Multiple imputation strategies for a bounded outcome variable in a competing risks analysis.

Elinor Curnow,Rachael A Hughes,Michael J Crowther,Kate Tilling,Margaret T May,Kate Birnie

doi:10.1002/sim.8879

Abstract

In patient follow‐up studies, events of interest may take place between periodic clinical assessments and so the exact time of onset is not observed. Such events are known as “bounded” or “interval‐censored.” Methods for handling such events can be categorized as either (i) applying multiple imputation (MI) strategies or (ii) taking a full likelihood‐based (LB) approach. We focused on MI strategies, rather than LB methods, because of their flexibility. We evaluated MI strategies for bounded event times in a competing risks analysis, examining the extent to which interval boundaries, features of the data distribution and substantive analysis model are accounted for in the imputation model. Candidate imputation models were predictive mean matching (PMM); log‐normal regression with postimputation back‐transformation; normal regression with and without restrictions on the imputed values and Delord and Genin's method based on sampling from the cumulative incidence function. We used a simulation study to compare MI methods and one LB method when data were missing at random and missing not at random, also varying the proportion of missing data, and then applied the methods to a hematopoietic stem cell transplantation dataset. We found that cumulative incidence and median event time estimation were sensitive to model misspecification. In a competing risks analysis, we found that it is more important to account for features of the data distribution than to restrict imputed values based on interval boundaries or to ensure compatibility with the substantive analysis by sampling from the cumulative incidence function. We recommend MI by type 1 PMM.

Highlights

A purpose of patient follow-up studies is to study the relationship between interventions or exposures and an event of interest
SE βand median time to acute graft-vs-host disease (aGvHD) for different imputation models, missing data mechanism (MDM) and percentage of missing data
When data were missing at random (MAR), bias was small for all methods with the exception of complete case analysis (CCA) and methods based on unrestricted normal imputation (NORM and NORMNOAUX) (Figure 1)

Summary

Introduction

A purpose of patient follow-up studies is to study the relationship between interventions or exposures and an event of interest. A frequent obstacle is that events are reported without an exact time of occurrence. The time of emergence of a particular tooth occurs at some point between the last dental clinic at which no tooth is seen and the first clinic by which the tooth has emerged. Such events are known as bounded or interval-censored: for subject i, event times are known to lie in the interval

Methods

Results

Conclusion