Background

In a cross-sectional stepped-wedge cluster randomized trial comparing usual care to a new intervention, treatment allocation and time are correlated by design because participants enrolled early in the trial predominantly receive usual care while those enrolled late in the trial predominantly receive the new intervention. Current guidelines recommend adjustment for time effects when analyzing stepped-wedge cluster randomized trials to remove the confounding bias induced by this correlation. However, adjustment for time effects impacts study power. Within the Frequentist framework, adopting a sample size calculation that includes time effects would ensure the trial having adequate power regardless of the magnitude of the effect of time on the outcome. But if in fact time effects were negligible, this would overestimate the required sample size and could lead to the trial being deemed infeasible due to cost or unavailability of the required numbers of clusters or participants. In this study, we explore the use of prior information on time effects to potentially reduce the required sample size of the trial.

Methods

We applied a Bayesian approach to incorporate the prior information on the time effects into cluster-level statistical models (for continuous, binary, or count outcomes) for the stepped-wedge cluster randomized trial. We conducted simulations to illustrate how the bias in the intervention effect estimate and the trial power vary as a function of the prior precision and the mis-specification of the prior means of the time effects in an example scenario.

Results

When a nearly flat prior for the time effects was used, the power or sample size calculated using the Bayesian approach matched the result obtained using the Frequentist approach with time effects included. When a highly precise prior for the time effects (with accurately specified prior means) was used, the Bayesian result matched the Frequentist result obtained with time effects excluded. When the prior means of the time effects were nearly correctly specified, including this information improved the efficiency of the trial with little bias introduced into the intervention effect estimate. When the prior means of the time effects were greatly mis-specified and a precise prior was used, this bias was substantial.

Conclusion

Including prior information on time effects using a Bayesian approach may substantially reduce the required sample size. When the prior can be justified, results from applying this approach could support the conduct of a trial, which would be deemed infeasible if based on the larger sample size obtained using a Frequentist calculation. Caution is warranted as biased intervention effect estimates may arise when the prior distribution for the time effects is concentrated far from their true values.

中文翻译：

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通过具有时间效应信息先验的贝叶斯建模提高阶梯楔试验设计的效率

背景

在一项比较常规护理与新干预措施的横断面阶梯楔形随机试验中，治疗分配和时间在设计上是相关的，因为早期参加试验的参与者主要接受常规护理，而参加试验后期的参与者主要接受新干预措施. 当前指南建议在分析阶梯楔形随机试验时调整时间效应，以消除这种相关性引起的混杂偏倚。然而，调整时间效应会影响研究能力。在频率论框架内，采用包含时间效应的样本量计算将确保试验具有足够的功效，而不管时间对结果的影响程度如何。但如果实际上时间影响可以忽略不计 这会高估所需的样本量，并可能导致试验因成本或所需数量的集群或参与者不可用而被视为不可行。在这项研究中，我们探索使用关于时间效应的先验信息来潜在地减少试验所需的样本量。

方法

我们应用贝叶斯方法将时间效应的先验信息合并到阶梯楔形集群随机试验的集群级统计模型（连续、二元或计数结果）中。我们进行了模拟，以说明在示例场景中干预效果估计和试验功效的偏差如何作为先验精度和时间效应先验均值的错误指定的函数而变化。

结果

当使用时间效应几乎平坦的先验时，使用贝叶斯方法计算的功效或样本大小与使用包含时间效应的频率论方法获得的结果相匹配。当使用时间效应的高度精确先验（具有准确指定的先验平均值）时，贝叶斯结果与排除时间效应获得的频率论结果相匹配。当时间效应的先验均值几乎被正确指定时，包括这些信息可以提高试验的效率，而在干预效应估计中引入的偏差很小。当时间效应的先验均值被严重错误指定并使用精确的先验时，这种偏差是很大的。

结论

使用贝叶斯方法包括有关时间效应的先验信息可能会大大减少所需的样本量。当可以证明先验是合理的时，应用这种方法的结果可以支持试验的进行，如果基于使用频率计算获得的更大样本量，这将被认为是不可行的。需要谨慎，因为当时间效应的先验分布远离其真实值时，可能会出现有偏差的干预效应估计。