Weight loss in primary care: A pooled analysis of two pragmatic cluster‐randomized trials

Obesity ◽  
2021 ◽  
Author(s):  
Peter T. Katzmarzyk ◽  
John W. Apolzan ◽  
Byron Gajewski ◽  
William D. Johnson ◽  
Corby K. Martin ◽  
...  
Keyword(s):  
Primary Care ◽  
Weight Loss ◽  
Randomized Trials ◽  
Pooled Analysis ◽  
Cluster Randomized Trials ◽  
Cluster Randomized

scholarly journals Analysis of cluster randomized trials in primary care: a practical approach

2000 ◽  
Vol 17 (2) ◽  
pp. 192-196 ◽  
Author(s):  
M. K Campbell ◽  
J. Mollison ◽  
N. Steen ◽  
J. M Grimshaw ◽  
M. Eccles
Keyword(s):  
Primary Care ◽  
Randomized Trials ◽  
Practical Approach ◽  
Cluster Randomized Trials ◽  
Cluster Randomized

Lessons for cluster randomized trials in the twenty-first century: a systematic review of trials in primary care

2004 ◽  
Vol 1 (1) ◽  
pp. 80-90 ◽  
Author(s):  
Sandra M Eldridge ◽  
Deborah Ashby ◽  
Gene S Feder ◽  
Alicja R Rudnicka ◽  
Obioha C Ukoumunne
Keyword(s):  
Systematic Review ◽  
Primary Care ◽  
Randomized Trials ◽  
First Century ◽  
Cluster Randomized Trials ◽  
Twenty First Century ◽  
Cluster Randomized

Implementation Research on Shared Decision Making in Primary Care: Inventory of Intracluster Correlation Coefficients

2019 ◽  
Vol 39 (6) ◽  
pp. 661-672
Author(s):  
Ali Ben Charif ◽  
Jordie Croteau ◽  
Rhéda Adekpedjou ◽  
Hervé Tchala Vignon Zomahoun ◽  
Evehouenou Lionel Adisso ◽  
...  
Keyword(s):  
Primary Care ◽  
Decision Making ◽  
Shared Decision Making ◽  
Randomized Trials ◽  
Correlation Coefficients ◽  
Shared Decision ◽  
Cluster Randomized Trials ◽  
Intracluster Correlation ◽  
Convenience Sample ◽  
Cluster Randomized

Background. Cluster randomized trials are important sources of information on evidence-based practices in primary care. However, there are few sources of intracluster correlation coefficients (ICCs) for designing such trials. We inventoried ICC estimates for shared decision-making (SDM) measures in primary care. Methods. Data sources were studies led by the Canada Research Chair in Shared Decision Making and Knowledge Transition. Eligible studies were conducted in primary care, included at least 2 hierarchical levels, included SDM measures for individual units nested under any type of cluster (area, clinic, or provider), and were approved by an ethics committee. We classified measures into decision antecedents, decision processes, and decision outcomes. We used Bayesian random-effect models to estimate mode ICCs and the 95% highest probability density interval (HPDI). We summarized estimates by calculating median and interquartile range (IQR). Results. Six of 14 studies were included. There were 97 ICC estimates for 17 measures. ICC estimates ranged from 0 to 0.5 (median, 0.03; IRQ, 0–0.07). They were higher for process measures (median, 0.03; IQR, 0–0.07) than for antecedent measures (0.02; 0–0.07) or outcome measures (0.02; 0–0.06), for which, respectively, “decisional conflict” (mode, 0.48; 95% HPDI, 0.39–0.57), “reluctance to disclose uncertainty to patients” (0.5; 0.11–0.89), and “quality of the decision” (0.45; 0.14–0.84) had the highest ICCs. ICCs for provider-level clustering (median, 0.06; IQR, 0–0.13) were higher than for other levels. Limitations. This convenience sample of studies may not reflect all potential ICC ranges for primary care SDM measures. Conclusions. Our inventory of ICC estimates for SDM measures in primary care will improve the ease and accuracy of power calculations in cluster randomized trials and inspire its further expansion in SDM contexts.

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