Likelihood Ratio-Based Confidence Intervals in Survival Analysis
研究了生存函数和累积风险函数的置信区间构建方法，基于似然比统计量的逆推，通过模拟验证其优于基于估计量渐近正态性的传统方法。
Confidence intervals for the survival function and the cumulative hazard function are considered. These confidence intervals are based on an inversion of the likelihood ratio statistic. To do this, two extensions of the likelihood, each of which yields meaningful likelihood ratio hypothesis tests and subsequent confidence intervals, are considered. The choice of the best extension is difficult. In the failure time setting, the binomial extension is best in constructing confidence intervals concerning the survival function and the Poisson extension is best in constructing confidence intervals concerning the cumulative hazard. Simulations indicate that these two methods perform as well as or better than competitors based on the asymptotic normality of the estimator.