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Model misspecification in semi-parametric survival models
(2025-10-24) Papastasinopoulou, Katerina; Παπαστασινοπούλου, Αικατερίνη; Pateras, Konstantinos; Pedeli, Xanthi; Besbeas, Panagiotis
Survival analyses in medical research often relies on the Cox proportional hazards (PH) model and related parametric approaches, yet routine violations of modeling assumptions can distort inference. This thesis investigates the consequences of model misspecification for both semi-parametric and parametric survival regression, using simulation. Time-to-event data are generated under prespecified data-generating mechanisms (DGMs), namely, Exponential, Weibull, and Logistic, as well as mixtures that induce both proportional and nonproportional baseline hazards. Then, commonly used models were fitted, and a spectrum of misspecifications was also examined, including omitted covariates and violations of the PH assumption. Two motivating case studies in radiation oncology use patient-level data to illustrate how misspecification appears in practice and how it biases variable effect estimates. The findings highlight that while the Cox model shows relative robustness in some scenarios, substantial bias and assumption violations occur in heterogeneous or mixed populations. Collectively, the results provide practical guidance for model selection and sensitivity analysis in time-to-event studies, emphasizing simulation as a principled means to probe and mitigate misspecification.