利用治愈模型和多重插补，将复发作为总生存的辅助变量。
Using cure models and multiple imputation to utilize recurrence as an auxiliary variable for overall survival.
机构信息
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.
出版信息
Clin Trials. 2011 Oct;8(5):581-90. doi: 10.1177/1740774511414741. Epub 2011 Sep 15.
BACKGROUND
Intermediate outcome variables can often be used as auxiliary variables for the true outcome of interest in randomized clinical trials. For many cancers, time to recurrence is an informative marker in predicting a patient's overall survival outcome and could provide auxiliary information for the analysis of survival times.
PURPOSE
To investigate whether models linking recurrence and death combined with a multiple imputation procedure for censored observations can result in efficiency gains in the estimation of treatment effects and be used to shorten trial lengths.
METHODS
Recurrence and death times are modeled using data from 12 trials in colorectal cancer. Multiple imputation is used as a strategy for handling missing values arising from censoring. The imputation procedure uses a cure model for time to recurrence and a time-dependent Weibull proportional hazards model for time to death. Recurrence times are imputed, and then death times are imputed conditionally on recurrence times. To illustrate these methods, trials are artificially censored 2 years after the last accrual, the imputation procedure implemented, and a log-rank test and Cox model used to analyze and compare these new data with the original data.
RESULTS
The results show modest, but consistent gains in efficiency in the analysis using the auxiliary information in recurrence times. Comparison of analyses show the treatment effect estimates and log-rank test results from the 2-year censored imputed data to be in between the estimates from the original data and the artificially censored data, indicating that the procedure was able to recover some of the lost information due to censoring.
LIMITATIONS
The models used are all fully parametric, requiring distributional assumptions of the data.
CONCLUSIONS
The proposed models may be useful in improving the efficiency in estimation of treatment effects in cancer trials and shortening trial length.
背景
中间结果变量通常可以作为随机临床试验中真正感兴趣的结果的辅助变量。对于许多癌症，复发时间是预测患者总体生存结果的一个有价值的标志物，并且可以为生存时间的分析提供辅助信息。
目的
探讨将复发和死亡联系起来的模型，以及用于处理截尾观测的多重插补程序，是否可以提高治疗效果估计的效率，并缩短试验长度。
方法
使用来自 12 项结直肠癌试验的数据来建模复发和死亡时间。使用多重插补作为处理缺失值的策略，这些缺失值是由于截尾引起的。插补程序使用复发时间的治愈模型和死亡时间的时变 Weibull 比例风险模型。首先对复发时间进行插补，然后根据复发时间对死亡时间进行条件插补。为了说明这些方法，将试验在最后一次入组后 2 年进行人为截尾，实施插补程序，并使用对数秩检验和 Cox 模型对这些新数据与原始数据进行分析和比较。
结果
结果表明，在使用复发时间辅助信息进行分析时，效率略有提高，但较为一致。分析结果比较表明，2 年截尾插补数据的分析估计值和对数秩检验结果介于原始数据和人为截尾数据的估计值之间，表明该程序能够恢复由于截尾而丢失的部分信息。
局限性
使用的模型都是完全参数化的，需要数据的分布假设。
结论
所提出的模型可能有助于提高癌症试验中治疗效果估计的效率，并缩短试验长度。
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