Information-Anchored Sensitivity Analysis: Theory and Application

TL;DR

This paper introduces the concept of information-anchored sensitivity analysis for longitudinal RCTs, ensuring the proportion of information lost due to missing data remains consistent with primary analysis, thereby improving interpretability.

Contribution

It develops a theoretical framework for information-anchored sensitivity analysis and demonstrates that many multiple imputation methods are inherently information-anchored for continuous data.

Findings

Controlled multiple imputation methods are often information-anchored.

Simulations and real data illustrate the practical application of the theory.

The approach enhances transparency and robustness in sensitivity analysis.

Abstract

Analysis of longitudinal randomised controlled trials is frequently complicated because patients deviate from the protocol. Where such deviations are relevant for the estimand, we are typically required to make an untestable assumption about post-deviation behaviour in order to perform our primary analysis and estimate the treatment effect. In such settings, it is now widely recognised that we should follow this with sensitivity analyses to explore the robustness of our inferences to alternative assumptions about post-deviation behaviour. Although there has been a lot of work on how to conduct such sensitivity analyses, little attention has been given to the appropriate loss of information due to missing data within sensitivity analysis. We argue more attention needs to be given to this issue, showing it is quite possible for sensitivity analysis to decrease and increase the information about the treatment effect. To address this critical issue, we introduce the concept of information-anchored sensitivity analysis. By this we mean sensitivity analysis in which the proportion of information about the treatment estimate lost due to missing data is the same as the proportion of information about the treatment estimate lost due to missing data in the primary analysis. We argue this forms a transparent, practical starting point for interpretation of sensitivity analysis. We then derive results showing that, for longitudinal continuous data, a broad class of controlled and reference-based sensitivity analyses performed by multiple imputation are information-anchored. We illustrate the theory with simulations and an analysis of a peer review trial, then discuss our work in the context of other recent work in this area. Our results give a theoretical basis for the use of controlled multiple imputation procedures for sensitivity analysis.