How to Analyze Longitudinal Patient‐Reported Outcomes in Populations With High Mortality Rates

Background Analyzing longitudinal patient‐reported outcomes (PROs) remains methodologically challenging, particularly in populations with high mortality rates during follow‐up. Many PRO studies overlook this issue, potentially leading to biased estimates and limited clinical interpretability. We discuss various methods for analyzing patients who died during follow‐up in PRO analyses and examine how these methods affect the interpretation of the study outcomes, using a clinical example in older patients with cancer. Methods For our clinical example, we included adults aged 70 years with cancer from a prospective cohort. Quality of life (QoL) was assessed with the EuroQoL 5‐Dimension (EQ‐5D) tool at baseline and 6 and 12 months after treatment initiation. We applied six different methods: Four methods calculated absolute changes in mean scores; two methods estimated the proportion of patients with a changed QoL. Results Of the 1218 participants, 321 (26%) died within 12 months. Methods restricted to the 897 survivors at the end of follow‐up lead to results that are difficult to interpret due to selection bias. In linear mixed models, EQ‐5D scores are implicitly reconstructed for the 321 participants that died, creating a hypothetical view of QoL assuming no patients died. Alternative approaches, such as the “while‐alive” strategy or composite endpoint strategies, treating death as the worst possible score on the EQ‐5D, produce more complete views of the QoL. Conclusions The interpretation of longitudinal PROs strongly depends on the chosen method, associated estimands and how patients that died are addressed: different methods lead to different estimates and conclusions. Properly defining the research question and choosing the most appropriate method is essential for meaningful interpretations for clinicians and patients.