Ahn Hongshik, Moon Hojin, Kodell Ralph L
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794-3600, USA.
J Biopharm Stat. 2008;18(5):901-14. doi: 10.1080/10543400802287453.
A new statistical method for estimating the lag time between onset of and death from an occult tumor is proposed for data without cause-of-death information. In this method, the survival function for time to tumor onset, tumor-specific survival function, and competing risks survival function are estimated using the maximum likelihood estimates of the parameters. The proposed method utilizes the estimated survival functions and statistically imputed fatal tumors to estimate the lag time. This approach is developed for rodent tumorigenicity assays that have at least one interim sacrifice and a terminal sacrifice. If the data contain cause-of-death information given by pathologists and it is believed to be reliable, it may be used for estimating the lag time. The proposed method is illustrated using a real data set. The accuracy of the estimation of lag time is evaluated via a Monte Carlo simulation study. This study shows that the estimated lag time is quite reliable.
针对无死因信息的数据,提出了一种新的统计方法,用于估计隐匿性肿瘤发病与死亡之间的滞后时间。在该方法中,使用参数的最大似然估计来估计肿瘤发病时间的生存函数、肿瘤特异性生存函数和竞争风险生存函数。所提出的方法利用估计的生存函数和统计推断的致命肿瘤来估计滞后时间。这种方法是为至少有一次中期处死和一次终末处死的啮齿动物致癌性试验而开发的。如果数据包含病理学家给出的死因信息且被认为可靠,则可用于估计滞后时间。使用真实数据集对所提出的方法进行了说明。通过蒙特卡罗模拟研究评估了滞后时间估计的准确性。该研究表明,估计的滞后时间相当可靠。