Handling missing values in patient-reported outcome data in the presence of intercurrent events.

ABSTRACT:

Introduction

As patient-reported outcomes (PROs) are increasingly used in the evaluation of medical treatments, it is important that PROs are carefully analyzed and interpreted. This may be challenging due to substantial missing values. The missingness in PROs is often closely related to patients' disease status. In that case, using observed information about intercurrent events (ICEs) such as disease progression and death will improve the handling of missing PRO data. Therefore, the aim of this study was to develop imputation models for repeated PRO measurements that leverage information about ICEs.

Methods

We assumed a setting in which missing PRO measurements are missing at random given observed measurements, as well as the occurrence and timing of ICEs, and potentially o

SUBMITTER: Thomassen D 

PROVIDER: S-EPMC11872335 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature