Optimal estimation for regression models on τ-year survival probability.

TitleOptimal estimation for regression models on τ-year survival probability.
Publication TypeJournal Article
Year of Publication2015
AuthorsKwak, Minjung, Jinseog Kim, and Sin-Ho Jung
JournalJ Biopharm Stat
Date Published2015
KeywordsBiometry, Computer Simulation, Humans, Kaplan-Meier Estimate, Logistic Models, Phenobarbital, Randomized Controlled Trials as Topic, Regression Analysis, Seizures, Statistical Distributions, Survival Analysis

A logistic regression method can be applied to regressing the [Formula: see text]-year survival probability to covariates, if there are no censored observations before time [Formula: see text]. But if some observations are incomplete due to censoring before time [Formula: see text], then the logistic regression cannot be applied. Jung (1996) proposed to modify the score function for logistic regression to accommodate the right-censored observations. His modified score function, motivated for a consistent estimation of regression parameters, becomes a regular logistic score function if no observations are censored before time [Formula: see text]. In this article, we propose a modification of Jung's estimating function for an optimal estimation for the regression parameters in addition to consistency. We prove that the optimal estimator is more efficient than Jung's estimator. This theoretical comparison is illustrated with a real example data analysis and simulations.

Alternate JournalJ Biopharm Stat
Original PublicationOptimal estimation for regression models on τ-year survival probability.
PubMed ID24897607
PubMed Central IDPMC4570829
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
CA142538-01 / CA / NCI NIH HHS / United States