Nonparametric Estimations of Quantile Difference for Survival Data Under General Biased Samples

Quantile difference is a critical measure for summarizing the random fluctuations of an outcome and has gained much attention. However, existing methods for quantile difference encounter challenges with general biased samples, which are common in fields such as economics, epidemiology, and medical studies. We propose two estimation approaches for quantile difference in survival data under general biased samples: a non-smoothed estimator and a more efficient smoothed estimator. The asymptotic properties of the proposed estimators are derived. Simulation studies are conducted, and two real data examples are analyzed.

Duke Scholars

Author Xiaofei Wang Biostatistics & Bioinformatics, Division of Biostatistics