Sample size considerations for matched‐pair cluster randomization design with incomplete observations of binary outcomes

2021 ◽  
Author(s):  
Xiaohan Xu ◽  
Hong Zhu ◽  
Anh Q. Hoang ◽  
Chul Ahn
Keyword(s):  
Sample Size ◽  
Matched Pair ◽  
Binary Outcomes ◽  
Cluster Randomization ◽  
Incomplete Observations

Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes

2021 ◽  
pp. 174077452110101
Author(s):  
Jennifer Proper ◽  
John Connett ◽  
Thomas Murray
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Error Rate ◽  
Adaptive Design ◽  
Type I Error ◽  
Probability Model ◽  
Binary Outcomes ◽  
Type I ◽  
Operating Characteristics ◽  
Type I Error Rate

Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. Methods: A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. Results: The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. Conclusion: Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.

A comparison of methods for sample size estimation for non-inferiority studies with binary outcomes

2010 ◽  
Vol 20 (6) ◽  
pp. 595-612 ◽  
Author(s):  
Steven A Julious ◽  
Roger J Owen
Keyword(s):  
Sample Size ◽  
Bayesian Methods ◽  
Sensitivity Analyses ◽  
Binary Outcomes ◽  
Sample Size Estimation ◽  
Controlled Trials ◽  
Comparison Of Methods ◽  
Size Estimation ◽  
Control Response ◽  
Established Treatment

Non-inferiority trials are motivated in the context of clinical research where a proven active treatment exists and placebo-controlled trials are no longer acceptable for ethical reasons. Instead, active-controlled trials are conducted where a treatment is compared to an established treatment with the objective of demonstrating that it is non-inferior to this treatment. We review and compare the methodologies for calculating sample sizes and suggest appropriate methods to use. We demonstrate how the simplest method of using the anticipated response is predominantly consistent with simulations. In the context of trials with binary outcomes with expected high proportions of positive responses, we show how the sample size is quite sensitive to assumptions about the control response. We recommend when designing such a study that sensitivity analyses be performed with respect to the underlying assumptions and that the Bayesian methods described in this article be adopted to assess sample size.

scholarly journals A new dependence parameter approach to improve the design of cluster randomized trials with binary outcomes

2011 ◽  
Vol 8 (6) ◽  
pp. 687-698 ◽  
Author(s):  
Catherine M Crespi ◽  
Weng Kee Wong ◽  
Sheng Wu
Keyword(s):  
Sample Size ◽  
Randomized Trials ◽  
Pearson Correlation ◽  
Sample Size Determination ◽  
Binary Outcomes ◽  
Alternative Measure ◽  
Cluster Randomized Trials ◽  
Sample Size Calculations ◽  
Cluster Randomized ◽  
Measure Of Dependence

Background and Purpose Power and sample size calculations for cluster randomized trials require prediction of the degree of correlation that will be realized among outcomes of participants in the same cluster. This correlation is typically quantified as the intraclass correlation coefficient (ICC), defined as the Pearson correlation between two members of the same cluster or proportion of the total variance attributable to variance between clusters. It is widely known but perhaps not fully appreciated that for binary outcomes, the ICC is a function of outcome prevalence. Hence, the ICC and the outcome prevalence are intrinsically related, making the ICC poorly generalizable across study conditions and between studies with different outcome prevalences. Methods We use a simple parametrization of the ICC that aims to isolate that part of the ICC that measures dependence among responses within a cluster from the outcome prevalence. We incorporate this parametrization into sample size calculations for cluster randomized trials and compare our method to the traditional approach using the ICC. Results Our dependence parameter, R, may be less influenced by outcome prevalence and has an intuitive meaning that facilitates interpretation. Estimates of R from previous studies can be obtained using simple statistics. Comparison of methods showed that the traditional ICC approach to sample size determination tends to overpower studies under many scenarios, calling for more clusters than truly required. Limitations The methods are developed for equal-sized clusters, whereas cluster size may vary in practice. Conclusions The dependence parameter R is an alternative measure of dependence among binary outcomes in cluster randomized trials that has a number of advantages over the ICC.

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 Sample size calculations for time-averaged difference of longitudinal binary outcomes

2016 ◽  
Vol 46 (1) ◽  
pp. 344-353 ◽  
Author(s):  
Ying Lou ◽  
Jing Cao ◽  
Song Zhang ◽  
Chul Ahn
Keyword(s):  
Sample Size ◽  
Binary Outcomes ◽  
Sample Size Calculations

scholarly journals Sample size requirements for pilot randomised controlled trials with binary outcomes: a simulation study

Trials ◽  
2013 ◽  
Vol 14 (Suppl 1) ◽  
pp. O21
Author(s):  
Dawn Teare ◽  
Munya Dimairo ◽  
Alexandra Hayman ◽  
Neil Shephard ◽  
Amy Whitehead ◽  
...  
Keyword(s):  
Sample Size ◽  
Simulation Study ◽  
Randomised Controlled Trials ◽  
Binary Outcomes ◽  
Controlled Trials ◽  
Randomised Controlled
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