GEEMAEE: A SAS macro for the analysis of correlated outcomes based on GEE and finite-sample adjustments with application to cluster randomized trials

Encouraging Pharmacist Referrals for Evidence-Based Statin Initiation: Two Cluster Randomized Clinical Trials

Importance

Despite statins' benefit in preventing major adverse cardiovascular events, most patients with an indication for statin therapy are not appropriately treated. Clinicians' limited time and lack of systematic efforts to address preventive care likely contribute to gaps in statin prescribing.

Objective

To determine the effect on statin prescribing of 2 interventions to refer appropriate patients to a pharmacist for lipid management.

Design, Setting, and Participants

These 2 pragmatic cluster randomized clinical trials were conducted among 12 total primary care practices in a community health system. Trial 1 was a delayed-intervention design of a visit-based intervention with randomization at the clinician level in a single clinic, and trial 2 was a parallel-arm trial of an asynchronous intervention with randomization at the clinic level in 11 clinics. Patients who were assigned to a primary care clinician at a participating practice, had an indication for a high-intensity or moderate-intensity statin, and were either not prescribed a statin or prescribed an inappropriately low statin dose were eligible for inclusion.

Intervention

Trial 1 tested an interruptive electronic health record alert that appeared during eligible patients' visits and facilitated referral to a pharmacist, while trial 2 tested an order for pharmacist referral placed by the study team for cosignature by the primary care clinician without regard to the timing of a clinic visit.

Main Outcome and Measure

The primary outcome was the proportion of patients prescribed a statin.

Results

Overall, 1412 patients were enrolled in trial 1 and 1950 in trial 2. Across both trials, mean (SD) patient age was 65.6 (9.9) years, and 1485 patients (44.2%) were female. Mean (SD) baseline 10-year risk of major cardiovascular events was 17.9% (9.4). In trial 1, the interruptive alert was not associated with a significant increase in statin prescriptions compared with usual care (15.6% vs 11.6%; unadjusted absolute difference, 3.9 percentage points; 95% CI, -0.4 to 8.3). In trial 2, semiautomated pharmacist referrals were associated with an increase in statin prescriptions by 16 percentage points compared with usual care (31.6% vs 15.2%; unadjusted absolute difference, 16.4 percentage points; 95% CI, 12.7-20.1).

Conclusions and Relevance

In these 2 cluster randomized clinical trials, visit-based interruptive alerts were not associated with a significant increase in statin prescribing compared with usual care, whereas a strategy of asynchronous semiautomated referral for pharmacist comanagement was associated with a substantial increase. This strategy of asynchronous semiautomated referrals for pharmacist involvement in lipid management could be a scalable and effective approach to increasing statin prescribing for patients at high risk.

Trial Registration

ClinicalTrials.gov Identifier: NCT05537064.

Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer's and related dementias

BACKGROUND

Cluster randomized trials (CRTs) are increasingly important for evaluating interventions embedded in health care systems. An essential parameter in sample size calculation to detect both overall and heterogeneous treatment effects for CRTs is the intra-cluster correlation coefficient (ICC) of both outcome and covariates of interest. However, obtaining advance estimates for the ICC can be challenging. When trial outcomes will be obtained from routinely collected data sources, there is an opportunity to obtain reliable ICC estimates in advance of the trial. Using USA national Medicare data, we estimated ICCs for a range of outcomes to inform the design of CRTs for people living with Alzheimer's and related dementias (ADRD).

METHOD

Data from 2018 Medicare Fee-for-Service beneficiaries, specifically, 1,898,812 individuals (≥ 65 years) with diagnosis of ADRD within 3436 hospital service areas (treated as clusters) and 306 hospital referral regions (treated as fixed strata), were used to calculate unadjusted and adjusted ICC estimates for three outcomes: death, any hospitalizations, and any emergency department (ED) visits and three covariates: age, race and sex. We present both overall and stratum-specific ICC estimates. We illustrate their use in sample size calculations for overall treatment effects as well as detecting treatment effect heterogeneity.

RESULTS

The unadjusted overall ICCs for death, hospitalizations, and ED visits were 0.001, 0.010, and 0.017 respectively. Stratum-specific ICCs varied widely across the 306 HRRs: median 0.001, 0.010 and 0.025 for death, hospitalizations, and ED visits respectively and 0.007, 0.001, and 0.080 for age, sex and race. An interactive R Shiny app is provided that allows users to retrieve estimates overlayed on a map of the USA.

CONCLUSIONS

We presented both adjusted and unadjusted ICCs for outcomes as well as unadjusted ICCs for covariates of potential interest from population-level data in the USA and demonstrated how the estimates may be used in sample size calculations for CRTs in ADRD.

Point-of-care C-reactive protein measurement by community health workers safely reduces antimicrobial use among children with respiratory illness in rural Uganda: A stepped wedge cluster randomized trial

BACKGROUND

Acute respiratory illness (ARI) is one of the most common reasons children receive antibiotic treatment. Measurement of C-reaction protein (CRP) has been shown to reduce unnecessary antibiotic use among children with ARI in a range of clinical settings. In many resource-constrained contexts, patients seek care outside the formal health sector, often from lay community health workers (CHW). This study's objective was to determine the impact of CRP measurement on antibiotic use among children presenting with febrile ARI to CHW in Uganda.

