Lamb Karen E, Williamson Elizabeth J, Coory Michael, Carlin John B
Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, VIC, Australia.
Murdoch Childrens Research Institute, Royal Children's Hospital, Melbourne, VIC, Australia.
Pharm Stat. 2015 Sep-Oct;14(5):409-17. doi: 10.1002/pst.1700. Epub 2015 Jul 27.
In cost-effectiveness analyses of drugs or health technologies, estimates of life years saved or quality-adjusted life years saved are required. Randomised controlled trials can provide an estimate of the average treatment effect; for survival data, the treatment effect is the difference in mean survival. However, typically not all patients will have reached the endpoint of interest at the close-out of a trial, making it difficult to estimate the difference in mean survival. In this situation, it is common to report the more readily estimable difference in median survival. Alternative approaches to estimating the mean have also been proposed. We conducted a simulation study to investigate the bias and precision of the three most commonly used sample measures of absolute survival gain--difference in median, restricted mean and extended mean survival--when used as estimates of the true mean difference, under different censoring proportions, while assuming a range of survival patterns, represented by Weibull survival distributions with constant, increasing and decreasing hazards. Our study showed that the three commonly used methods tended to underestimate the true treatment effect; consequently, the incremental cost-effectiveness ratio (ICER) would be overestimated. Of the three methods, the least biased is the extended mean survival, which perhaps should be used as the point estimate of the treatment effect to be inputted into the ICER, while the other two approaches could be used in sensitivity analyses. More work on the trade-offs between simple extrapolation using the exponential distribution and more complicated extrapolation using other methods would be valuable.
在药物或卫生技术的成本效益分析中，需要对挽救的生命年数或质量调整生命年数进行估计。随机对照试验可以提供平均治疗效果的估计值；对于生存数据，治疗效果是平均生存期的差异。然而，通常并非所有患者在试验结束时都会达到感兴趣的终点，这使得估计平均生存期的差异变得困难。在这种情况下，报告更容易估计的中位生存期差异很常见。也有人提出了估计均值的替代方法。我们进行了一项模拟研究，以调查在不同删失比例下，当三种最常用的绝对生存获益样本量度——中位生存期差异、受限平均生存期和延长平均生存期——用作真实均值差异的估计值时，在一系列生存模式（由具有恒定、增加和减少风险的威布尔生存分布表示）下的偏差和精度。我们的研究表明，这三种常用方法往往会低估真实的治疗效果；因此，增量成本效益比（ICER）会被高估。在这三种方法中，偏差最小的是延长平均生存期，也许应该将其用作输入到ICER中的治疗效果的点估计值，而其他两种方法可用于敏感性分析。关于使用指数分布进行简单外推与使用其他方法进行更复杂外推之间权衡的更多研究将是有价值的。
