Effectiveness of an mHealth Intervention to Increase Adherence to Triage of HPV DNA Positive Women Who Have Performed Self-Collection (The ATICA Study): A Hybrid Type I Cluster Randomized Effectiveness-Implementation Trial

Abstract Background The most efficient procedures to engage and guide healthcare professionals in collaborative processes that seek to optimize practice are unknown. The PREDIAPS project aims to assess the effectiveness and feasibility of different procedures to perform a facilitated interprofessional collaborative process to optimize type 2 diabetes prevention in routine primary care. Methods A type II hybrid cluster randomized implementation trial was conducted in nine primary care centers of the Basque Health Service. All centers received training on effective healthy lifestyle promotion. Headed by a local leader and an external facilitator, centers conducted a collaborative structured process—the PVS-PREDIAPS implementation strategy—to adapt the intervention and its implementation to their specific context. The centers were randomly allocated to one of two groups: one group applied the implementation strategy globally, promoting the cooperation of all health professionals from the beginning, and the other performed it sequentially, centered first on nurses, who later sought the pragmatic cooperation of physicians. The following patients were eligible for inclusion: all those aged ≥ 30 years old with at least one known cardiovascular risk factor and an impaired fasting glucose level (≥ 110-125 mg/dl) but without diabetes who attended centers during the study period. The main outcome measures concerned changes in type 2 diabetes prevention practice indicators after 12 months. Results After 12 months, 3273 eligible patients at risk of type 2 diabetes had attended their family physician at least once, and of these, 490 (15%) have been addressed by assessing their healthy lifestyles in both comparison groups. The proportion of at-risk patients receiving a personalized prescription of lifestyle change was slightly higher (8.6%; range 13.5-5.9% vs 6.8%; range 7.2-5.8%) and 2.3 times more likely (95% CI for adjusted hazard ratio, 1.38-3.94) in the sequential than in the global centers, after 8 months of the intervention program implementation period. The probability of meeting the recommended levels of physical activity and fruit and vegetable intake were four- and threefold higher after the prescription of lifestyle change than only assessment and provision of advice. The procedure of engagement in and execution of the implementation strategy does not modify the effect of prescribing healthy habits (p interaction component of intervention by group, p > 0.05). Discussion Our results show that the PVS-PREDIAPS implementation strategy manages to integrate interventions with proven efficacy in the prevention of type 2 diabetes in clinical practice in primary care. Further, they suggest that implementation outcomes were somewhat better with a sequential facilitated collaborative process focused on enhancing the autonomy and responsibility of nurses who subsequently seek a pragmatic cooperation of GPs. Trial registration Clinicaltrials.gov identifier: NCT03254979. Registered 16 August 2017—retrospectively registered.

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xtgeebcv: A command for bias-corrected sandwich variance estimation for GEE analyses of cluster randomized trials

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x20931001 ◽

2020 ◽

Vol 20 (2) ◽

pp. 363-381 ◽

Cited By ~ 4

Author(s):

John A. Gallis ◽

Fan Li ◽

Elizabeth L. Turner

Keyword(s):

Randomized Trials ◽

Variance Estimation ◽

Public Health Education ◽

Standard Errors ◽

Future Research ◽

Type I ◽

Cluster Randomized Trials ◽

Finite Sample ◽

Individual Level ◽

Cluster Randomized

Cluster randomized trials, where clusters (for example, schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must consider that outcomes for members of the same cluster tend to be more similar than outcomes for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (for example, 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. Some bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, but none has yet been implemented in Stata. In this article, we describe several popular bias corrections to the robust sandwich variance. We then introduce our newly created command, xtgeebcv, which will allow Stata users to easily apply finite-sample corrections to standard errors obtained from GEE models. We then provide examples to demonstrate the use of xtgeebcv. Finally, we discuss suggestions about which finite-sample corrections to use in which situations and consider areas of future research that may improve xtgeebcv.

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Design and analysis of a 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria: Small sample considerations for cluster-randomized trials with count data

Clinical Trials ◽

10.1177/17407745211028581 ◽

2021 ◽

pp. 174077452110285

Author(s):

Conner L Jackson ◽

Kathryn Colborn ◽

Dexiang Gao ◽

Sangeeta Rao ◽

Hannah C Slater ◽

...

Keyword(s):

Randomized Trial ◽

Cluster Randomized Trial ◽

Mixed Effects ◽

Small Sample ◽

Mixed Effects Models ◽

Type I ◽

Cluster Randomized ◽

Cluster Level ◽

Generalized Estimating

Background: Cluster-randomized trials allow for the evaluation of a community-level or group-/cluster-level intervention. For studies that require a cluster-randomized trial design to evaluate cluster-level interventions aimed at controlling vector-borne diseases, it may be difficult to assess a large number of clusters while performing the additional work needed to monitor participants, vectors, and environmental factors associated with the disease. One such example of a cluster-randomized trial with few clusters was the “efficacy and risk of harms of repeated ivermectin mass drug administrations for control of malaria” trial. Although previous work has provided recommendations for analyzing trials like repeated ivermectin mass drug administrations for control of malaria, additional evaluation of the multiple approaches for analysis is needed for study designs with count outcomes. Methods: Using a simulation study, we applied three analysis frameworks to three cluster-randomized trial designs (single-year, 2-year parallel, and 2-year crossover) in the context of a 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria. Mixed-effects models, generalized estimating equations, and cluster-level analyses were evaluated. Additional 2-year parallel designs with different numbers of clusters and different cluster correlations were also explored. Results: Mixed-effects models with a small sample correction and unweighted cluster-level summaries yielded both high power and control of the Type I error rate. Generalized estimating equation approaches that utilized small sample corrections controlled the Type I error rate but did not confer greater power when compared to a mixed model approach with small sample correction. The crossover design generally yielded higher power relative to the parallel equivalent. Differences in power between analysis methods became less pronounced as the number of clusters increased. The strength of within-cluster correlation impacted the relative differences in power. Conclusion: Regardless of study design, cluster-level analyses as well as individual-level analyses like mixed-effects models or generalized estimating equations with small sample size corrections can both provide reliable results in small cluster settings. For 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria, we recommend a mixed-effects model with a pseudo-likelihood approximation method and Kenward–Roger correction. Similarly designed studies with small sample sizes and count outcomes should consider adjustments for small sample sizes when using a mixed-effects model or generalized estimating equation for analysis. Although the 2-year parallel follow-up of repeated ivermectin mass drug administrations for control of malaria is already underway as a parallel trial, applying the simulation parameters to a crossover design yielded improved power, suggesting that crossover designs may be valuable in settings where the number of available clusters is limited. Finally, the sensitivity of the analysis approach to the strength of within-cluster correlation should be carefully considered when selecting the primary analysis for a cluster-randomized trial.

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