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

Lilian Golzarri-Arroyo, Stephanie L. Dickinson, Yasaman Jamshidi-Naeini, Roger S. Zoh, Andrew W. Brown, Arthur H. Owora, Peng Li, J. Michael Oakes, David B. Allison

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Background: 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. Methods: 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”. Results: 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.

Original languageEnglish (US)
Article number106654
JournalComputer Methods and Programs in Biomedicine
Volume215
DOIs
StatePublished - Mar 2022

Bibliographical note

Funding Information:
The computation of this research was performed on IU's Big Red 3 supercomputer, which was supported in part by Lilly Endowment, Inc., through its support for the Indiana University Pervasive Technology Institute. We thank Jennifer Holmes for language editing. We are grateful to Dr. Keith Muller and Dr. Pengcheng Xun for their constructive comments.

Funding Information:
This work was supported by the National Institutes of Health (NIH) [grant numbers R25HL124208, R25DK099080, R25GM141507, P30AG050886, and U24AG056053]. The opinions expressed are those of the authors and do not necessarily represent those of the NIH or any other organization.

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Cluster Analysis
  • Computer Simulation
  • Humans
  • Linear Models
  • Research Design
  • Sample Size

PubMed: MeSH publication types

  • Journal Article
  • Randomized Controlled Trial

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