INTRODUCTION Greater dementia caregiver self-efficacy (CSE) may have considerable benefits for persons living with dementia (PLWD). The correlates of CSE have received little study.METHODS Secondary analysis of an 18-month randomized pragmatic clinical trial of 2037 community-dwelling PLWD and their caregivers. Baseline and longitudinal analyses were conducted.RESULTS In cross-sectional multiple regression analyses of baseline data, several factors were significantly associated with lower CSE, including not living with the PLWD, having higher depression scores, distress from PLWD behavioral symptoms, more caregiver strain, and higher PLWD basic activities of daily living functional status. In longitudinal analyses, CSE increased among those with lower baseline CSE, in women more than men, among caregivers with lower baseline depression scores, and among caregivers of PLWD with worse functioning.CONCLUSIONS CSE is associated with modifiable factors and can be improved with comprehensive dementia care interventions, particularly among those with lower baseline CSE.TRIAL REGISTRATION ClinicalTrials.gov, NCT03786471, https://clinicaltrials.gov/

Importance The effectiveness of different approaches to dementia care is unknown. Objective To determine the effectiveness of health system–based, community-based dementia care, and usual care for persons with dementia and for caregiver outcomes. Design, Setting, and Participants Randomized clinical trial of community-dwelling persons living with dementia and their caregivers conducted at 4 sites in the US (enrollment June 2019-January 2023; final follow-up, August 2023). Interventions Participants were randomized 7:7:1 to health system–based care provided by an advanced practice dementia care specialist (n = 1016); community-based care provided by a social worker, nurse, or licensed therapist care consultant (n = 1016); or usual care (n = 144). Main Outcomes and Measures Primary outcomes were caregiver-reported Neuropsychiatric Inventory Questionnaire (NPI-Q) severity score for persons living with dementia (range, 0-36; higher scores, greater behavioral symptoms severity; minimal clinically important difference [MCID], 2.8-3.2) and Modified Caregiver Strain Index for caregivers (range, 0-26; higher scores, greater strain; MCID, 1.5-2.3). Three secondary outcomes included caregiver self-efficacy (range, 4-20; higher scores, more self-efficacy). Results Among 2176 dyads (individuals with dementia, mean age, 80.6 years; 58.4%, female; and 20.6%, Black or Hispanic; caregivers, mean age, 65.2 years; 75.8%, female; and 20.8% Black or Hispanic), primary outcomes were assessed for more than 99% of participants, and 1343 participants (62% of those enrolled and 91% still alive and had not withdrawn) completed the study through 18 months. No significant differences existed between the 2 treatments or between treatments vs usual care for the primary outcomes. Overall, the least squares means (LSMs) for NPI-Q scores were 9.8 for health system, 9.5 for community-based, and 10.1 for usual care. The difference between health system vs community-based care was 0.30 (97.5% CI, −0.18 to 0.78); health system vs usual care, −0.33 (97.5% CI, −1.32 to 0.67); and community-based vs usual care, −0.62 (97.5% CI, −1.61 to 0.37). The LSMs for the Modified Caregiver Strain Index were 10.7 for health system, 10.5 for community-based, and 10.6 for usual care. The difference between health system vs community-based care was 0.25 (97.5% CI, −0.16 to 0.66); health system vs usual care, 0.14 (97.5% CI, −0.70 to 0.99); and community-based vs usual care, −0.10 (97.5% CI, −0.94 to 0.74). Only the secondary outcome of caregiver self-efficacy was significantly higher for both treatments vs usual care but not between treatments: LSMs were 15.1 for health system, 15.2 for community-based, and 14.4 for usual care. The difference between health system vs community-based care was −0.16 (95% CI, −0.37 to 0.06); health system vs usual care, 0.70 (95% CI, 0.26-1.14); and community-based vs usual care, 0.85 (95% CI, 0.42 to 1.29). Conclusions and Relevance In this randomized trial of dementia care programs, no significant differences existed between health system–based and community-based care interventions nor between either active intervention or usual care regarding patient behavioral symptoms and caregiver strain. Trial Registration ClinicalTrials.gov Identifier: NCT03786471

