Nested case-control study design, which is cost-effective, has been widely used in large prospective epidemiological studies. In these studies, biomarkers exhibiting time-varying associations with disease outcome occur frequently. To analyze data from nested case-control study design with temporal biomarker effects, we proposed a martingale-based estimating procedure building on a generalized Cox model, an estimating equation approach to a censored quantile regression model, and a kernel weighted pseudo-likelihood approach on the classic Cox model. The inverse probability weighting technique is implemented to overcome the complex correlation structure among nested case-control sub-cohort. A perturbation based resampling procedure is proposed for variance estimation, and furthermore, a computationally efficient resampling procedure is developed. We study the asymptotic properties of the resulting estimators, including the uniform consistency and weak convergence. Extensions with additional matching and second-stage inferences are developed accordingly. Simulation studies demonstrated nice finite sample performance of the proposed methods and procedures. Real data analysis on Nurses’ Health Study and Wilms Tumor Study are presented for illustration.