Impact of complex, partially nested clustering in a three-arm individually randomized group treatment trial: A case study with the wHOPE trial

Abstract

Background/Aims When participants in individually randomized group treatment trials are treated by multiple clinicians or in multiple group treatment sessions throughout the trial, this induces partially nested clusters which can affect the power of a trial. We investigate this issue in the Whole Health Options and Pain Education trial, a three-arm pragmatic, individually randomized clinical trial. We evaluate whether partial clusters due to multiple visits delivered by different clinicians in the Whole Health Team arm and dynamic participant groups due to changing group leaders and/or participants across treatment sessions during treatment delivery in the Primary Care Group Education arm may impact the power of the trial. We also present a Bayesian approach to estimate the intraclass correlation coefficients. Methods We present statistical models for each treatment arm of Whole Health Options and Pain Education trial in which power is estimated under different intraclass correlation coefficients and mapping matrices between participants and clinicians or treatment sessions. Power calculations are based on pairwise comparisons. In practice, sample size calculations depend on estimates of the intraclass correlation coefficients at the treatment sessions and clinician levels. To accommodate such complexities, we present a Bayesian framework for the estimation of intraclass correlation coefficients under different participant-to-session and participant-to-clinician mapping scenarios. We simulated continuous outcome data based on various clinical scenarios in Whole Health Options and Pain Education trial using a range of intraclass correlation coefficients and mapping matrices and used Gibbs samplers with conjugate priors to obtain posteriors of the intraclass correlation coefficients under those different scenarios. Posterior means and medians and their biases are calculated for the intraclass correlation coefficients to evaluate the operating characteristics of the Bayesian intraclass correlation coefficient estimators. Results Power for Whole Health Team versus Primary Care Group Education is sensitive to the intraclass correlation coefficient in the Whole Health Team arm. In these two arms, an increased number of clinicians, more evenly distributed workload of clinicians, or more homogeneous treatment group sizes leads to increased power. Our simulation study for the intraclass correlation coefficient estimation indicates that the posterior mean intraclass correlation coefficient estimator has less bias when the true intraclass correlation coefficients are large (i.e. 0.10), but when the intraclass correlation coefficient is small (i.e. 0.01), the posterior median intraclass correlation coefficient estimator is less biased. Conclusion Knowledge of intraclass correlation coefficients and the structure of clustering are critical to the design of individually randomized group treatment trials with partially nested clusters. We demonstrate that the intraclass correlation coefficient of the Whole Health Team arm can affect power in the Whole Health Options and Pain Education trial. A Bayesian approach provides a flexible procedure for estimating the intraclass correlation coefficients under complex scenarios. More work is needed to educate the research community about the individually randomized group treatment design and encourage publication of intraclass correlation coefficients to help inform future trial designs.

Sample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances

“…These sessions were led by three community practitioners, each treating 10 patients on average ($$$ {m}_1=10 $$$). Because patients were treated by the same practitioner across all sessions, this study is a standard IRGT rather than a cross‐classified IRGT; therefore the more specialized multiple membership model 30 is not applicable in this context. Participants in the control arm received individual treatment and therefore clustering was absent in the control arm.…”

Randomization, design and analysis for interdependency in aging research: no person or mouse is an island

“…This is particularly relevant in aging related research involving older persons in congregate settings. Such circumstances by their nature tend to involve interdependency and examples of studies involving clusterrandomized trials 33,34 , pseudo cluster randomization 35,36 , group composition designs 37 and individu ally randomized but groupdelivered trials 38,39 exist. For example, herd immunity can affect the analysis of vaccine efficacy 40 , as discussed in 'Clusterrandomized controlled trials'.…”

Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials

“…Existing literature shows that determining ICCs during the design or early implementation can be crucial to the eventual treatment effect estimation. 25 Our method can be particularly valuable in understanding factors that contribute to dispersed responses within clusters and may potentially improve implementation and dissemination of interventions during their rollout or for a future intervention design. Fourth, our methods contribute to a different aspect of understanding the heterogeneous treatment effect, which usually means the heterogeneous responses to intervention due to preexisting individual or cluster features.…”