Power Estimation in Planning Randomized Two-Arm Pre-Post Intervention Trials with Repeated Longitudinal Outcomes.

Journal of biometrics & biostatistics Pub Date : 2018-01-01 Epub Date: 2018-06-20 DOI:10.4172/2155-6180.1000403
Yirui Hu, Donald R Hoover
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引用次数: 1

Abstract

Background: Intervention effect on ongoing medical processes is estimated from clinical trials on units (i.e. persons or facilities) with fixed timing of repeated longitudinal measurements. All units start out untreated. A randomly chosen subset is switched to the intervention at the same time point. The pre-post switch change in the outcome between these units and unswitched controls is compared using Generalized Least Squares models. Power estimation for such studies is hindered by lack of available GLS based approaches and normative data.

Methods: We derive Generalized Least Squares variance of the intervention effect. For the commonly assumed compound symmetry correlation structure, this leads to simple power formulas with important optimality properties. To maximize power given a constrained number of total time points, we investigate on the optimal pre-post allocation with the local minimization of variance.

Results: In four examples from nursing home and HIV patients, the Toepltiz within-unit correlation of repeated measures differed from compound symmetry. We applied empirical Toeplitz based calculations for variance of the estimated intervention effect to these examples (each with up to seven longitudinal measures). Unlike what happened under compound symmetry, where power was often maximized with multiple observations being pre-intervention, for these examples, having one pre-intervention measure tended to maximize power. Attempts to approximate the Toeplitz variance structures with compound symmetry (to take advantage of the simpler formulas) resulted in overestimation of power for these examples.

Conclusions: While compound symmetry correlation among repeated within-unit measures leads to simple power estimation formulas, this structure often did not hold. There may be strong underestimation of variance of the intervention effect estimate from incorporating short-term within-unit correlation estimates as a common compound symmetry correlation to approximate an unknown Toeplitz correlation without adequately accounting for the correlation between repeated measures declining with time.

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具有重复纵向结果的计划随机双臂干预前后试验的功效估计。
背景:干预对正在进行的医疗过程的影响是通过对单位(即人员或设施)进行临床试验来估计的,这些单位具有固定的重复纵向测量时间。所有单位一开始都没有治疗。一个随机选择的子集在同一时间点切换到干预。使用广义最小二乘模型比较这些单元和未切换控制之间的结果在切换前和切换后的变化。由于缺乏可用的基于GLS的方法和规范数据,此类研究的功率估计受到阻碍。方法:导出干预效果的广义最小二乘方差。对于通常假设的复合对称相关结构,这导致了具有重要最优性的简单幂公式。为了在给定总时间点数量的约束下使功率最大化,我们研究了局部方差最小的最优前后分配问题。结果:在4例养老院和HIV患者中,重复测量的Toepltiz单位内相关不同于复合对称。我们对这些例子(每个例子最多有七个纵向测量)应用了基于实证Toeplitz的估计干预效果方差计算。与复合对称不同的是,在这种情况下,权力往往是通过多次观察作为预干预来最大化的,对于这些例子来说,有一个预干预措施往往会最大化权力。试图用复合对称近似Toeplitz方差结构(以利用更简单的公式)会导致对这些示例的功率估计过高。结论:虽然重复单位内测量之间的复合对称相关导致简单的功率估计公式,但这种结构往往不成立。由于将短期单位内相关估计作为一种常见的复合对称相关来近似未知的Toeplitz相关,而没有充分考虑随时间下降的重复测量之间的相关性,可能会严重低估干预效果估计的方差。
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