Sample size calculations are poorly conducted and reported in many randomized trials of hip and knee osteoarthritis: results of a systematic review

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.

Download Full-text

Causal link between peak knee adduction moment during walking and structural progression of knee osteoarthritis: a systematic review of cohorts and randomized trials

Osteoarthritis and Cartilage ◽

10.1016/j.joca.2013.02.186 ◽

2013 ◽

Vol 21 ◽

pp. S86-S87

Author(s):

M. Henriksen ◽

M. Creaby ◽

H. Lund ◽

C. Juhl ◽

R. Christensen

Keyword(s):

Systematic Review ◽

Knee Osteoarthritis ◽

Randomized Trials ◽

Causal Link ◽

Knee Adduction Moment ◽

Structural Progression ◽

Peak Knee

Download Full-text

A systematic review of the reporting of sample size calculations and corresponding data components in observational functional magnetic resonance imaging studies

NeuroImage ◽

10.1016/j.neuroimage.2013.08.012 ◽

2014 ◽

Vol 86 ◽

pp. 172-181 ◽

Cited By ~ 15

Author(s):

Qing Guo ◽

Lehana Thabane ◽

Geoffrey Hall ◽

Margaret McKinnon ◽

Ron Goeree ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Systematic Review ◽

Magnetic Resonance ◽

Functional Magnetic Resonance Imaging ◽

Sample Size ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Imaging Studies ◽

Sample Size Calculations

Download Full-text

Sample Size Calculations for Cluster Randomized Trials

Sample Size Calculations in Clinical Research: Third Edition ◽

10.1201/9781315183084-15 ◽

2017 ◽

pp. 337-348

Author(s):

Shein-Chung Chow ◽

Jun Shao ◽

Hansheng Wang ◽

Yuliya Lokhnygina

Keyword(s):

Sample Size ◽

Randomized Trials ◽

Cluster Randomized Trials ◽

Sample Size Calculations ◽

Cluster Randomized

Download Full-text

A systematic review of the “promising zone” design

Trials ◽

10.1186/s13063-020-04931-w ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Julia M. Edwards ◽

Stephen J. Walters ◽

Cornelia Kunz ◽

Steven A. Julious

Keyword(s):

Systematic Review ◽

Sample Size ◽

Interim Analysis ◽

Practical Implementation ◽

Ethical Concerns ◽

Target Sample Size ◽

Maximum Sample Size ◽

Sample Size Calculations ◽

Original Target ◽

Design Simplicity

Abstract Introduction Sample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary. The “promising zone” (Mehta and Pocock, Stat Med 30:3267–3284, 2011) concept is appealing to researchers for its design simplicity. However, it is still relatively new in the application and has been a source of controversy. Objectives This research aims to synthesise current approaches and practical implementation of the promising zone design. Methods This systematic review comprehensively identifies the reporting of methodological research and of clinical trials using promising zone. Databases were searched according to a pre-specified search strategy, and pearl growing techniques implemented. Results The combined search methods resulted in 270 unique records identified; 171 were included in the review, of which 30 were trials. The median time to the interim analysis was 60% of the original target sample size (IQR 41–73%). Of the 15 completed trials, 7 increased their sample size. Only 21 studies reported the maximum sample size that would be considered, for which the median increase was 50% (IQR 35–100%). Conclusions Promising zone is being implemented in a range of trials worldwide, albeit in low numbers. Identifying trials using promising zone was difficult due to the lack of reporting of SSR methodology. Even when SSR methodology was reported, some had key interim analysis details missing, and only eight papers provided promising zone ranges.

Download Full-text