Hudgens Michael G, Maathuis Marloes H, Gilbert Peter B
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
Biometrics. 2007 Jun;63(2):372-80. doi: 10.1111/j.1541-0420.2006.00709.x.
This article considers three nonparametric estimators of the joint distribution function for a survival time and a continuous mark variable when the survival time is interval censored and the mark variable may be missing for interval-censored observations. Finite and large sample properties are described for the nonparametric maximum likelihood estimator (NPMLE) as well as estimators based on midpoint imputation (MIDMLE) and coarsening the mark variable (CMLE). The estimators are compared using data from a simulation study and a recent phase III HIV vaccine efficacy trial where the survival time is the time from enrollment to infection and the mark variable is the genetic distance from the infecting HIV sequence to the HIV sequence in the vaccine. Theoretical and empirical evidence are presented indicating the NPMLE and MIDMLE are inconsistent. Conversely, the CMLE is shown to be consistent in general and thus is preferred.
本文考虑了生存时间为区间删失且标记变量在区间删失观测中可能缺失时，生存时间与连续标记变量联合分布函数的三种非参数估计量。描述了非参数极大似然估计量（NPMLE）以及基于中点插补的估计量（MIDMLE）和标记变量粗化估计量（CMLE）的有限样本和大样本性质。使用模拟研究数据和最近的一项III期HIV疫苗疗效试验数据对这些估计量进行了比较，其中生存时间是从入组到感染的时间，标记变量是从感染的HIV序列到疫苗中HIV序列的遗传距离。给出了理论和实证证据，表明NPMLE和MIDMLE是不一致的。相反，CMLE总体上被证明是一致的，因此更受青睐。
