Shu Xu, Schaubel Douglas E
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, 48109-2029, U.S.A.
Biometrics. 2016 Jun;72(2):525-34. doi: 10.1111/biom.12427. Epub 2015 Oct 26.
Times between successive events (i.e., gap times) are of great importance in survival analysis. Although many methods exist for estimating covariate effects on gap times, very few existing methods allow for comparisons between gap times themselves. Motivated by the comparison of primary and repeat transplantation, our interest is specifically in contrasting the gap time survival functions and their integration (restricted mean gap time). Two major challenges in gap time analysis are non-identifiability of the marginal distributions and the existence of dependent censoring (for all but the first gap time). We use Cox regression to estimate the (conditional) survival distributions of each gap time (given the previous gap times). Combining fitted survival functions based on those models, along with multiple imputation applied to censored gap times, we then contrast the first and second gap times with respect to average survival and restricted mean lifetime. Large-sample properties are derived, with simulation studies carried out to evaluate finite-sample performance. We apply the proposed methods to kidney transplant data obtained from a national organ transplant registry. Mean 10-year graft survival of the primary transplant is significantly greater than that of the repeat transplant, by 3.9 months (p=0.023), a result that may lack clinical importance.
连续事件之间的时间间隔（即间隔时间）在生存分析中非常重要。尽管存在许多估计协变量对间隔时间影响的方法，但现有的方法中很少有能对间隔时间本身进行比较的。受初次移植和再次移植比较的启发，我们特别关注对比间隔时间生存函数及其积分（受限平均间隔时间）。间隔时间分析中的两个主要挑战是边缘分布的不可识别性和相依删失的存在（除第一个间隔时间外的所有间隔时间）。我们使用Cox回归来估计每个间隔时间的（条件）生存分布（给定先前的间隔时间）。结合基于这些模型拟合的生存函数，以及应用于删失间隔时间的多重填补，然后我们在平均生存和受限平均寿命方面对比第一个和第二个间隔时间。推导了大样本性质，并进行了模拟研究以评估有限样本性能。我们将所提出的方法应用于从国家器官移植登记处获得的肾移植数据。初次移植的平均10年移植物存活率比再次移植显著高3.9个月（p = 0.023），这一结果可能缺乏临床重要性。
