Semiparametric model averaging prediction in nested case-control studies

Survival predictions for patients are becoming increasingly important in clinical practice as they play a crucial role in aiding healthcare professionals to make more informed diagnoses and treatment decisions. The nested case-control designs have been extensively utilized as a cost-effective solution in many large cohort studies across epidemiology and other research fields. To achieve accurate survival predictions of individuals from nested case-control studies, we propose a semiparametric model averaging approach based on the partly linear additive proportional hazards structure to avoid the curse of dimensionality. The inverse probability weighting method is considered to estimate the parameters of submodels used in model averaging. We choose the weights by maximizing the pseudo-likelihood function constructed for the aggregated model and discuss the asymptotic optimality of selected weights. Simulation studies are conducted to assess the performance of our proposed model averaging method in the nested case-control study. Furthermore, we apply the proposed approach to real data to demonstrate its superiority.