|Title||Quantile Regression Models for Current Status Data.|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Authors||Ou, Fang-Shu, Donglin Zeng, and Jianwen Cai|
|Journal||J Stat Plan Inference|
|Date Published||2016 Nov|
Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regression model to analyze current status data, because it does not require distributional assumptions and the coefficients can be interpreted as direct regression effects on the distribution of failure time in the original time scale. Our model assumes that the conditional quantile of failure time is a linear function of covariates. We assume conditional independence between the failure time and observation time. An M-estimator is developed for parameter estimation which is computed using the concave-convex procedure and its confidence intervals are constructed using a subsampling method. Asymptotic properties for the estimator are derived and proven using modern empirical process theory. The small sample performance of the proposed method is demonstrated via simulation studies. Finally, we apply the proposed method to analyze data from the Mayo Clinic Study of Aging.
|Alternate Journal||J Stat Plan Inference|
|Original Publication||Quantile regression models for current status data.|
|PubMed Central ID||PMC5160027|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
R01 CA082659 / CA / NCI NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States
U01 NS082062 / NS / NINDS NIH HHS / United States
Quantile Regression Models for Current Status Data.