TY - JOUR
T1 - Sample size calculation for cluster randomized trials with zero-inflated count outcomes
AU - Zhou, Zhengyang
AU - Li, Dateng
AU - Zhang, Song
N1 - Publisher Copyright:
© 2022 John Wiley & Sons Ltd.
PY - 2022/5/30
Y1 - 2022/5/30
N2 - Cluster randomized trials (CRT) have been widely employed in medical and public health research. Many clinical count outcomes, such as the number of falls in nursing homes, exhibit excessive zero values. In the presence of zero inflation, traditional power analysis methods for count data based on Poisson or negative binomial distribution may be inadequate. In this study, we present a sample size method for CRTs with zero-inflated count outcomes. It is developed based on GEE regression directly modeling the marginal mean of a zero-inflated Poisson outcome, which avoids the challenge of testing two intervention effects under traditional modeling approaches. A closed-form sample size formula is derived which properly accounts for zero inflation, ICCs due to clustering, unbalanced randomization, and variability in cluster size. Robust approaches, including t-distribution-based approximation and Jackknife re-sampling variance estimator, are employed to enhance trial properties under small sample sizes. Extensive simulations are conducted to evaluate the performance of the proposed method. An application example is presented in a real clinical trial setting.
AB - Cluster randomized trials (CRT) have been widely employed in medical and public health research. Many clinical count outcomes, such as the number of falls in nursing homes, exhibit excessive zero values. In the presence of zero inflation, traditional power analysis methods for count data based on Poisson or negative binomial distribution may be inadequate. In this study, we present a sample size method for CRTs with zero-inflated count outcomes. It is developed based on GEE regression directly modeling the marginal mean of a zero-inflated Poisson outcome, which avoids the challenge of testing two intervention effects under traditional modeling approaches. A closed-form sample size formula is derived which properly accounts for zero inflation, ICCs due to clustering, unbalanced randomization, and variability in cluster size. Robust approaches, including t-distribution-based approximation and Jackknife re-sampling variance estimator, are employed to enhance trial properties under small sample sizes. Extensive simulations are conducted to evaluate the performance of the proposed method. An application example is presented in a real clinical trial setting.
KW - cluster randomized trials
KW - generalized estimating equation
KW - marginalized models
KW - sample size
KW - zero-inflated outcomes
UR - http://www.scopus.com/inward/record.url?scp=85124559404&partnerID=8YFLogxK
U2 - 10.1002/sim.9350
DO - 10.1002/sim.9350
M3 - Article
C2 - 35139584
AN - SCOPUS:85124559404
SN - 0277-6715
VL - 41
SP - 2191
EP - 2204
JO - Statistics in Medicine
JF - Statistics in Medicine
IS - 12
ER -