Use of clustering analysis in randomized controlled trials in orthopaedic surgery

Abstract

Background: The effects of clustering in randomized controlled trials (RCTs) and the resulting potential violation of assumptions of independence are now well recognized. When patients in a single study are treated by several therapists, there is good reason to suspect that the variation in outcome will be smaller for patients treated in the same group than for patients treated in different groups. This potential correlation of outcomes results in a loss of independence of observations. The purpose of this study is to examine the current use of clustering analysis in RCTs published in the top five journals of orthopaedic surgery. Methods: RCTs published from 2006 to 2010 in the top five journals of orthopaedic surgery, as determined by 5-year impact factor, that included multiple therapists and/or centers were included. Identified articles were assessed for accounting for the effects of clustering of therapists and/or centers in randomization or analysis. Logistic regression used both univariate and multivariate models, with use of clustering analysis as the outcome. Multivariate models were constructed using stepwise deletion. An alpha level of 0.10 was considered significant. Results: A total of 271 articles classified as RCTs were identified from the five journals included in the study. Thirty-two articles were excluded due to inclusion of nonhuman subjects. Of the remaining 239 articles, 186 were found to include multiple centers and/or therapists. The prevalence of use of clustering analysis was 21.5%. Fewer than half of the studies reported inclusion of a statistician, epidemiologist or clinical trials methodologist on the team. In multivariate modeling, adjusting for clustering was associated with a 6.7 times higher odds of inclusion of any type of specialist on the team (P∈=∈0.08). Likewise, trials that accounted for clustering had 3.3 times the odds of including an epidemiologist/clinical trials methodologist than those that did not account for clustering (P∈=∈0.04). Conclusions: Including specialists on a study team, especially an epidemiologist or clinical trials methodologist, appears to be important in the decision to account for clustering in RCT reporting. The use of clustering analysis remains an important piece of unbiased reporting, and accounting for clustering in RCTs should be a standard reporting practice.