Sample size considerations for matched-pair cluster randomization design with incomplete observations of continuous outcomes

2021 ◽  
pp. 106336
Author(s):  
Xiaohan Xu ◽  
Hong Zhu ◽  
Chul Ahn
Keyword(s):  
Sample Size ◽  
Matched Pair ◽  
Cluster Randomization ◽  
Incomplete Observations ◽  
Continuous Outcomes

Sample size considerations for paired experimental design with incomplete observations of continuous outcomes

2017 ◽  
Vol 28 (2) ◽  
pp. 589-598
Author(s):  
Hong Zhu ◽  
Xiaohan Xu ◽  
Chul Ahn
Keyword(s):  
Missing Data ◽  
Experimental Design ◽  
Sample Size ◽  
Estimating Equation ◽  
Generalized Estimating Equation ◽  
Size Estimate ◽  
Incomplete Observations ◽  
Continuous Outcomes ◽  
Sample Size Estimate ◽  
Generalized Estimating

Paired experimental design is widely used in clinical and health behavioral studies, where each study unit contributes a pair of observations. Investigators often encounter incomplete observations of paired outcomes in the data collected. Some study units contribute complete pairs of observations, while the others contribute either pre- or post-intervention observations. Statistical inference for paired experimental design with incomplete observations of continuous outcomes has been extensively studied in literature. However, sample size method for such study design is sparsely available. We derive a closed-form sample size formula based on the generalized estimating equation approach by treating the incomplete observations as missing data in a linear model. The proposed method properly accounts for the impact of mixed structure of observed data: a combination of paired and unpaired outcomes. The sample size formula is flexible to accommodate different missing patterns, magnitude of missingness, and correlation parameter values. We demonstrate that under complete observations, the proposed generalized estimating equation sample size estimate is the same as that based on the paired t-test. In the presence of missing data, the proposed method would lead to a more accurate sample size estimate comparing with the crude adjustment. Simulation studies are conducted to evaluate the finite-sample performance of the generalized estimating equation sample size formula. A real application example is presented for illustration.

A comparative study of matched pair designs with two binary endpoints

2016 ◽  
Vol 26 (6) ◽  
pp. 2526-2542
Author(s):  
Yuanyuan Jiang ◽  
Jin Xu
Keyword(s):  
Comparative Study ◽  
Sample Size ◽  
Cancer Chemotherapy ◽  
Adaptive Design ◽  
Sample Size Calculation ◽  
Matched Pair ◽  
Power Approximation

We study matched pair designs with two binary endpoints under three different approaches. Power approximation and sample size calculation are derived under these situations and facilitated by R programs. An adaptive design with sample size re-estimation is also presented. Through extensive simulations, we provide general guidelines for practitioners to choose the best approach according to the ranges of the interested parameters in the sense of feasibility and robustness. Application to a cancer chemotherapy trial is illustrated.

scholarly journals Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes

2018 ◽  
Vol 38 (7) ◽  
pp. 1262-1275 ◽  
Author(s):  
Richard D. Riley ◽  
Kym I.E. Snell ◽  
Joie Ensor ◽  
Danielle L. Burke ◽  
Frank E. Harrell ◽  
...  
Keyword(s):  
Prediction Model ◽  
Sample Size ◽  
Minimum Sample Size ◽  
Continuous Outcomes ◽  
Minimum Sample
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