BACKGROUND AND OBJECTIVES:Routinely collected data (RCD) from healthcare claims and encounters are increasingly used for outcomes in randomized trials; however, methods for estimating the validity and relative precision of RCD-derived outcomes compared to those from conventional outcome ascertainment are limited. We developed an approach to measuring validity and relative precision of RCD and quantifying uncertainty. METHODS:We reanalyzed data from the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) cluster-randomized, controlled trial. Eighty-six primary care practices in 10 US healthcare systems were randomized to either a multifactorial intervention delivered by nurse falls care managers, or enhanced usual care, with 5451 persons age ≥ 70 at increased fall injury risk enrolled in the study. We estimated the hazard ratio (HR) and confidence interval (CI) for STRIDE's primary outcome (time to first serious fall injury) using original study data and RCD. The ratio of the RCD HR to original HR ("ratio of HRs") measured validity. The confidence limit ratio (CLR; upper divided by lower confidence limits of CI) measured precision, with the ratio of the CLR with RCD to the CLR from the original study data ("ratio of CLRs"), measuring relative precision. We estimated uncertainty around the ratio of HRs and ratio of CLRs using bootstrapped 95% CIs and performed sensitivity analyses to assess the effects of adaptations needed to use RCD. RESULTS:Among the original sample of 5451 study participants, 5036 (92%) were linked to RCD. The intervention to control HR was 0.91 (95% CI: 0.78-1.07) in RCD, compared to 0.92 (95% CI: 0.80-1.06) in the original data. Using all RCD through STRIDE's administrative end date, the ratio of HRs was 1.00 (95% CI: 0.89-1.11) and ratio of CLRs was 1.03 (95% CI: 0.96-1.06). The CI around ratio of HRs was about three-fold wider for RCD than for the original STRIDE data in individuals who linked to RCD. Relative precision of RCD improved with increased length of follow-up. CONCLUSION:Relying solely on RCD to ascertain the primary outcome in STRIDE would have resulted in similar point estimates and confidence limits for the treatment effect as in the original data. However, there was meaningful uncertainty around the estimate of validity. Efforts to validate RCD-derived outcomes for use as clinical trial endpoints should include measurement of uncertainty around validity estimates.

Importance:Despite the large numbers of persons living with dementia, the best approach to providing dementia care is unknown. Objective:To compare the effectiveness of health system dementia care (HSDC), community-based dementia care (CBDC), and usual care (UC) on person living with dementia and caregiver outcomes. Design, Setting, and Participants:The Dementia Care Study (D-CARE) was a pragmatic, 18-month, 3-armed, assessor-blinded randomized clinical trial conducted from June 2019 through August 2023 at 4 clinical trial sites in North Carolina, Texas, and Pennsylvania. Person living with dementia-caregiver dyads were included. Data were analyzed from December 2024 to June 2025. Interventions:HSDC comanagement by nurse practitioners or physician assistants or CBDC provided telephonically by a social worker, nurse, or licensed therapist for 18 months. Main Outcomes and Measures:Prespecified outcomes included person living with dementia cognition, functional status, and quality of life; caregiver ratings of quality of care (10 items; range, 0-10) and satisfaction with care (11 items; range, 11-55), including how helpful the dementia care was, access to services, and support; positive aspects of caregiving; and overall caregiver burden as well as a measure of whether either the person living with dementia or caregiver benefitted. Results:A total of 2176 person living with dementia-caregiver dyads were enrolled; 1271 persons living with dementia (58.4%) and 1650 caregivers (75.8%) were female, and their mean (SD) age were 80.6 (8.5) years and 65.2 (12.3) years, respectively. There were no treatment differences between groups in person living with dementia functional status, cognition, or quality of life or in overall caregiver burden or positive aspects of caregiving. Caregiver satisfaction with care was higher with both interventions compared with UC (HSDC: least-squares mean difference, 2.6 points; 98.3% CI, 1.0-4.2; P < .001; CBDC: least-squares mean difference, 3.3 points; 98.3% CI, 1.7-4.9; P < .001). The difference in caregiver satisfaction with care between the interventions and UC was apparent by 3 months and persisted through the study. Caregiver-rated quality of care was higher in the CBDC group compared with UC (least-squares mean ratio, 1.1; 98.3% CI, 1.0-1.3; P = .046). Conclusions and Relevance:In this randomized clinical trial, HSDC and CBDC did not differ from UC on most person living with dementia and caregiver measures. However, caregivers reported higher satisfaction with both interventions compared with UC. These findings can help further refine comprehensive dementia care programs. Trial Registration:ClinicalTrials.gov Identifier: NCT03786471.

