Evaluation of the type I error rate when using parametric bootstrap analysis of a cluster randomized controlled trial with binary outcomes and a small number of clusters.
Evaluation of the type I error rate when using parametric bootstrap analysis of a cluster randomized controlled trial with binary outcomes and a small number of clusters.
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DOI：
10.1016/j.cmpb.2022.106654
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发表时间：
2022-03
影响因子：
6.1
通讯作者：
Allison DB
中科院分区：
文献类型：
--
作者：
Golzarri-Arroyo L;Dickinson SL;Jamshidi-Naeini Y;Zoh RS;Brown AW;Owora AH;Li P;Oakes JM;Allison DB
Cluster randomized controlled trials (cRCTs) are increasingly used but must be analyzed carefully. We conducted a simulation study to evaluate the validity of a parametric bootstrap (PB) approach with respect to the empirical type I error rate for a cRCT with binary outcomes and a small number of clusters. We simulated a case study with a binary (0/1) outcome, four clusters, and 100 subjects per cluster. To compare the validity of the test with respect to error rate, we simulated the same experiment with K=10, 20, and 30 clusters, each with 2,000 simulated datasets. To test the null hypothesis, we used a generalized linear mixed model including a random intercept for clusters and obtained p-values based on likelihood ratio tests (LRTs) using the parametric bootstrap method as implemented in the R package “pbkrtest”. The PB test produced error rates of 9.1%, 5.5%, 4.9%, and 5.0% on average across all ICC values for K=4, K=10, K=20, and K=30, respectively. The error rates were higher, ranging from 9.1% to 36.5% for K=4, in the models with singular fits (i.e., ignoring clustering) because the ICC was estimated to be zero. Using the parametric bootstrap for cRCTs with a small number of clusters results in inflated error rates and is not valid.
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影响因子：
2.3
作者：
Li F;Hughes JP;Hemming K;Taljaard M;Melnick ER;Heagerty PJ
通讯作者：
Heagerty PJ
影响因子：
4
作者：
Li P;Redden DT
通讯作者：
Redden DT
影响因子：
7.7
作者：
Leyrat, Clemence;Morgan, Katy E.;Kahan, Brennan C.
通讯作者：
Kahan, Brennan C.
DOI：
10.1093/biostatistics/kxaa056
发表时间：
2022-07-18
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
通讯作者：
--
影响因子：
4
作者：
Ma, Jinhui;Thabane, Lehana;Levitt, Cheryl
通讯作者：
Levitt, Cheryl