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

TL;DR

This paper introduces a semi-parametric sensitivity analysis method for trials with irregular, informative assessment times, enabling robust treatment effect estimation under untestable assumptions.

Contribution

It develops a new influence function-based estimator that separates data modeling from sensitivity parameters, improving analysis of irregular assessment times.

Findings

Method applied to asthma trial data.

Flexible semiparametric modeling achieved.

Sensitivity analysis quantifies assumption deviations.

Abstract

Many trials are designed to collect outcomes at or around pre-specified times after randomization. If there is variability in the times when participants are actually assessed, this can 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. Methods have been developed that account for some types of such irregular and informative assessment times; however, since these methods rely on untestable assumptions, sensitivity analyses are needed. We develop a methodology that is benchmarked at the explainable assessmen (EA) assumption, under which assessment and outcomes at each time are related only through data collected prior to that time. Our method uses an exponential tilting assumption, governed by a sensitivity analysis parameter, that posits deviations from the EA assumption. Our inferential strategy is based on a new influence function-based, augmented inverse intensity-weighted estimator. Our approach allows for flexible semiparametric modeling of the observed data, which is separated from specification of the sensitivity parameter. We apply our method to a randomized trial of low-income individuals with uncontrolled asthma, and we illustrate implementation of our estimation procedure in detail.