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

Campbell, Marion K; Mollison, Jill; Steen, Nick; Grimshaw, Jeremy; Eccles, Martin

doi:10.1093/fampra/17.2.192

Cited by 271 publications

(234 citation statements)

References 9 publications

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“…It has been previously noted that ICCs for process variables (nonoutcome variables, ICCs in the range of 0.05-0.15) are generally an order of magnitude higher than those for outcome variables (ICCs generally lower than 0.05) (in implementation trials in primary care) [4]. We chose to explore the impact of a wide range of plausible imputed ICC ranging from 0.01 to 0.5 for trials not reporting an ICC or VIF.…”

Section: Assumptions and Priors Used In The Statistical Analysesmentioning

confidence: 99%

“…However, statistical independence of individuals randomized at a cluster level cannot be assumed [1]. An explanation for the lack of independence of individuals in CRTs is that individuals within a cluster may be more similar to each other than to individuals in other clusters; the similarities within a cluster are represented by the intracluster correlation co-efficient (ICC, rho) [1,4]. As first noted by Cornfield in 1978, cluster randomization results in some loss of statistical efficiency compared to individual randomization and this must be accounted for in the analysis [5].…”

Section: Introductionmentioning

confidence: 99%

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Hip protectors decrease hip fracture risk in elderly nursing home residents: a Bayesian meta-analysis

Sawka

Boulos

Beattie

et al. 2007

Journal of Clinical Epidemiology

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Objective-To design a Bayesian random effects model for pooling binary outcome data from cluster randomized trials (CRTs) with individually randomized trials (IRTs) and then use this model to determine if hip protectors decrease the risk of hip fracture in elderly nursing home residents.Study Design and Setting-Eight electronic databases were searched; abstracts and papers were reviewed in duplicate. Randomized controlled trials of hip protectors in nursing homes were included. The pooled mean odds ratio (OR) of a hip fracture in an individual allocated to hip protectors with 95% credibility interval (CRI) was calculated. CIHR Author Manuscript CIHR Author Manuscript CIHR Author ManuscriptResults-We included four trials of 1,922 individuals (including three CRTs). The pooled OR of an elderly nursing home resident sustaining one or more hip fractures with hip protector allocation was 0.40 (95% CRI 0.25, 0.61). The model was robust in multiple sensitivity analyses assuming alternative intracluster correlation coefficient values. Conclusion-TheBayesian approach may be used in meta-analyses of IRTs and CRTs. Using this approach, we have determined that hip protectors decrease the risk of hip fracture in elderly nursing home residents. Methodologic limitations of the included trials and a possible herd effect in CRTs may have influenced these results.

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Section: Assumptions and Priors Used In The Statistical Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hip protectors decrease hip fracture risk in elderly nursing home residents: a Bayesian meta-analysis

Sawka

Boulos

Beattie

et al. 2007

Journal of Clinical Epidemiology

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“…Multilevel modeling also has the ability to adjust for confounders on several levels (eg, it allows adjustment for variables on both practice level and patient level). 27 The analyses were adjusted for baseline values and for health insurance (which differed between the 2 groups at baseline). 28 The analyses of pain and physical functioning also were adjusted for potential prognostic variables based on the literature (ie, sex, 29,30 duration of the current episode of back pain, 31 previous back pain episode [s], 32 and education 33 ).…”

Section: Discussionmentioning

confidence: 99%

“…The ICC takes a value of between 0 and 1 and would be high if, for example, the outcome of patients within practices is very consistent but there is wide variation across different practices. 27 The sample size was corrected by an ICC of .05, which is an estimate for the correlation of outcome variables in primary care. 27,36 We estimated the cluster size to be 5 patients per physical therapist.…”

Section: Discussionmentioning

confidence: 99%

Implementation of Clinical Guidelines on Physical Therapy for Patients With Low Back Pain: Randomized Trial Comparing Patient Outcomes After a Standard and Active Implementation Strategy

