TY - JOUR
T1 - Sample size considerations for matched-pair cluster randomization design with incomplete observations of continuous outcomes
AU - Xu, Xiaohan
AU - Zhu, Hong
AU - Ahn, Chul
N1 - Publisher Copyright:
© 2021
PY - 2021/5
Y1 - 2021/5
N2 - Matched-pair cluster randomization design is becoming increasingly used in clinical and health behavioral studies. Investigators often encounter incomplete observations in the data collected. Statistical inference for matched-pair cluster randomization design with incomplete observations has been extensively studied in literature. However, sample size method for such study design is sparsely available. We propose a closed-form sample size formula for matched-pair cluster randomization design with continuous outcomes, based on the generalized estimating equation approach by treating incomplete observations as missing data in a marginal linear model. The sample size formula is flexible to accommodate different correlation structures, missing patterns, and magnitude of missingness. In the presence of missing data, the proposed method would lead to a more accurate sample size estimation than the crude adjustment method. Simulation studies are conducted to evaluate the finite-sample performance of the proposed sample size method under various design configurations. We use bias-corrected variance estimators to address the issue of inflated type I error when the number of clusters per group is small. A real application example of physical fitness study in Ecuadorian adolescents is presented for illustration.
AB - Matched-pair cluster randomization design is becoming increasingly used in clinical and health behavioral studies. Investigators often encounter incomplete observations in the data collected. Statistical inference for matched-pair cluster randomization design with incomplete observations has been extensively studied in literature. However, sample size method for such study design is sparsely available. We propose a closed-form sample size formula for matched-pair cluster randomization design with continuous outcomes, based on the generalized estimating equation approach by treating incomplete observations as missing data in a marginal linear model. The sample size formula is flexible to accommodate different correlation structures, missing patterns, and magnitude of missingness. In the presence of missing data, the proposed method would lead to a more accurate sample size estimation than the crude adjustment method. Simulation studies are conducted to evaluate the finite-sample performance of the proposed sample size method under various design configurations. We use bias-corrected variance estimators to address the issue of inflated type I error when the number of clusters per group is small. A real application example of physical fitness study in Ecuadorian adolescents is presented for illustration.
KW - Continuous outcomes
KW - Generalized estimating equation
KW - Intraclass correlation
KW - Matched-pair cluster design
KW - Sample size
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U2 - 10.1016/j.cct.2021.106336
DO - 10.1016/j.cct.2021.106336
M3 - Article
C2 - 33689919
AN - SCOPUS:85102514290
SN - 1551-7144
VL - 104
JO - Contemporary Clinical Trials
JF - Contemporary Clinical Trials
M1 - 106336
ER -