Patterns of intra-cluster correlation from primary care research to inform study design and analysis

Adams, Geoffrey; Gulliford, Martin; Ukoumunne, Obioha C; Eldridge, Sandra; Chinn, Susan; Campbell, Mike

doi:10.1016/j.jclinepi.2003.12.013

Cited by 484 publications

(437 citation statements)

References 52 publications

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“…The intraclass correlation coeffi cient of 0.14 is higher than 95% of those from a survey of 1,039 intraclass correlation coeffi cients from 31 studies in primary care. 27 Evidence for the construct validity of the continuityof-care measure is provided by the results of the factor analysis, which generally support the proposed factorial structure of the measure. The criterion validity of the measure is supported by the policy-relevant fi ndings that measured experienced continuity of care was substantially higher for patients who received some diabetes care from their family practice, especially if there was a designated doctor for diabetes care.…”

Section: Discussionmentioning

confidence: 65%

Measuring Continuity of Care in Diabetes Mellitus: An Experience-Based Measure

Gulliford¹,

Naithani²,

Morgan

2006

The Annals of Family Medicine

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PURPOSE Continuity is an important attribute of health care, but appropriate measures are not currently available. We developed an experience-based measure of continuity of care in type 2 diabetes.METHODS A 19-item measure of experienced continuity of care for diabetes mellitus (ECC-DM) was developed from qualitative patient interview data with 4 continuity subdomains: longitudinal, fl exible, relational, and team and crossboundary continuity. The measure was implemented in a survey of 193 patients with type 2 diabetes from 19 family practices. Associations of ECC-DM scores with clinician organizational characteristics were estimated.RESULTS Potential ECC-DM scores ranged from 0 to 100 with an observed mean of 62.1 (SD 16.0). The average inter-item correlation was 0.343 and Cronbach's α was 0.908. Factor analysis found 4 factors that were generally consistent with the proposed subdomains. Patients' mean scores varied signifi cantly between practices (P = .001), ranging from 46 to 78 at different family practices. Experienced continuity was lower for patients receiving only hospital clinic care than for those receiving some diabetes care from their family practice (difference 13.7; 95% confi dence interval [CI], 8.2-19.2; P <.001). Patients had higher ECC-DM scores if their family practice had a designated lead doctor for diabetes (difference 8.2; 95% CI, 2.7-13.6; P = .003). CONCLUSIONSThe results provide evidence for the reliability, construct validity, and criterion validity of the experienced continuity-of-care measure. The measure may be used in research and monitoring to evaluate patient-centered outcomes of diabetes care. Patients' experiences of continuity of care vary between health care organizations and are infl uenced by the organizational arrangements for care.

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Section: Discussionmentioning

confidence: 65%

Measuring Continuity of Care in Diabetes Mellitus: An Experience-Based Measure

Gulliford¹,

Naithani²,

Morgan

2006

The Annals of Family Medicine

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“…12 An assumed intra-class correlation co-efficient of 0.03 was identified as appropriate from a published source. 13 This was used to derive a design effect estimate for each study based on its mean cluster size, and the numerator and denominator values for each trial arm were divided by this factor. The pooled odds ratio was then re-estimated to account for clustering.…”

Section: Discussionmentioning

confidence: 99%

Changing Clinical Practice Through Patient Specific Reminders Available at the Time of the Clinical Encounter: Systematic Review and Meta-Analysis

2012

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OBJECTIVE: To synthesise current evidence for the influence on clinical behaviour of patient-specific electronically generated reminders available at the time of the clinical encounter. DATA SOURCES: PubMed, Cochrane library of systematic reviews; Science Citation Index Expanded; Social Sciences Citation Index; ASSIA; EMBASE; CINAHL; DARE; HMIC were searched for relevant articles. STUDY ELIGIBILITY CRITERIA, PARTICIPANTS AND INTERVENTIONS: We included controlled trials of reminder interventions if the intervention was: directed at clinician behaviour; available during the clinical encounter; computer generated (including computer generated paper-based reminders); and generated by patient-specific (rather than condition specific or drug specific) data. STUDY APPRAISAL AND SYNTHESIS METHODS:Systematic review and meta-analysis of controlled trials published since 1970. A random effects model was used to derive a pooled odds ratio for adherence to recommended care or achievement of target outcome. Subgroups were examined based on area of care and study design. Odds ratios were derived for each sub-group. We examined the designs, settings and other features of reminders looking for factors associated with a consistent effect. RESULTS: Altogether, 42 papers met the inclusion criteria. The studies were of variable quality and some were affected by unit of analysis errors due to a failure to account for clustering. An overall odds ratio of 1.79 [95% confidence interval 1.56, 2.05] in favour of reminders was derived. Heterogeneity was high and factors predicting effect size were difficult to identify. LIMITATIONS: Methodological diversity added to statistical heterogeneity as an obstacle to meta-analysis. The quality of included studies was variable and in some reports procedural details were lacking. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS:The analysis suggests a moderate effect of electronically generated, individually tailored reminders on clinician behaviour during the clinical encounter. Future research should concentrate on identifying the features of reminder interventions most likely to result in the target behaviour.

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“…In order to support the researcher in carefully planning an experimental trial, realistic estimates of the error variance or estimates of the intra-cluster and intra-subject correlation are required. Concerning cluster randomized trials without crossover Murray and Short (1997), Murray et al (2001), Campbell et al (2001), Murray et al (2002), Smeeth and Siu-Woon (2002) and Adams et al (2004) provide estimates of the intra-cluster correlation for different areas of research. Unfortunately, these estimates are not available for cluster randomized crossover trials.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

The Design of Cluster Randomized Crossover Trials

Rietbergen¹,

Moerbeek

2011

Journal of Educational and Behavioral Statistics

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The inefficiency induced by between-cluster variation in cluster randomized trials can be reduced by implementing a crossover design. In a simple crossover trial each subject receives each treatment in random order. A powerful characteristic of this design is that each subject serves as its own control. In a cluster randomized crossover trial clusters of subjects are randomly allocated to a sequence of interventions. Under this design each subject is either included in only one of the treatment periods (crossover at cluster level) or in both periods (crossover at subject level). In this study the efficiency of both cluster randomized crossover trials relative to the cluster randomized trial without crossover is demonstrated. Furthermore, the optimal allocation of clusters and subjects given a fixed budget or desired power level is discussed.

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Patterns of intra-cluster correlation from primary care research to inform study design and analysis

Cited by 484 publications

References 52 publications

Measuring Continuity of Care in Diabetes Mellitus: An Experience-Based Measure

Measuring Continuity of Care in Diabetes Mellitus: An Experience-Based Measure

Changing Clinical Practice Through Patient Specific Reminders Available at the Time of the Clinical Encounter: Systematic Review and Meta-Analysis

The Design of Cluster Randomized Crossover Trials

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