Robust analyzes for longitudinal clinical trials with missing and non-normal continuous outcomes

IF 0.7 Q3 STATISTICS & PROBABILITY
Siyi Liu, Yilong Zhang, Gregory T. Golm, Guanghan (Frank) Liu, Shu Yang
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引用次数: 1

Abstract

Missing data is unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conventional multiple imputation with the mixed model with repeated measures analysis of the average treatment effect (ATE) based on the multivariate normal assumption may produce bias and power loss. Control-based imputation (CBI) is an approach for evaluating the treatment effect under the assumption that participants in both the test and control groups with missing outcome data have a similar outcome profile as those with an identical history in the control group. We develop a robust framework to handle non-normal outcomes under CBI without imposing any parametric modeling assumptions. Under the proposed framework, sequential weighted robust regressions are applied to protect the constructed imputation model against non-normality in the covariates and the response variables. Accompanied by the subsequent mean imputation and robust model analysis, the resulting ATE estimator has good theoretical properties in terms of consistency and asymptotic normality. Moreover, our proposed method guarantees the analysis model robustness of the ATE estimation in the sense that its asymptotic results remain intact even when the analysis model is misspecified. The superiority of the proposed robust method is demonstrated by comprehensive simulation studies and an AIDS clinical trial data application.
对缺失和非正常连续结果的纵向临床试验进行稳健分析
在纵向临床试验中,数据缺失是不可避免的,而且结果并不总是正态分布的。在存在异常值或重尾分布的情况下,基于多元正态假设的混合模型和重复测量分析平均处理效果(ATE)的传统多重拟合可能会产生偏差和功率损失。基于对照的归算(control -based imputation, CBI)是一种评估治疗效果的方法,假设缺失结果数据的试验组和对照组的参与者与对照组中具有相同病史的参与者具有相似的结果概况。我们开发了一个鲁棒框架来处理CBI下的非正态结果,而不强加任何参数化建模假设。在该框架下,采用顺序加权鲁棒回归来保护构建的imputation模型免受协变量和响应变量的非正态性影响。伴随着随后的均值imputation和鲁棒模型分析,所得的ATE估计量在一致性和渐近正态性方面具有良好的理论性质。此外,我们提出的方法保证了分析模型的稳健性,即即使分析模型被错误指定,其渐近结果也保持完整。综合仿真研究和艾滋病临床试验数据的应用证明了所提出的鲁棒方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.90
自引率
20.00%
发文量
21
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