2020
DOI: 10.1186/s12874-020-00971-7
|View full text |Cite
|
Sign up to set email alerts
|

Clustering of continuous and binary outcomes at the general practice level in individually randomised studies in primary care - a review of 10 years of primary care trials

Abstract: Background: In randomised controlled trials, the assumption of independence of individual observations is fundamental to the design, analysis and interpretation of studies. However, in individually randomised trials in primary care, this assumption may be violated because patients are naturally clustered within primary care practices. Ignoring clustering may lead to a loss of power or, in some cases, type I error. Methods: Clustering can be quantified by intra-cluster correlation (ICC), a measure of the simila… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
4
0

Year Published

2022
2022
2023
2023

Publication Types

Select...
4

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(5 citation statements)
references
References 41 publications
0
4
0
Order By: Relevance
“…With an assumed drop-out rate of 10%, a sample size of 567 participants from 24 medical practices (α = 0.0125, power = 80%) is required to detect differences between both intervention groups of low to moderate effect size ( f = 0.175). To account for the effects of clustering, an intraclass correlation (ICC) of 0.02 is considered, estimated based on previous studies for cRCTs in primary care [ 26 , 27 ]. We consider this ICC to be realistic in our trial as the participating practices are similarly structured (professional background, qualification level, treating patient group size and region) and were trained by the research team.…”
Section: Methodsmentioning
confidence: 99%
“…With an assumed drop-out rate of 10%, a sample size of 567 participants from 24 medical practices (α = 0.0125, power = 80%) is required to detect differences between both intervention groups of low to moderate effect size ( f = 0.175). To account for the effects of clustering, an intraclass correlation (ICC) of 0.02 is considered, estimated based on previous studies for cRCTs in primary care [ 26 , 27 ]. We consider this ICC to be realistic in our trial as the participating practices are similarly structured (professional background, qualification level, treating patient group size and region) and were trained by the research team.…”
Section: Methodsmentioning
confidence: 99%
“…We have assumed an ICC of 0.045 based on 145 ICCs from cluster randomised trials in primary care. 55 This sample size will also provide at least 80% power to detect changes in secondary outcomes, including a minimum difference of 10% for pain (1 point on the 0-10 Numerical Rating Scale; assuming SD of 1.9), 50 and 10% on the 24-item RMDQ (assuming SD of 5.4). 50 This sample size will also provide >80% power to detect a difference of 20% in the proportion of people who continue to use opioids at 1 year and 20% reduction in the proportion of dispensations for strong, long-acting opioids.…”
Section: Sample Sizementioning
confidence: 99%
“…43,44 As a consequence, studies might be underpowered or estimated treatment effects invalid. 12,4547 The report of methodological and statistical aspects of the within-patient RCT design, especially regarding the correlation of outcomes within subjects, can further demonstrate its value and assist the set-up of future studies. Another limitation of several previous within-patient controlled trials is the lack of randomization, which unnecessarily limited their level of evidence.…”
Section: Quantitative Analyses Of Within-patient Rct In Posterolatera...mentioning
confidence: 99%