Functional attrition and Quality of Life in Advanced Cancer Trial: modeling patients’ trajectories and evaluating the impact of missing outcome data handling on quality-of-life predictive model performance

Background: Functional attrition (non-response) of Patient-Reported Outcomes (PROs) and Quality of Life (QoL) deterioration are common and non-random in oncology trials. While functional attrition is informative, it is often treated as simple missing data, which biases the interpretation of QoL trajectories and prediction models. Objectives and Methods: This study aimed (1) To examine prognostic factors of transitions between active participation, functional attrition, and death using multinomial models and (2) To evaluate how different methods of dealing with missing data affect the reliability of QoL deterioration prediction models. A discrete-state modeling framework was used to analyze transitions between active participation, functional attrition, and death in advanced cancer patients. QoL decline was then predicted using LASSO, Random Forest, and XGBoost algorithms, while Inverse Probability of Completion Weighting (IPCW) under a conditional Missing At Random (MAR) assumption was systematically compared against Multiple Imputation by Chained Equations (MICE). At the end, we examined robustness to deviations from MAR through delta-adjusted Missing Not At Random (MNAR) sensitivity analyses. Results: Functional attrition accumulated more rapidly than death within the first 12 weeks, confirming its high prevalence and distinct determinants. Lower educational attainment was associated with functional attrition, whereas poor performance status and depressive symptoms primarily predicted mortality. Among predictive models, LASSO combined with MICE achieved the best performance (median AUC = 0.671 [IQR 0.136]), reflecting the modest predictability of dynamic PRO outcomes when relying on baseline covariates. Methodological comparison demonstrated superior discrimination and substantially improved calibration for MICE relative to IPCW, with IPCW models showing marked miscalibration. These findings remained stable across MNAR sensitivity scenarios. Conclusion: By modeling the functional attrition as a distinct state, we revealed its specific socio-demographic determinants and clarified how covariate profiles shape patient trajectories . For QoL prediction under conditional MAR, MICE yields more stable and better-calibrated models than IPCW. Robustness across MNAR scenarios supports the methodological validity of these conclusions and highlights the importance of principled handling of informative non-response in longitudinal PRO research.