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

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.

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 other (baseline or time-varying) covariates. We then showed how these missingness assumptions can be translated into concrete imputation models that also account for a longitudinal data structure. The resulting models were applied to impute anonymized PRO data from a single-arm clinical trial in patients with advanced lung cancer.

In our trial example, accounting for death and other ICEs in the imputation of missing data led to lower estimated mean health-related quality of life (while alive) compared to an available case analysis and a naive linear mixed model imputation.

Information about the timing and occurrence of ICEs contribute to a more plausible handling of missing PRO data. To account for ICE information when handling missing PROs, the missing data model should be separated from the analysis model.