BackgroundVariable data quality poses a challenge to using electronic health record (EHR) data to ascertain acute clinical outcomes in multi-site clinical trials. Differing EHR platforms and data comprehensiveness across clinical trial sites, especially if patients received care outside of the clinical site's network, can also affect validity of results. Overcoming these challenges requires a structured approach.MethodsWe propose a framework and create a checklist to assess the readiness of clinical sites to contribute EHR data to a clinical trial for the purpose of outcome ascertainment, based on our experience with the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) study, which enrolled 5451 participants in 86 primary care practices across 10 healthcare systems (sites).ResultsThe site readiness checklist includes assessment of the infrastructure (i.e., size and structure of the site's healthcare system or clinical network), data procurement (i.e., quality of the data), and cost of obtaining study data. The checklist emphasizes the importance of understanding how data are captured and integrated across a site's catchment area and having a protocol in place for data procurement to ensure consistent and uniform extraction across each site.ConclusionsWe suggest rigorous, prospective vetting of the data quality and infrastructure of each clinical site before launching a multi-site trial dependent on EHR data. The proposed checklist serves as a guiding tool to help investigators ensure robust and unbiased data capture for their clinical trials.Original trial registration numberNCT02475850

Background Diagnosis-code-based algorithms to identify fall injuries in Medicare data are useful for ascertaining outcomes in interventional and observational studies. However, these algorithms have not been validated against a fully external reference standard, in ICD-10-CM, or in Medicare Advantage (MA) data.Methods We linked self-reported fall injuries leading to medical attention (FIMA) from the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial (reference standard) to Medicare fee-for-service (FFS) and MA data from 2015-19. We measured the area under the receiver operating characteristic curve (AUC) based on sensitivity and specificity of a diagnosis-code-based algorithm against the reference standard for presence or absence of >= 1 FIMA within a specified window of dates, varying the window size to obtain points on the curve. We stratified results by source (FFS vs MA), trial arm (intervention vs control), and STRIDE's 10 participating health care systems.Results Both reference standard data and Medicare data were available for 4 941 (of 5 451) participants. The reference standard and algorithm identified 2 054 and 2 067 FIMA, respectively. The algorithm had 45% sensitivity (95% confidence interval [CI]: 43%-47%) and 99% specificity (95% CI: 99%-99%) to identify reference standard FIMA within the same calendar month. The AUC was 0.79 (95% CI: 0.78-0.81) and was similar by FFS or MA data source and by trial arm but showed variation among STRIDE health care systems (AUC range by health care system, 0.71 to 0.84).Conclusions An ICD-10-CM algorithm to identify fall injuries demonstrated acceptable performance against an external reference standard, in both MA and FFS data.

BACKGROUND:Issues with specification of margins, adherence, and analytic population can potentially bias results toward the alternative in randomized noninferiority pragmatic trials. To investigate this potential for bias, we conducted a targeted search of the medical literature to examine how noninferiority pragmatic trials address these issues.METHODS:An Ovid MEDLINE database search was performed identifying publications in New England Journal of Medicine, Journal of the American Medical Association, Lancet, or British Medical Journal published between 2015 and 2021 that included the words "pragmatic" or "comparative effectiveness" and "noninferiority" or "non-inferiority." Our search identified 14 potential trials, 12 meeting our inclusion criteria (11 individually randomized, 1 cluster-randomized).RESULTS:Eleven trials had results that met the criteria established for noninferiority. Noninferiority margins were prespecified for all trials; all but two trials provided justification of the margin. Most trials did some monitoring of treatment adherence. All trials conducted intent-to-treat or modified intent-to-treat analyses along with per-protocol analyses and these analyses reached similar conclusions. Only two trials included all randomized participants in the primary analysis, one used multiple imputation for missing data. The percentage excluded from primary analyses ranged from ∼2% to 30%. Reasons for exclusion included randomization in error, nonadherence, not receiving assigned treatment, death, withdrawal, lost to follow-up, and incomplete data.CONCLUSION:Specification of margins, adherence, and analytic population require careful consideration to prevent bias toward the alternative in noninferiority pragmatic trials. Although separate guidance has been developed for noninferiority and pragmatic trials, it is not compatible with conducting a noninferiority pragmatic trial. Hence, these trials should probably not be done in their current format without developing new guidelines.

