Sample size planning in the design and analysis of cluster randomized trials using the symbolic two-step method

Research output: Contribution to journalArticle

Abstract

Introduction: Evidence that can be used to improve clinical practice patterns and processes is frequently generated through standard, parallel-arms cluster randomized trial (CRT) designs that test interventions implemented at the center-level. Although the primary endpoint of these trials is often a center-level outcome, patient-level factors may vary between centers and, consequently, may influence the center-level outcome. Furthermore, there may be important factors that predict the variation in the center-level outcome and this knowledge can help contextualize the trial results and inform practice patterns. Methods: Our symbolic two-step method that applies symbolic data analysis to account for patient-level factors when estimating and testing a center-level effect on both the average center-level outcome and its variation was developed for such settings. Herein, we sought to extend the method to prospectively size a CRT so that the application of our method in data analysis is consistent with the design. Results: Our formulaic approach to sample size planning incorporated predictive factors of the within-center variation and accounted for patient-level characteristics. The sample size approximation performed well in many different pragmatic settings. Conclusions: Our symbolic two-step method provides an alternate approach in the design and analysis of CRTs evaluating novel improvement processes within care delivery research.

Original languageEnglish (US)
Article number100609
JournalContemporary Clinical Trials Communications
Volume19
DOIs
StatePublished - Sep 2020

Keywords

  • Care delivery research
  • Cluster randomized trial
  • Sample size estimation
  • Symbolic data analysis

ASJC Scopus subject areas

  • Pharmacology

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