Multiple imputation of partially observed data after treatment-withdrawal

TL;DR

This paper proposes a novel Bayesian approach to multiple imputation for handling missing data after treatment withdrawal, improving estimation stability by integrating a core reference-based model with a compliance model.

Contribution

It introduces a new parameterization method using mildly informative priors in a combined model for better imputation of missing data post-treatment withdrawal.

Findings

Enhanced imputation accuracy with Bayesian priors.

Reduced standard errors in parameter estimates.

Improved handling of non-estimable parameters.

Abstract

The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires the monitoring of patients and collection of primary outcome data following termination of randomized treatment. However, when patients withdraw from a study before nominal completion this creates true missing data complicating the analysis. One possible way forward uses multiple imputation to replace the missing data based on a model for outcome on and off treatment prior to study withdrawal, often referred to as retrieved dropout multiple imputation. This article explores a novel approach to parameterizing this imputation model so that those parameters which may be difficult to estimate have mildly informative Bayesian priors applied during the imputation stage. A core reference-based model is combined with a compliance model, using both on- and off- treatment data to form an extended model for the purposes of imputation. This alleviates the problem of specifying a complex set of analysis rules to accommodate situations where parameters which influence the estimated value are not estimable or are poorly estimated, leading to unrealistically large standard errors in the resulting analysis.