Importance:Lack of knowledge about longer-term outcomes remains a critical blind spot for trauma systems. Recent efforts have expanded trauma quality evaluation to include a broader array of postdischarge quality metrics. It remains unknown how such quality metrics should be used. Objective:To examine the utility of implementing recommended postdischarge quality metrics as a composite score and ascertain how composite score performance compares with that of in-hospital mortality for evaluating associations with hospital-level factors. Design, Setting, and Participants:This national hospital-level quality assessment evaluated hospital-level care quality using 100% Medicare fee-for-service claims of older adults (aged ≥65 years) hospitalized with primary diagnoses of trauma, hip fracture, and severe traumatic brain injury (TBI) between January 1, 2014, and December 31, 2015. Hospitals with annual volumes encompassing 10 or more of each diagnosis were included. The data analysis was performed between January 1, 2021, and December 31, 2022. Exposures:Reliability-adjusted quality metrics used to calculate composite scores included hospital-specific performance on mortality, readmission, and patients' average number of healthy days at home (HDAH) within 30, 90, and 365 days among older adults hospitalized with all forms of trauma, hip fracture, and severe TBI. Main Outcomes and Measures:Associations with hospital-level factors were compared using volume-weighted multivariable logistic regression. Results:A total of 573 554 older adults (mean [SD] age, 83.1 [8.3] years; 64.8% female; 35.2% male) from 1234 hospitals were included. All 27 reliability-adjusted postdischarge quality metrics significantly contributed to the composite score. The most important drivers were 30- and 90-day readmission, patients' average number of HDAH within 365 days, and 365-day mortality among all trauma patients. Associations with hospital-level factors revealed predominantly anticipated trends when older adult trauma quality was evaluated using composite scores (eg, worst performance was associated with decreased older adult trauma volume [odds ratio, 0.89; 95% CI, 0.88-0.90]). Results for in-hospital mortality showed inverted associations for each considered hospital-level factor and suggested that compared with nontrauma centers, level 1 trauma centers had a 17 times higher risk-adjusted odds of worst (highest quantile) vs best (lowest quintile) performance (odds ratio, 17.08; 95% CI, 16.17-18.05). Conclusions and Relevance:The study results challenge historical notions about the adequacy of in-hospital mortality as the single measure of older adult trauma quality and suggest that, when it comes to older adults, decisions about how quality is evaluated can profoundly alter understandings of what constitutes best practices for care. Composite scores appear to offer a promising means by which postdischarge quality metrics could be used.

Objectives The Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) Study cluster-randomized 86 primary care practices in 10 healthcare systems to a patient-centered multifactorial fall injury prevention intervention or enhanced usual care, enrolling 5451 participants. We estimated total healthcare costs from participant-reported fall injuries receiving medical attention (FIMA) that were averted by the STRIDE intervention and tested for healthcare-system-level heterogeneity and heterogeneity of treatment effect (HTE). Methods Participants were community-dwelling adults age ≥ 70 at increased fall injury risk. We estimated practice-level total costs per person-year of follow-up (PYF), assigning unit costs to FIMA with and without an overnight hospital stay. Using independent variables for treatment arm, healthcare system, and their interaction, we fit a generalized linear model with log link, log follow-up time offset, and Tweedie error distribution. Results Unadjusted total costs per PYF were $2,034 (intervention) and $2,289 (control). The adjusted (intervention minus control) cost difference per PYF was -$167 (95% confidence interval (CI), -$491, $216). Cost heterogeneity by healthcare system was present (p = 0.035), as well as HTE (p = 0.090). Adjusted total costs per PYF in control practices varied from $1,529 to $3,684 for individual healthcare systems; one system with mean intervention minus control costs of -$2092 (95% CI, -$3,686 to -$944) per PYF accounted for HTE, but not healthcare system cost heterogeneity. Conclusions We observed substantial heterogeneity of healthcare system costs in the STRIDE study, with small reductions in healthcare costs for FIMA in the STRIDE intervention accounted for by a single healthcare system. Trial registration Clinicaltrials.gov (NCT02475850).

