Missingness mechanisms and generalizability of patient reported outcome measures in colorectal cancer survivors – assessing the reasonableness of the “missing completely at random” assumption

Abstract

Abstract Background Patient-Reported Outcome Measures (PROM) provide important information, however, missing PROM data threaten the interpretability and generalizability of findings by introducing potential bias. This study aims to provide insight into missingness mechanisms and inform future researchers on generalizability and possible methodological solutions to overcome missing PROM data problems during data collection and statistical analyses. Methods We identified 10,236 colorectal cancer survivors (CRCs) above 18y, diagnosed between 2014 and 2018 through the Danish Clinical Registries. We invited a random 20% (2,097) to participate in a national survey in May 2023. We distributed reminder e-mails at day 10 and day 20, and compared Initial Responders (response day 0–9), Subsequent Responders (response day 10–28) and Non-responders (no response after 28 days) in demographic and cancer-related characteristics and PROM-scores using linear regression. Results Of the 2,097 CRCs, 1,188 responded (57%). Of these, 142 (7%) were excluded leaving 1,955 eligible CRCs. 628 (32%) were categorized as initial responders, 418 (21%) as subsequent responders, and 909 (47%) as non-responders. Differences in demographic and cancer-related characteristics between the three groups were minor and PROM-scores only marginally differed between initial and subsequent responders. Conclusion In this study of long-term colorectal cancer survivors, we showed that initial responders, subsequent responders, and non-responders exhibit comparable demographic and cancer-related characteristics. Among respondents, Patient-Reported Outcome Measures were also similar, indicating generalizability. Assuming Patient-Reported Outcome Measures of subsequent responders represent answers by the non-responders (would they be available), it may be reasonable to judge the missingness mechanism as Missing Completely At Random.