Methods for Designing Cluster Randomized Trials to Detect Treatment Effect Heterogeneity

February 2023 – In Grand Rounds 34, IMPACT Design and Statistics Executive Committee Member, Dr. Li, sheds light on several methods for designing cluster randomized trials (CRTs) to detect the variability of treatment effects for individuals within a population, also known as heterogeneity of treatment effect (HTE).

Speaker

Fan Li, PhD

Fan Li, PhD

Assistant Professor
Department of Biostatistics

Yale School of Public Health

Learning Objectives

  • Understand the sample size requirements for testing treatment effect heterogeneity in cluster randomized trials.
  • Be aware of tools for designing cluster randomized trials.
  • A call for involving statisticians at the outset to design cluster randomized trials.

 

Plan switching among Medicare Advantage beneficiaries with Alzheimer’s disease and other dementias

Plan switching among Medicare Advantage beneficiaries with Alzheimer’s disease and other dementias

March 23, 2021

A paper presenting results from a study in which researchers used hospital, outpatient, and post‐acute care data to identify MA beneficiaries with and without ADRD in 2014. Multinomial logit models estimated the percentage of people who disenrolled to traditional Medicare (TM) or switched to a different MA plan in 2015. Read the full article here.

A tutorial on sample size calculation for cluster randomised multiple-period parallel, cross-over and stepped-wedge trials using the Shiny CRT Calculator.

A tutorial on sample size calculation for cluster randomised multiple-period parallel, cross-over and stepped-wedge trials using the Shiny CRT Calculator

February 22, 2020

This paper provides a tutorial on sample size calculation for cluster randomized designs–with particular emphasis on designs with multiple periods of measurement–and provides a web-based tool to allow researchers to easily conduct these calculations. Read the full article at this link.