Simulation studies play an important role in evaluating the performance of statistical models developed for analyzing complex survival data such as those with competing risks and clustering. This article aims to provide researchers with a basic understanding of competing risks data generation, techniques for inducing cluster-level correlation, and ways to combine them together in simulation studies, in the context of randomized clinical trials with a binary exposure or treatment. We review data generation with competing and semi-competing risks and three approaches of inducing cluster-level correlation for time-to-event data: the frailty model framework, the probability transform, and Moran’s algorithm. Using exponentially distributed event times as an example, we discuss how to introduce cluster-level correlation into generating complex survival outcomes, and illustrate multiple ways of combining these methods to simulate clustered, competing and semi-competing risks data with pre-specified correlation values or degree of clustering.

While statistical methods for analyzing cluster randomized trials with continuous and binary outcomes have been extensively studied and compared, little comparative evidence has been provided for analyzing cluster randomized trials with survival outcomes in the presence of competing risks. Motivated by the Strategies to Reduce Injuries and Develop Confidence in Elders trial, we carried out a simulation study to compare the operating characteristics of several existing population-averaged survival models, including the marginal Cox, marginal Fine and Gray, and marginal multi-state models. For each model, we found that adjusting for the intraclass correlations through the sandwich variance estimator effectively maintained the type I error rate when the number of clusters is large. With no more than 30 clusters, however, the sandwich variance estimator can exhibit notable negative bias, and a permutation test provides better control of type I error inflation. Under the alternative, the power for each model is differentially affected by two types of intraclass correlations—the within-individual and between-individual correlations. Furthermore, the marginal Fine and Gray model occasionally leads to higher power than the marginal Cox model or the marginal multi-state model, especially when the competing event rate is high. Finally, we provide an illustrative analysis of Strategies to Reduce Injuries and Develop Confidence in Elders trial using each analytical strategy considered.

Abstract Background: Issues with specification of margins and analytic methods can potentially bias results towards the alternative in randomized noninferiority pragmatic trials. To investigate this potential for bias we conducted a targeted search of the medical literature to examine how noninferiority pragmatic trials address these issues. Methods: An Ovid MEDLINE database search was performed that identified any publications in New England Journal of Medicine, Journal of the American Medical Association, Lancet, or British Medical Journal published between 2015 and 2021 (inclusive) that included the words “pragmatic” or “comparative effectiveness” as well as “noninferiority” or “non-inferiority” in a multi-purpose search. Our search identified 14 potential trials of which 12 met our inclusion criteria. Results: Of the 12 randomized pragmatic noninferiority trials, 11 were individually randomized trials and one was a cluster randomized trial. Ten of the 11 individually randomized trials met the criteria established for noninferiority as did the one cluster randomized trial. Noninferiority margins were prespecified for all the trials. The majority of margins (6) were based on either minimum clinically important differences, clinical experts, or consensus, while others were based on sample size, empirical data, or clinical decision. For two trials, no justification for the margin was provided. All trials conducted intent to treat or modified intent to treat analyses along with per protocol analyses and reached similar conclusions. Only two trials included all randomized participants in the primary analysis, one of which used multiple imputation to impute missing data. The percentage of participants excluded from the primary analysis ranged from about 2% to nearly 30% and sometimes differed between treatment arms.Conclusions: Specification of margins and methods of analysis require careful consideration to prevent bias towards the alternative in noninferiority trials. Much of the guidance on these two issues has been developed for a regulatory environment and not for pragmatic noninferiority trials. Since many pragmatic trials generally follow the PRECIS criteria of little or no monitoring of participant or practitioner adherence, it affects separation of treatments which in turn affects both the setting of margins and analysis. More recent developments on estimands can address the latter issue.

Falls are common in older adults and can lead to severe injuries. The Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial cluster‐randomized 86 primary care practices across 10 health systems to a multifactorial intervention to prevent fall injuries, delivered by registered nurses trained as falls care managers, or enhanced usual care. STRIDE enrolled 5451 community‐dwelling older adults age ≥70 at increased fall injury risk.

