Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials with binary and count outcomes.

IF 3.9 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Kenneth Menglin Lee, Grace Meijuan Yang, Yin Bun Cheung
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引用次数: 0

Abstract

Background: The fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. While all clusters in a SW-CRT are typically designed to crossover from the control to receive the intervention, some trials can end with unexposed clusters (clusters that never receive the intervention), such as when a trial is terminated early due to safety concerns. It was previously unclear whether unexposed clusters would contribute to the estimation of the intervention effect in a fixed effects analysis. However, recent work has demonstrated that including an unexposed cluster can improve the precision of the intervention effect estimator in a fixed effects analysis of SW-CRTs with continuous outcomes. Still, SW-CRTs are commonly designed with binary outcomes and it is unknown if those previous results extend to SW-CRTs with non-continuous outcomes.

Methods: In this article, we mathematically prove that the inclusion of unexposed clusters improves the precision of the fixed effects intervention effect estimator for SW-CRTs with binary and count outcomes. We then explore the benefits of including an unexposed cluster in simulated datasets with binary or count outcomes and a real palliative care data example with binary outcomes.

Results: The simulations show that including unexposed clusters leads to tangible improvements in the precision, power, and root mean square error of the intervention effect estimator. The inclusion of the unexposed cluster in the SW-CRT of a novel palliative care intervention with binary outcomes yielded smaller standard errors and narrower 95% Wald Confidence Intervals.

Conclusions: In this article, we demonstrate that the inclusion of unexposed clusters in the fixed effects analysis can lead to the improvements in precision, power, and RMSE of the fixed effects intervention effect estimator for SW-CRTs with binary or count outcomes.

纳入未暴露群组可提高二元和计数结果的阶梯楔形群组随机试验固定效应分析的精确度。
背景:固定效应模型是混合效应模型的一种有效替代方法,可用于分析阶梯楔形分组随机试验(SW-CRT)。它可以控制所有时间不变的群组级混杂因素,并在群组数量较少时适当控制 I 型误差。虽然 SW-CRT 中的所有群组通常都被设计为从对照组交叉到接受干预的群组,但有些试验可能会以未暴露群组(从未接受干预的群组)结束,例如当试验因安全性问题而提前终止时。以前还不清楚在固定效应分析中,未暴露群组是否有助于估计干预效果。然而,最近的研究表明,在对具有连续性结果的 SW-CRT 进行固定效应分析时,纳入未暴露群组可以提高干预效应估计值的精确度。尽管如此,SW-CRT 通常是针对二元结果设计的,而之前的这些结果是否适用于非连续结果的 SW-CRT,目前还不得而知:在本文中,我们用数学方法证明,对于二元和计数结果的 SW-CRT 而言,纳入未暴露群组可提高固定效应干预效果估计的精确度。然后,我们在具有二元或计数结果的模拟数据集和具有二元结果的真实姑息治疗数据示例中探讨了纳入未暴露群集的益处:模拟结果表明,加入未暴露群组可显著提高干预效果估计的精确度、功率和均方根误差。将未暴露群组纳入具有二元结果的新型姑息治疗干预的 SW-CRT 中,可获得更小的标准误差和更窄的 95% Wald 置信区间:在这篇文章中,我们证明了在固定效应分析中纳入未暴露群组可以提高二元或计数结果的SW-CRT固定效应干预效果估计器的精度、功率和均方误差。
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
BMC Medical Research Methodology
BMC Medical Research Methodology 医学-卫生保健
CiteScore
6.50
自引率
2.50%
发文量
298
审稿时长
3-8 weeks
期刊介绍: BMC Medical Research Methodology is an open access journal publishing original peer-reviewed research articles in methodological approaches to healthcare research. Articles on the methodology of epidemiological research, clinical trials and meta-analysis/systematic review are particularly encouraged, as are empirical studies of the associations between choice of methodology and study outcomes. BMC Medical Research Methodology does not aim to publish articles describing scientific methods or techniques: these should be directed to the BMC journal covering the relevant biomedical subject area.
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