METHODS AND FINDINGS

We conducted a cross-sectional, stepped wedge cluster randomized trial in 15 villages in Bugoye subcounty comparing a clinical algorithm that included CRP measurement by CHW to guide antibiotic treatment (STAR Sick Child Job Aid [SCJA]; intervention condition) with the Integrated Community Care Management (iCCM) SCJA currently in use by CHW in the region (control condition). Villages were stratified into 3 strata by altitude, distance to the clinic, and size; in each stratum, the 5 villages were randomly assigned to one of 5 treatment sequences. Children aged 2 months to 5 years presenting to CHW with fever and cough were eligible. CHW conducted follow-up assessments 7 days after the initial visit. Our primary outcome was the proportion of children who were given or prescribed an antibiotic at the initial visit. Our secondary outcomes were (1) persistent fever on day 7; (2) development of prespecified danger signs; (3) unexpected visits to the CHW; (4) hospitalizations; (5) deaths; (6) lack of perceived improvement per the child's caregiver on day 7; and (7) clinical failure, a composite outcome of persistence of fever on day 7, development of danger signs, hospitalization, or death. The 65 participating CHW enrolled 1,280 children, 1,220 (95.3%) of whom had sufficient data. Approximately 48% (587/1,220) and 52% (633/1,220) were enrolled during control (iCCM SCJA) and intervention periods (STAR SCJA), respectively. The observed percentage of children who were given or prescribed antibiotics at the initial visit was 91.8% (539/587) in the control periods as compared to 70.8% (448/633) during the intervention periods (adjusted prevalence difference -24.6%, 95% CI: -36.1%, -13.1%). The odds of antibiotic prescription by the CHW were over 80% lower in the intervention as compared to the control periods (OR 0.18, 95% CI: 0.06, 0.49). The frequency of clinical failure (iCCM SCJA 3.9% (23/585) v. STAR SCJA 1.8% (11/630); OR 0.41, 95% CI: 0.09, 1.83) and lack of perceived improvement by the caregiver (iCCM SCJA 2.1% (12/584) v. STAR SCJA 3.5% (22/627); OR 1.49, 95% CI: 0.37, 6.52) was similar. There were no unexpected visits or deaths in either group within the follow-up period.

CONCLUSIONS

Incorporating CRP measurement into iCCM algorithms for evaluation of children with febrile ARI by CHW in rural Uganda decreased antibiotic use. There is evidence that this decrease was not associated with worse clinical outcomes, although the number of adverse events was low. These findings support expanded access to simple, point-of-care diagnostics to improve antibiotic stewardship in rural, resource-constrained settings where individuals with limited medical training provide a substantial proportion of care.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05294510. The study was reviewed and approved by the University of North Carolina Institutional Review Board (#18-2803), Mbarara University of Science and Technology Research Ethics Committee (14/03-19), and Uganda National Council on Science and Technology (HS 2631).

Analysing cluster randomised controlled trials using GLMM, GEE1, GEE2, and QIF: results from four case studies

BACKGROUND

Using four case studies, we aim to provide practical guidance and recommendations for the analysis of cluster randomised controlled trials.

METHODS

Four modelling approaches (Generalized Linear Mixed Models with parameters estimated by maximum likelihood/restricted maximum likelihood; Generalized Linear Models with parameters estimated by Generalized Estimating Equations (1st order or second order) and Quadratic Inference Function, for analysing correlated individual participant level outcomes in cluster randomised controlled trials were identified after we reviewed the literature. We systematically searched the online bibliography databases of MEDLINE, EMBASE, PsycINFO (via OVID), CINAHL (via EBSCO), and SCOPUS. We identified the above-mentioned four statistical analytical approaches and applied them to four case studies of cluster randomised controlled trials with the number of clusters ranging from 10 to 100, and individual participants ranging from 748 to 9,207. Results were obtained for both continuous and binary outcomes using R and SAS statistical packages.

RESULTS

The intracluster correlation coefficient (ICC) estimates for the case studies were less than 0.05 and are consistent with the observed ICC values commonly reported in primary care and community-based cluster randomised controlled trials. In most cases, the four methods produced similar results. However, in a few analyses, quadratic inference function produced different results compared to the generalized linear mixed model, first-order generalized estimating equations, and second-order generalized estimating equations, especially in trials with small to moderate numbers of clusters.

CONCLUSION

This paper demonstrates the analysis of cluster randomised controlled trials with four modelling approaches. The results obtained were similar in most cases, however, for trials with few clusters we do recommend that the quadratic inference function should be used with caution, and where possible a small sample correction should be used. The generalisability of our results is limited to studies with similar features to our case studies, for example, studies with a similar-sized ICC. It is important to conduct simulation studies to comprehensively evaluate the performance of the four modelling approaches.