Multiple imputation strategies for missing event times in a multi-state model analysis

Elinor Curnow*, Rachael Hughes, Kate Birnie, Kate M Tilling, Michael J Crowther

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)

Abstract

In clinical studies, multi-state model (MSM) analysis is often used to describe the sequence of events that patients experience, enabling better understanding of disease progression. A complicating factor in many MSM studies is that the exact event times may not be known. Motivated by a real dataset of patients who received stem cell transplants, we considered the setting in which some event times were exactly observed and some were missing. In our setting, there was little information about the time intervals in which the missing event times occurred and missingness depended on the event type, given the analysis model covariates. These additional challenges limited the usefulness of some missing data methods (maximum likelihood, complete case analysis, and inverse probability weighting). We show that multiple imputation (MI) of event times can perform well in this setting. MI is a flexible method that can be used with any complete data analysis model. Through an extensive simulation study, we show that MI by predictive mean matching (PMM), in which sampling is from a set of observed times without reliance on a specific parametric distribution, has little bias when event times are missing at random, conditional on the observed data. Applying PMM separately for each sub-group of patients with a different pathway through the MSM tends to further reduce bias and improve precision. We recommend MI using PMM methods when performing MSM analysis with Markov models and partially observed event times.
Original languageEnglish
Pages (from-to)1238-1255
Number of pages18
JournalStatistics in Medicine
Volume43
Issue number6
Early online date22 Jan 2024
DOIs
Publication statusPublished - 22 Feb 2024

Bibliographical note

Funding Information:
This research received no specific grant from any funding agency in the public, commercial, or not‐for‐profit sectors. Elinor Curnow is supported by funding from NHS Blood and Transplant. Elinor Curnow and Kate Tilling work in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol which is supported by the Medical Research Council (grant no MC_UU_00032/02) and the University of Bristol. Rachael Hughes is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 215408/Z/19/Z).

Publisher Copyright:
© 2024 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

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