An evaluation of inverse probability weighting using the propensity score for baseline covariate adjustment in smaller population randomised controlled trials with a continuous outcome
An evaluation of inverse probability weighting using the propensity score for baseline covariate adjustment in smaller population randomised controlled trials with a continuous outcome
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DOI：
10.1186/s12874-020-00947-7
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发表时间：
2020-03-23
影响因子：
4
通讯作者：
Cro, Suzie
中科院分区：
文献类型：
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作者：
Raad, Hanaya;Cornelius, Victoria;Cro, Suzie
背景在随机对照试验分析中，准确、精确地估计感兴趣的治疗效果是很重要的。提高估计精确度的一种方法，从而提高具有连续结局的随机试验的功效，是通过调整预先指定的预后基线协变量。通常使用回归分析进行协变量调整，但最近提出了使用倾向评分的治疗加权逆概率（IPTW）作为替代方法。对于一个连续的结果，它已被证明，IPTW估计具有相同的大样本统计特性，通过协方差分析获得。然而，IPTW的性能尚未在较小的人群试验（< 100名参与者）中进行探索，在这些试验中，精确估计治疗效果可能比在较大的样本中产生更大的影响。方法在本文中，我们探讨了在较小的人口试验设置使用IPTW估计的基线调整的治疗效果的性能。为此，我们提出了一个模拟研究，包括一些不同的试验方案，样本量范围从40到200，并调整多达6个协变量。我们还重新分析了一项包括60名儿童的儿科湿疹试验。结果在模拟研究中，IPTW方差估计器的性能是次优的较小的样本容量。对于样本量= 100，95% CI的覆盖率略低于95%< 150 and >。对于样本量< 100，所有协变量设置的95%CI覆盖率始终显著低于95%。使用IPTW获得的最小覆盖率为89%，n = 40。相比之下，回归调整总是导致95%的覆盖率。湿疹试验的分析证实了在真实的小人群环境中IPTW和回归估计值之间的差异。结论IPTW方差估计在小样本情况下表现不佳。因此，当样本量小于150时，特别是当样本量< 100时，我们警告不要在小样本设置中使用IPTW。
Background It is important to estimate the treatment effect of interest accurately and precisely within the analysis of randomised controlled trials. One way to increase precision in the estimate and thus improve the power for randomised trials with continuous outcomes is through adjustment for pre-specified prognostic baseline covariates. Typically covariate adjustment is conducted using regression analysis, however recently, Inverse Probability of Treatment Weighting (IPTW) using the propensity score has been proposed as an alternative method. For a continuous outcome it has been shown that the IPTW estimator has the same large sample statistical properties as that obtained via analysis of covariance. However the performance of IPTW has not been explored for smaller population trials (< 100 participants), where precise estimation of the treatment effect has potential for greater impact than in larger samples. Methods In this paper we explore the performance of the baseline adjusted treatment effect estimated using IPTW in smaller population trial settings. To do so we present a simulation study including a number of different trial scenarios with sample sizes ranging from 40 to 200 and adjustment for up to 6 covariates. We also re-analyse a paediatric eczema trial that includes 60 children. Results In the simulation study the performance of the IPTW variance estimator was sub-optimal with smaller sample sizes. The coverage of 95% CI's was marginally below 95% for sample sizes < 150 and >= 100. For sample sizes < 100 the coverage of 95% CI's was always significantly below 95% for all covariate settings. The minimum coverage obtained with IPTW was 89% with n = 40. In comparison, regression adjustment always resulted in 95% coverage. The analysis of the eczema trial confirmed discrepancies between the IPTW and regression estimators in a real life small population setting. Conclusions The IPTW variance estimator does not perform so well with small samples. Thus we caution against the use of IPTW in small sample settings when the sample size is less than 150 and particularly when sample size < 100.