Department of Biostatistics, Yale School of Public Health, 135 College Street, CT, New Haven, 06510, USA.
Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT, USA.
BMC Med Res Methodol. 2023 Apr 6;23(1):85. doi: 10.1186/s12874-023-01887-8.
Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming missing completely at random or missing at random have been previously developed, the impact of attrition on the power for detecting heterogeneous treatment effects in cluster randomized trials remains unknown.
We provide a sample size formula for testing for a heterogeneous treatment effect assuming the outcome is missing completely at random. We also propose an efficient Monte Carlo sample size procedure for assessing heterogeneous treatment effect assuming covariate-dependent outcome missingness (missing at random). We compare our sample size methods with the direct inflation method that divides the estimated sample size by the mean follow-up rate. We also evaluate our methods through simulation studies and illustrate them with a real-world example.
Simulation results show that our proposed sample size methods under both missing completely at random and missing at random provide sufficient power for assessing heterogeneous treatment effect. The proposed sample size methods lead to more accurate sample size estimates than the direct inflation method when the missingness rate is high (e.g., ≥ 30%). Moreover, sample size estimation under both missing completely at random and missing at random is sensitive to the missingness rate, but not sensitive to the intracluster correlation coefficient among the missingness indicators.
Our new sample size methods can assist in planning cluster randomized trials that plan to assess a heterogeneous treatment effect and participant attrition is expected to occur.
在群组随机试验和实施研究中，检测治疗效果异质性是一个重要目标。虽然已经提出了用于检验考虑参与者失访的平均处理效应的样本量程序，这些失访假设为完全随机缺失或随机缺失，但失访对群组随机试验中检测异质治疗效果的功效的影响仍不清楚。
我们提供了一个用于检验假设结局完全随机缺失的异质处理效应的样本量公式。我们还提出了一种有效的蒙特卡罗样本量程序，用于评估协变量相关结局缺失（随机缺失）情况下的异质处理效应。我们将我们的样本量方法与直接膨胀法进行了比较，后者将估计的样本量除以平均随访率。我们还通过模拟研究评估了我们的方法，并通过一个实际例子来说明。
模拟结果表明，我们在完全随机缺失和随机缺失两种情况下提出的样本量方法都能为评估异质处理效果提供足够的功效。当缺失率较高（例如，≥30%）时，与直接膨胀法相比，我们提出的样本量方法能更准确地估计样本量。此外，完全随机缺失和随机缺失下的样本量估计都对缺失率敏感，但对缺失指标的组内相关系数不敏感。
我们的新样本量方法可以协助规划预计会发生参与者失访的群组随机试验，并评估异质治疗效果。
