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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • The Cox proportional hazards model is standard for time-to-event data.
  • Nested case-control (NCC) designs reduce costs for rare events with difficult covariates.
  • Model misspecification can impact NCC design validity and inference.

Purpose of the Study:

  • To investigate the robustness of NCC designs under model misspecification.
  • To develop a novel estimator for NCC designs when covariate functional forms are misspecified.
  • To ensure reliable inference on associations of interest in NCC studies.

Main Methods:

  • Investigated the impact of covariate functional form misspecification on NCC partial likelihood estimators.
  • Proposed a new estimator to mitigate the dependency on the number of sampled controls.
  • Validated the proposed estimator through simulation studies and theoretical analysis.
  • Applied the estimator to Alzheimer's Disease Neuroimaging Initiative data.

Main Results:

  • NCC estimates are sensitive to covariate misspecification, depending on the number of controls.
  • The proposed estimator recovers full cohort results under misspecification.
  • Simulation studies confirm the estimator's utility and theoretical properties.
  • The estimator was successfully applied to real-world Alzheimer's disease data.

Conclusions:

  • NCC designs require careful model specification for valid inference.
  • The proposed estimator enhances the robustness of NCC designs against model misspecification.
  • This method provides reliable association estimates, comparable to full cohort analyses, even with potential model misspecification.