Leveraging baseline covariates to analyze small cluster‐randomized trials with a rare binary outcome

Abstract

Cluster‐randomized trials (CRTs) involve randomizing entire groups of participants—called clusters—to treatment arms but are often comprised of a limited or fixed number of available clusters. While covariate adjustment can account for chance imbalances between treatment arms and increase statistical efficiency in individually randomized trials, analytical methods for individual‐level covariate adjustment in small CRTs have received little attention to date. In this paper, we systematically investigate, through extensive simulations, the operating characteristics of propensity score weighting and multivariable regression as two individual‐level covariate adjustment strategies for estimating the participant‐average causal effect in small CRTs with a rare binary outcome and identify scenarios where each adjustment strategy has a relative efficiency advantage over the other to make practical recommendations. We also examine the finite‐sample performance of the bias‐corrected sandwich variance estimators associated with propensity score weighting and multivariable regression for quantifying the uncertainty in estimating the participant‐average treatment effect. To illustrate the methods for individual‐level covariate adjustment, we reanalyze a recent CRT testing a sedation protocol in 31 pediatric intensive care units.

Equity-Centered Postdischarge Support for Medicaid-Insured People: Protocol for a Type 1 Hybrid Effectiveness-Implementation Stepped Wedge Cluster Randomized Controlled Trial

“…Sensitivity analyses may be performed using logistic regression for health service use based on the distribution of the data. We will also examine the stability of effect estimates using generalized estimation equations with small sample adjustment, given the study design [ 55 ].…”

Equity Centered Post Discharge Support for Medicaid Insured: A Type 1 Hybrid Effectiveness Implementation Stepped-Wedge Cluster Randomized Trial Study Protocol For The ‘THRIVE’ Clinical Pathway

“…We will also examine the stability of effect estimates using Generalized Estimation Equations with small-sample adjustment given the study design. 55 Equity Evaluation. Our will consider several equity metrics and include interactions terms to capture racial and ethnic differences, and biological sex for THRIVE participants compared to patients receiving usual care.…”

Key considerations for designing, conducting and analysing a cluster randomized trial

“… 81 Marginal standardization, as an alternative to direct covariate adjustment, can be used to estimate the marginal covariate-adjusted summary measure, as too can inverse probability weighting. 82 , 83 …”

“… 61 , 82 When adjusting for covariates in the context of low or high prevalence binary outcomes, model convergence can be problematic, and in these settings, propensity score approaches might help. 82 , 83 Where covariate data are incomplete, any multiple imputation procedures should appropriately allow for the clustered nature of the design. 86 …”