A Data-Generation Process for Data with Specified Risk Differences or Numbers Needed to Treat
A Data-Generation Process for Data with Specified Risk Differences or Numbers Needed to Treat
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DOI：
10.1080/03610910903528301
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发表时间：
2010-01-01
影响因子：
0.9
通讯作者：
Austin, Peter C.
中科院分区：
文献类型：
--
作者：
Austin, Peter C.
蒙特卡罗模拟方法越来越多地被用于评估统计方法和估计器的性能。然而，这些方法的实用性取决于是否存在适当的数据生成过程。临床评论员建议，风险差异和需要治疗的相关数字(NNT)是在结果是二元的情况下衡量治疗效果的重要指标。虽然这些量在随机对照试验中很容易估计，但人们对使用观察性或非随机化数据估计这些量的方法越来越感兴趣。然而，缺乏用于模拟数据的数据生成过程，在该过程中处理导致特定的风险差异，阻碍了对这些方法的性能的系统检查。在目前的研究中，我们描述和评估了模拟数据的数据生成过程的性能，在该过程中，处理导致特定的风险差异。这一过程基于使用蒙特卡罗积分评估边际风险差异的迭代过程。拟议的数据生成过程是灵活的，可以很容易地纳入基线协变量的不同分布和事件基线风险的不同水平。数据生成过程也可以很容易地修改，以模拟治疗导致特定相对风险的数据。
Monte Carlo simulation methods are increasingly being used to evaluate the performance of statistical methods and estimators. However, the utility of these methods depends upon the existence of appropriate data-generating processes. Clinical commentators have suggested that the risk difference and the associated number needed to treat (NNT) are important measures of treatment effect when outcomes are binary. While these quantities are easily estimated in randomized controlled trials, there is an increasing interest in methods to estimate these quantities using observational or non-randomized data. However, the lack of a data-generating process for simulating data in which treatment induces a specified risk difference hinders the systematic examination of the performance of these methods. In the current study, we describe and evaluate the performance of a data-generating process for simulating data in which treatment induces a specified risk difference. The process is based upon an iterative process of evaluating marginal risk differences using Monte Carlo integration. The proposed data-generating process is flexible and can easily incorporate different distributions for baseline covariates and different levels of the baseline risk of the event. The data-generating process can also be easily modified to simulate data in which treatment induces a specified relative risk.