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Introduction <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月1日

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  • Doranne Thomassen,
  • Satrajit Roychoudhury,
  • Cecilie Delphin Amdal,
  • Dries Reynders,
  • Jammbe Z. Musoro,
  • Willi Sauerbrei,
  • Els Goetghebeur,
  • Saskia le Cessie,
  • Rajesh Kamalakar,
  • Kavita Sail,
  • Ethan Basch,
  • Jaap Reijneveld,
  • Christoph Gerlinger,
  • Ahu Alanya,
  • Gerhard Rumpold,
  • Maxime Sasseville,
  • Jennifer Black,
  • Geert Molenberghs,
  • Khadija Rantell,
  • Michael Schlichting,
  • Antoine Regnault,
  • David Ness,
  • Silene ten Seldam,
  • Tove Ragna Reksten,
  • Anja Schiel,
  • Ragnhild Sorum Falk,
  • Alicyn Campbell,
  • Joseph C. Cappelleri,
  • Alexander Russell-Smith,
  • Melanie Calvert,
  • Samantha Cruz Rivera,
  • Olalekan Lee Aiyegbusi,
  • Limin Liu,
  • Kelly Van Lancker,
  • Claudia Rutherford,
  • Vishal Bhatnagar,
  • Ting-Yu Chen,
  • Mallorie Fiero,
  • Paul Kluetz

摘要

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 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.

Results

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.

Conclusion

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.