Handling Partially Observed Trial Data After Treatment Withdrawal: Introducing Retrieved Dropout Reference‐Base Centred Multiple Imputation

Author:

Cro Suzie1ORCID,Roger James H.2,Carpenter James R.23

Affiliation:

1. Imperial Clinical Trials Unit Imperial College London London UK

2. Medical Statistics Department London School of Hygiene & Tropical Medicine London UK

3. MRC Clinical Trials Unit @ UCL UCL London UK

Abstract

ABSTRACTThe 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 randomised treatment. However, when patients withdraw from a study early before 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 introduces a novel approach to parameterising 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 retrieved dropout 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. We refer to this new approach as retrieved dropout reference‐base centred multiple imputation.

Funder

National Institute for Health and Care Research

Medical Research Council

Publisher

Wiley

Reference28 articles.

1. “International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use ” Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials 2019.https://database.ich.org/sites/default/files/E9‐R1_Step4_Guideline_2019_1203.pdf.

2. Statistical methods for handling missing data to align with treatment policy strategy

3. Analysis of Longitudinal Trials with Protocol Deviation: A Framework for Relevant, Accessible Assumptions, and Inference via Multiple Imputation

4. Standard and reference‐based conditional mean imputation

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