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.

Trial registration: ClinicalTrials.gov NCT04330365.

Keywords: Bayesian; Individually randomized group treatment; clustering; dynamic treatment group; intraclass correlation; multiple membership model; multiple-arm trial; power; pragmatic trials.

Conflict of interest statement

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Trial registration number at NCT04330365.

Figures

Figure 1.

Whole Health Team vs. Primary Care Group Education to Promote Non-Pharmacological Strategies to Improve Pain, Functioning, and Quality of Life in Veterans (wHOPE) Trial Flowchart.

Figure 2.

Power curves for the three pairwise comparisons in the wHOPE trial (Whole Health Team vs. Usual Primary Care; Primary Care Group Education vs. Usual Primary Care; Whole Health Team vs. Primary Care Group Education) under varying ICCs in the Whole Health Team Arm and fixed ICC of 0.20 in Primary Care Group Education Arm. WHT: Whole Health Team; PC-GE: Primary Care Group Education; UPC: Usual Primary Care (a): W1V1 (b): W1V2 (c): W2V1 (d): W2V2. W1: each participant is treated by only one primary Whole Health coach (8 sessions). W2: each participant is also treated by a backup coach (1 of 8 sessions) if backup is available. V1: each session has an equal number of participants. V2: each session has either 1 or 10 participants.