irregular and Informative Assessment Times

Autor: Smith, Bonnie B., Gao, Yujing, Yang, Shu, Varadhan, Ravi, Apter, Andrea J., Scharfstein, Daniel O.
Rok vydání: 2022
Předmět:
Druh dokumentu: Working Paper
Popis: Many trials are designed to collect outcomes at or around pre-specified times after randomization. In practice, there can be substantial variability in the times when participants are actually assessed. Such irregular assessment times pose a challenge to learning the effect of treatment since not all participants have outcome assessments at the times of interest. Furthermore, observed outcome values may not be representative of all participants' outcomes at a given time. This problem, known as informative assessment times, can arise if participants tend to have assessments when their outcomes are better (or worse) than at other times, or if participants with better outcomes tend to have more (or fewer) assessments. Methods have been developed that account for some types of informative assessment; however, since these methods rely on untestable assumptions, sensitivity analyses are needed. We develop a sensitivity analysis methodology by extending existing weighting methods. Our method accounts for the possibility that participants with worse outcomes at a given time are more (or less) likely than other participants to have an assessment at that time, even after controlling for variables observed earlier in the study. We apply our method to a randomized trial of low-income individuals with uncontrolled asthma. We illustrate implementation of our influence-function based estimation procedure in detail, and we derive the large-sample distribution of our estimator and evaluate its finite-sample performance.
Comment: Revised to include more implementation details, including a tutorial implementing our estimator, and using a more flexible outcome modeling approach in the data analysis
Databáze: arXiv
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