Nonpharmacological treatments are considered first-line pain management strategies, but they remain clinically underused. For years, pain-focused pragmatic clinical trials (PCTs) have generated evidence for the enhanced use of nonpharmacological interventions in routine clinical settings to help overcome implementation barriers. The Pragmatic Explanatory Continuum Indicator Summary (PRECIS-2) framework describes the degree of pragmatism across 9 key domains. Among these, "flexibility in delivery" and "flexibility in adherence," address a key goal of pragmatic research by tailoring approaches to settings in which people receive routine care. However, to maintain scientific and ethical rigor, PCTs must ensure that flexibility features do not compromise delivery of interventions as designed, such that the results are ethically and scientifically sound. Key principles of achieving this balance include clear definitions of intervention core components, intervention monitoring and documentation that is sufficient but not overly burdensome, provider training that meets the demands of delivering an intervention in real-world settings, and use of an ethical lens to recognize and avoid potential trial futility when necessary and appropriate. PERSPECTIVE: This article presents nuances to be considered when applying the PRECIS-2 framework to describe pragmatic clinical trials. Trials must ensure that patient-centered treatment flexibility does not compromise delivery of interventions as designed, such that measurement and analysis of treatment effects is reliable.

BACKGROUND:The traditional informed consent (IC) process rarely emphasizes research participants' comprehension of medical information, leaving them vulnerable to unknown risks and consequences associated with procedures or studies. OBJECTIVE:This paper explores how we evaluated the feasibility of a digital health tool called Virtual Multimedia Interactive Informed Consent (VIC) for advancing the IC process and compared the results with traditional paper-based methods of IC. METHODS:Using digital health and web-based coaching, we developed the VIC tool that uses multimedia and other digital features to improve the current IC process. The tool was developed on the basis of the user-centered design process and Mayer's cognitive theory of multimedia learning. This study is a randomized controlled trial that compares the feasibility of VIC with standard paper consent to understand the impact of interactive digital consent. Participants were recruited from the Winchester Chest Clinic at Yale New Haven Hospital in New Haven, Connecticut, and healthy individuals were recruited from the community using fliers. In this coordinator-assisted trial, participants were randomized to complete the IC process using VIC on the iPad or with traditional paper consent. The study was conducted at the Winchester Chest Clinic, and the outcomes were self-assessed through coordinator-administered questionnaires. RESULTS:A total of 50 participants were recruited in the study (VIC, n=25; paper, n=25). The participants in both groups had high comprehension. VIC participants reported higher satisfaction, higher perceived ease of use, higher ability to complete the consent independently, and shorter perceived time to complete the consent process. CONCLUSIONS:The use of dynamic, interactive audiovisual elements in VIC may improve participants' satisfaction and facilitate the IC process. We believe that using VIC in an ongoing, real-world study rather than a hypothetical study improved the reliability of our findings, which demonstrates VIC's potential to improve research participants' comprehension and the overall process of IC. TRIAL REGISTRATION:ClinicalTrials.gov NCT02537886; https://clinicaltrials.gov/ct2/show/NCT02537886.

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.

Characterizing the impacts of disruption attributable to the COVID-19 pandemic on clinical research is important, especially in pain research where psychological, social, and economic stressors attributable to the COVID-19 pandemic may greatly impact treatment effects. The National Institutes of Health - Department of Defense - Department of Veterans Affairs Pain Management Collaboratory (PMC) is a collective effort supporting 11 pragmatic clinical trials studying nonpharmacological approaches and innovative integrated care models for pain management in veteran and military health systems. The PMC rapidly developed a brief pandemic impacts measure for use across its pragmatic trials studying pain while remaining broadly applicable to other areas of clinical research. Through open discussion and consensus building by the PMC's Phenotypes and Outcomes Work Group, the PMC Coronavirus Pandemic (COVID-19) Measure was iteratively developed. The measure assesses the following domains (one item/domain): access to healthcare, social support, finances, ability to meet basic needs, and mental or emotional health. Two additional items assess infection status (personal and household) and hospitalization. The measure uses structured responses with a three-point scale for COVID-19 infection status and four-point ordinal rank response for all other domains. We recommend individualized adaptation as appropriate by clinical research teams using this measure to survey the effects of the COVID-19 pandemic on study participants. This can also help maintain utility of the measure beyond the COVID-19 pandemic to characterize impacts during future public health emergencies that may require mitigation strategies such as periods of quarantine and isolation.