Robustness of ordinary least squares in randomized clinical trials
Robustness of ordinary least squares in randomized clinical trials
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DOI：
10.1002/sim.6839
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发表时间：
2016-05-20
影响因子：
2
通讯作者：
Porter, Kristin E.
中科院分区：
文献类型：
--
作者：
Judkins, David R.;Porter, Kristin E.
该杂志上有一系列关于临床试验分析中稳健协变量控制的半参数方法的偶发论文。这些方法在当前可用的计算机上相当容易应用，但标准软件包尚不支持这些方法以及简单的选项选择。此外，这些方法可能很难向仅受过基础统计教育的从业者解释。还有一段被忽视的历史表明，普通最小二乘法 (OLS) 对于结果分布特征类型非常稳健，这些特征激发了稳健协变量控制的新方法。我们回顾了这两方面的文献，并报告了一些新的模拟，这些模拟证明了 OLS 对比之前探索的更极端的正态性违规的鲁棒性。新的模拟涉及两个强烈的尖峰结果：接近零的二元结果和零膨胀的伽马结果。此类结果的潜在例子分别包括 IV 期癌症的 5 年生存率和罕见疾病的医疗索赔金额。我们发现，对于这样的结果，传统的 OLS 方法在样本量非常小的情况下也能很好地发挥作用。在某些情况下，具有稳健标准误差的 OLS 可以很好地处理较小的样本量。鉴于这篇文献综述和我们的新模拟，我们认为大多数研究人员可以轻松地继续使用标准 OLS 软件，最好具有稳健的标准误差。版权所有 (c) 2015 约翰·威利父子有限公司
There has been a series of occasional papers in this journal about semiparametric methods for robust covariate control in the analysis of clinical trials. These methods are fairly easy to apply on currently available computers, but standard software packages do not yet support these methods with easy option selections. Moreover, these methods can be difficult to explain to practitioners who have only a basic statistical education. There is also a somewhat neglected history demonstrating that ordinary least squares (OLS) is very robust to the types of outcome distribution features that have motivated the newer methods for robust covariate control. We review these two strands of literature and report on some new simulations that demonstrate the robustness of OLS to more extreme normality violations than previously explored. The new simulations involve two strongly leptokurtic outcomes: near-zero binary outcomes and zero-inflated gamma outcomes. Potential examples of such outcomes include, respectively, 5-year survival rates for stage IV cancer and healthcare claim amounts for rare conditions. We find that traditional OLS methods work very well down to very small sample sizes for such outcomes. Under some circumstances, OLS with robust standard errors work well with even smaller sample sizes. Given this literature review and our new simulations, we think that most researchers may comfortably continue using standard OLS software, preferably with the robust standard errors. Copyright (c) 2015 John Wiley & Sons, Ltd.