Bekkering¹,

Tulder²,

et al. 2005

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Background and Purpose. An active strategy was developed for the implementation of the clinical guidelines on physical therapy for patients with low back pain. The effect of this strategy on patients' physical functioning, coping strategy, and beliefs regarding their low back pain was studied. Subjects. One hundred thirteen primary care physical therapists treated a total of 500 patients. Methods. The physical therapists were randomly assigned to 1 of 2 groups. The control group received the guidelines by mail (standard passive method of dissemination). The intervention group, in contrast, received an additional active training strategy consisting of 2 sessions with education, group discussion, role playing, feedback, and reminders. Patients with low back pain, treated by the participating therapists, completed questionnaires on physical functioning, pain, sick leave, coping, and beliefs. Results. Physical functioning and pain in the 2 groups improved substantially in the first 12 weeks. Multilevel longitudinal analysis showed no differences between the 2 groups on any outcome measure during follow-up. Discussion and Conclusion. The authors found no additional benefit to applying an active strategy to implement the physical therapy guidelines for patients with low back pain. Active implementation strategies are not recommended if patient outcomes are to be improved.

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“…[35][36][37][38] The sample size calculation was performed with a significance level of 0.05 and 80 % power. Based on prior studies showing clinical inertia in 60 % of hypertension visits, [35][36][37][38][39][40][41] we estimated needing a sample of 200 patient-clinician visits to detect a 33 % improvement in visits with appropriate management. We included a stopping rule in which recruitment would be stopped at the midpoint for futility if the z-score was negative or for efficacy if the z-score was positive and the p value ≤ 0.001.…”

Section: Sample Sizementioning

confidence: 99%

An Electronic Adherence Measurement Intervention to Reduce Clinical Inertia in the Treatment of Uncontrolled Hypertension: The MATCH Cluster Randomized Clinical Trial

et al. 2016

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BACKGROUND: To appropriately manage uncontrolled hypertension, clinicians must decide whether blood pressure (BP) is above goal due to a need for additional medication or to medication nonadherence. Yet, clinicians are poor judges of adherence, and uncertainty about adherence may promote inertia with respect to medication modification. OBJECTIVE: We aimed to determine the effect of sharing electronically-measured adherence data with clinicians on the management of uncontrolled hypertension. DESIGN: This was a cluster randomized trial. PARTICIPANTS: Twenty-four primary care providers (12 intervention, 12 usual care; cluster units) and 100 patients with uncontrolled hypertension (65 intervention, 35 usual care) were included in the study. INTERVENTIONS: At one visit per patient, clinicians in the intervention group received a report summarizing electronically measured adherence to the BP regimen and recommended clinical actions. Clinicians in the control group did not receive a report. MAIN MEASURES: The primary outcome was the proportion of visits with appropriate clinical management (i.e., treatment intensification among adherent patients and adherence counseling among nonadherent patients). Secondary outcomes included patient-rated quality of care and communication during the visit. KEY RESULTS: The proportion of visits with appropriate clinical management was higher in the intervention group than the control group (45 out of 65; 69 %) versus (12 out of 35; 34 %; p = 0.001). A higher proportion of adherent patients in the intervention group had their regimen intensified (p = 0.01), and a higher proportion of nonadherent patients in the intervention group received adherence counseling (p = 0.005). Patients in the intervention group were more likely to give their clinician high ratings on quality of care (p = 0.05), and on measures of patientcentered (p = 0.001) and collaborative communication (p = 0.02).CONCLUSIONS: Providing clinicians with electronicallymeasured antihypertensive adherence reports reduces inertia in the management of uncontrolled hypertension. TRIAL REGISTRATION: NCT01257347; http://clinicaltrials.gov/show/NCT01257347 KEY WORDS: uncontrolled hypertension; medication adherence; clinical inertia; randomized clinical trial.

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

Cited by 271 publications

References 9 publications

Hip protectors decrease hip fracture risk in elderly nursing home residents: a Bayesian meta-analysis

Hip protectors decrease hip fracture risk in elderly nursing home residents: a Bayesian meta-analysis

Implementation of Clinical Guidelines on Physical Therapy for Patients With Low Back Pain: Randomized Trial Comparing Patient Outcomes After a Standard and Active Implementation Strategy

An Electronic Adherence Measurement Intervention to Reduce Clinical Inertia in the Treatment of Uncontrolled Hypertension: The MATCH Cluster Randomized Clinical Trial

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