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Optimal allocation of subjects in a matched pair cluster-randomized trial with fixed number of heterogeneous clusters.

ABSTRACT: In cluster-randomized trials, investigators randomize clusters of individuals such as households, medical practices, schools or classrooms despite the unit of interest are the individuals. It results in the loss of efficiency in terms of the estimation of the unknown parameters as well as the power of the test for testing the treatment effects. To recoup this efficiency loss, some studies pair similar clusters and randomize treatment within pairs. However, the clusters within a treatment arm might be heterogeneous in nature. In this article, we propose a locally optimal design that accounts the clusters heterogeneity and optimally allocates the subjects within each cluster. To address the dependency of design on the unknown parameters, we also discuss Bayesian optimal designs. Performances of proposed designs are investigated numerically through some data examples.

SUBMITTER: Singh SP

PROVIDER: S-EPMC9097976 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Optimal allocation of subjects in a matched pair cluster-randomized trial with fixed number of heterogeneous clusters.

Singh Satya Prakash SP Yadav Pradeep P

Journal of applied statistics 20200612 9

In cluster-randomized trials, investigators randomize clusters of individuals such as households, medical practices, schools or classrooms despite the unit of interest are the individuals. It results in the loss of efficiency in terms of the estimation of the unknown parameters as well as the power of the test for testing the treatment effects. To recoup this efficiency loss, some studies pair similar clusters and randomize treatment within pairs. However, the clusters within a treatment arm mig ...[more]

PMID: 35706575

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Project description:BackgroundPatients at risk for generating high health care expenditures often receive fragmented, low-quality, inefficient health care. Guided Care is designed to provide proactive, coordinated, comprehensive care for such patients.ObjectiveWe hypothesized that Guided Care, compared to usual care, produces better functional health and quality of care, while reducing the use of expensive health services.Design32-month, single-blind, matched-pair, cluster-randomized controlled trial of Guided Care, conducted in eight community-based primary care practices.PatientsThe "Hierarchical Condition Category" (HCC) predictive model was used to identify high-risk older patients who were insured by fee-for-service Medicare, a Medicare Advantage plan or Tricare. Patients with HCC scores in the highest quartile (at risk for generating high health care expenditures during the coming year) were eligible to participate.InterventionA registered nurse collaborated with two to five primary care physicians in providing eight services to participants: comprehensive assessment, evidence-based care planning, proactive monitoring, care coordination, transitional care, coaching for self-management, caregiver support, and access to community-based services.Main measuresFunctional health was measured using the Short Form-36. Quality of care and health services utilization were measured using the Patient Assessment of Chronic Illness Care and health insurance claims, respectively.Key resultsOf the eligible patients, 904 (37.8 %) gave written consent to participate; of these, 477 (52.8 %) completed the final interview, and 848 (93.8 %) provided complete claims data. In intention-to-treat analyses, Guided Care did not significantly improve participants' functional health, but it was associated with significantly higher participant ratings of the quality of care (difference = 0.27, 95 % CI = 0.08-0.45) and 29 % lower use of home care (95 % CI = 3-48 %).ConclusionsGuided Care improves high-risk older patients' ratings of the quality of their care, and it reduces their use of home care, but it does not appear to improve their functional health.

Dataset Information

Optimal allocation of subjects in a matched pair cluster-randomized trial with fixed number of heterogeneous clusters.

Publications

Optimal allocation of subjects in a matched pair cluster-randomized trial with fixed number of heterogeneous clusters.

Similar Datasets

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