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Recurrent events are common in clinical studies and are often subject to terminal events. In pragmatic trials, participants are often nested in clinics and can be susceptible or structurally unsusceptible to the recurrent events. We develop a Bayesian shared random effects model to accommodate this complex data structure. To achieve robustness, we consider the Dirichlet processes to model the residual of the accelerated failure time model for the survival process as well as the cluster-specific shared frailty distribution, along with an efficient sampling algorithm for posterior inference. Our method is applied to a recent cluster randomized trial on fall injury prevention.

While considerable scholarship has explored responsibilities owed to research participants at the conclusion of explanatory clinical trials, no guidance exists regarding responsibilities owed at the conclusion of a pragmatic clinical trial (PCT). Yet post-trial responsibilities in PCTs present distinct considerations from those emphasized in existing guidance and prior scholarship. Among these considerations include the responsibilities of the healthcare delivery systems in which PCTs are embedded, and decisions about implementation for interventions that demonstrate meaningful benefit following their integration into usual care settings-or deimplementation for those that fail to do so. In this article, we present an overview of prior scholarship and guidance on post-trial responsibilities, and then identify challenges for post-trial responsibilities for PCTs. We argue that, given one of the key rationales for PCTs is that they can facilitate uptake of their results by relevant decision-makers, there should be a presumptive default that PCT study results be incorporated into future care delivery processes. Fulfilling this responsibility will require prospective planning by researchers, healthcare delivery system leaders, institutional review boards, and sponsors, so as to ensure that the knowledge gained from PCTs does, in fact, influence real-world practice.

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Pragmatic trials aim to assess intervention efficacy in usual patient care settings, contrasting with explanatory trials conducted under controlled conditions. In aging research, pragmatic trials are important designs for obtaining real-world evidence in elderly populations, which are often underrepresented in trials. In this review, we discuss statistical considerations from a frequentist approach for the design and analysis of pragmatic trials. When choosing the dependent variable, it is essential to use an outcome that is highly relevant to usual medical care while also providing sufficient statistical power. Besides traditionally used binary outcomes, ordinal outcomes can provide pragmatic answers with gains in statistical power. Cluster randomization requires careful consideration of sample size calculation and analysis methods, especially regarding missing data and outcome variables. Mixed effects models and generalized estimating equations (GEEs) are recommended for analysis to account for center effects, with tools available for sample size estimation. Multi-arm studies pose challenges in sample size calculation, requiring adjustment for design effects and consideration of multiple comparison correction methods. Secondary analyses are common but require caution due to the risk of reduced statistical power and false-discovery rates. Safety data collection methods should balance pragmatism and data quality. Overall, understanding statistical considerations is crucial for designing rigorous pragmatic trials that evaluate interventions in elderly populations under real-world conditions. In conclusion, this review focuses on various statistical topics of interest to those designing a pragmatic clinical trial, with consideration of aspects of relevance in the aging research field.

Treatment noncompliance and censoring are two common complications in clinical trials. Motivated by the ADAPTABLE pragmatic clinical trial, we develop methods for assessing treatment effects in the presence of treatment noncompliance with a right-censored survival outcome. We classify the participants into principal strata, defined by their joint potential compliance status under treatment and control. We propose a multiply robust estimator for the causal effects on the survival probability scale within each principal stratum. This estimator is consistent even if one, sometimes two, of the four working models-on the treatment assignment, the principal strata, censoring, and the outcome-is misspecified. A sensitivity analysis strategy is developed to address violations of key identification assumptions, the principal ignorability and monotonicity. We apply the proposed approach to the ADAPTABLE trial to study the causal effect of taking low- versus high-dosage aspirin on all-cause mortality and hospitalization from cardiovascular diseases.

BACKGROUND: Variable 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.
METHODS: We 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).
RESULTS: The 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.
CONCLUSIONS: We 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.
UNASSIGNED: NCT02475850.

In this perspective, we present our experience developing and conducting two pragmatic clinical trials investigating physical therapist-led telehealth strategies for persons with chronic low back pain. Both trials, the BeatPain Utah and AIM-Back trials, are part of pragmatic clinical trial collaboratories and are being conducted with persons from communities that experience pain management disparities. Practice guidelines recommend nonpharmacologic care, and advise against opioid therapy, for the primary care management of persons with chronic low back pain. Gaps between these recommendations and actual practice patterns are pervasive, particularly for persons from racial or ethnic minoritized communities, those with fewer economic resources, and those living in rural areas including Veterans. Access barriers to evidence-based nonpharmacologic care, which is often provided by physical therapists, have contributed to these evidence-practice gaps. Telehealth delivery has created new opportunities to overcome access barriers for nonpharmacologic pain care. As a relatively new delivery mode however, telehealth delivery of physical therapy comes with additional challenges related to technology, intervention adaptations and cultural competence. The purpose of this article is to describe the challenges encountered when implementing telehealth physical therapy programs for persons with chronic low back pain in historically underserved communities. We also discuss strategies developed to overcome barriers in an effort to improve access to telehealth physical therapy and reduce pain management disparities. Inclusion of diverse and under-represented communities in pragmatic clinical trials is a critical consideration for improving disparities, but the unique circumstances present in these communities must be considered when developing implementation strategies.

Patients, families, healthcare providers and funders face multiple comparable treatment options without knowing which provides the best quality of care. As a step towards improving this, the REthinking Clinical Trials (REaCT) pragmatic trials program started in 2014 to break down many of the traditional barriers to performing clinical trials. However, until other innovative methodologies become widely used, the impact of this program will remain limited. These innovations include the incorporation of near equivalence analyses and the incorporation of artificial intelligence (AI) into clinical trial design. Near equivalence analyses allow for the comparison of different treatments (drug and non-drug) using quality of life, toxicity, cost-effectiveness, and pharmacokinetic/pharmacodynamic data. AI offers unique opportunities to maximize the information gleaned from clinical trials, reduces sample size estimates, and can potentially \"rescue\" poorly accruing trials. On 2 May 2023, the first REaCT international symposium took place to connect clinicians and scientists, set goals and identify future avenues for investigator-led clinical trials. Here, we summarize the topics presented at this meeting to promote sharing and support other similarly motivated groups to learn and share their experiences.

BACKGROUND: Mental illness among emerging adults is often difficult to ameliorate due to fluctuating symptoms and heterogeneity. Recently, innovative approaches have been developed to improve mental health care for emerging adults, including (1) implementing patient-reported outcome measures (PROMs) to assess illness severity and inform stratified care to assign emerging adults to a treatment modality commensurate with their level of impairment and (2) implementing a rapid learning health system in which data are continuously collected and analyzed to generate new insights, which are then translated to clinical practice, including collaboration among clients, health care providers, and researchers to co-design and coevaluate assessment and treatment strategies.
OBJECTIVE: The aim of the study is to determine the feasibility and acceptability of implementing a rapid learning health system to enable a measurement-based, stratified care treatment strategy for emerging adults.
METHODS: This study takes place at a specialty clinic serving emerging adults (age 16-24 years) in Calgary, Canada, and involves extensive collaboration among researchers, providers, and youth. The study design includes six phases: (1) developing a transdiagnostic platform for PROMs, (2) designing an initial stratified care model, (3) combining the implementation of PROMs with stratified care, (4) evaluating outcomes and disseminating results, (5) modification of stratified care based on data derived from PROMs, and (6) spread and scale to new sites. Qualitative and quantitative feedback will be collected from health care providers and youth throughout the implementation process. These data will be analyzed at regular intervals and used to modify the way future services are delivered. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework is used to organize and evaluate implementation according to 3 key objectives: improving treatment selection, reducing average wait time and treatment duration, and increasing the value of services.
RESULTS: This project was funded through a program grant running from 2021 to 2026. Ethics approval for this study was received in February 2023. Presently, we have developed a system of PROMs and organized clinical services into strata of care. We will soon begin using PROMs to assign clients to a stratum of care and using feedback from youth and clinicians to understand how to improve experiences and outcomes.
CONCLUSIONS: This study has key implications for researchers and clinicians looking to understand how to customize emerging adult mental health services to improve the quality of care and satisfaction with care. This study has significant implications for mental health care systems as part of a movement toward value-based health care.
UNASSIGNED: PRR1-10.2196/51667.