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There is an increasing interest in the use of joint models for the analysis of longitudinal and survival data. While random effects models have been extensively studied, these models can be hard to implement and the fixed effect regression parameters must be interpreted conditional on the random effects. Copulas provide a useful alternative framework for joint modeling. One advantage of using copulas is that practitioners can directly specify marginal models for the outcomes of interest. We develop a joint model using a Gaussian copula to characterize the association between multivariate longitudinal and survival outcomes. Rather than using an unstructured correlation matrix in the copula model to characterize dependence structure as is common, we propose a novel decomposition that allows practitioners to impose structure (e.g., auto-regressive) which provides efficiency gains in small to moderate sample sizes and reduces computational complexity. We develop a Markov chain Monte Carlo model fitting procedure for estimation. We illustrate the method's value using a simulation study and present a real data analysis of longitudinal quality of life and disease-free survival data from an International Breast Cancer Study Group trial., (© The Author 2024. Published by Oxford University Press. All rights reserved. For Permissions, email: journals.permissions@oup.com.)

In recent years, multi-regional clinical trials (MRCTs) have increased in popularity in the pharmaceutical industry due to their ability to accelerate the global drug development process. To address potential challenges with MRCTs, the International Council for Harmonisation released the E17 guidance document which suggests the use of statistical methods that utilize information borrowing across regions if regional sample sizes are small. We develop an approach that allows for information borrowing via Bayesian model averaging in the context of a joint analysis of survival and longitudinal data from MRCTs. In this novel application of joint models to MRCTs, we use Laplace's method to integrate over subject-specific random effects and to approximate posterior distributions for region-specific treatment effects on the time-to-event outcome. Through simulation studies, we demonstrate that the joint modeling approach can result in an increased rejection rate when testing the global treatment effect compared with methods that analyze survival data alone. We then apply the proposed approach to data from a cardiovascular outcomes MRCT., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. [br]For permissions, please e-mail: journals.permissions@oup.com.)

There are many Bayesian design methods allowing for the incorporation of historical data for sample size determination (SSD) in situations where the outcome in the historical data is the same as the outcome of a new study. However, there is a dearth of methods supporting the incorporation of data from a previously completed clinical trial that investigated the same or similar treatment as the new trial but had a primary outcome that is different. We propose a simulation-based Bayesian SSD framework using the partial-borrowing scale transformed power prior (straPP). The partial-borrowing straPP is developed by applying a novel scale transformation to a traditional power prior on the parameters from the historical data model to make the information better align with the new data model. The scale transformation is based on the assumption that the standardized parameters (i.e., parameters multiplied by the square roots of their respective Fisher information matrices) are equal. To illustrate the method, we present results from simulation studies that use real data from a previously completed clinical trial to design a new clinical trial with a primary time-to-event endpoint.

We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCTs) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g., unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted power prior provides robust inference under various forms of incompatibility between the external controls and RCT population., (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)

Sponsors often rely on multi-regional clinical trials (MRCTs) to introduce new treatments more rapidly into the global market. Many commonly used statistical methods do not account for regional differences, and small regional sample sizes frequently result in lower estimation quality of region-specific treatment effects. The International Council for Harmonization E17 guidelines suggest consideration of methods that allow for information borrowing across regions to improve estimation. In response to these guidelines, we develop a novel methodology to estimate global and region-specific treatment effects from MRCTs with time-to-event endpoints using Bayesian model averaging (BMA). This approach accounts for the possibility of heterogeneous treatment effects between regions, and we discuss how to assess the consistency of these effects using posterior model probabilities. We obtain posterior samples of the treatment effects using a Laplace approximation, and we show through simulation studies that the proposed modeling approach estimates region-specific treatment effects with lower mean squared error than a Cox proportional hazards model while resulting in a similar rejection rate of the global treatment effect. We then apply the BMA approach to data from the LEADER trial, an MRCT designed to evaluate the cardiovascular safety of an anti-diabetic treatment., (© 2023 The International Biometric Society.)

Joint models for recurrent event and terminating event data are increasingly used for the analysis of clinical trials. However, few methods have been proposed for designing clinical trials using these models. In this article, we develop a Bayesian clinical trial design methodology focused on evaluating the effect of an investigational product (IP) on both recurrent event and terminating event processes considered as multiple primary endpoints, using a multifrailty joint model. Dependence between the recurrent and terminating event processes is accounted for using a shared frailty. Inferences for the multiple primary outcomes are based on posterior model probabilities corresponding to mutually exclusive hypotheses regarding the benefit of IP with respect to the recurrent and terminating event processes. We propose an approach for sample size determination to ensure the trial design has a high power and a well-controlled type I error rate, with both operating characteristics defined from a Bayesian perspective. We also consider a generalization of the proposed parametric model that uses a nonparametric mixture of Dirichlet processes to model the frailty distributions and compare its performance to the proposed approach. We demonstrate the methodology by designing a colorectal cancer clinical trial with a goal of demonstrating that the IP causes a favorable effect on at least one of the two outcomes but no harm on either., (© The Author 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In 2019, the National Health Interview survey found that nearly 59% of adults reported pain some, most, or every day in the past 3 months, with 39% reporting back pain, making back pain the most prevalent source of pain, and a significant issue among adults. Often, identifying a direct, treatable cause for back pain is challenging, especially as it is often attributed to complex, multifaceted issues involving biological, psychological, and social components. Due to the difficulty in treating the true cause of chronic low back pain (cLBP), an over-reliance on opioid pain medications among cLBP patients has developed, which is associated with increased prevalence of opioid use disorder and increased risk of death. To combat the rise of opioid-related deaths, the National Institutes of Health (NIH) initiated the Helping to End Addiction Long-TermSM (HEAL) initiative, whose goal is to address the causes and treatment of opioid use disorder while also seeking to better understand, diagnose, and treat chronic pain. The NIH Back Pain Consortium (BACPAC) Research Program, a network of 14 funded entities, was launched as a part of the HEAL initiative to help address limitations surrounding the diagnosis and treatment of cLBP. This paper provides an overview of the BACPAC research program's goals and overall structure, and describes the harmonization efforts across the consortium, define its research agenda, and develop a collaborative project which utilizes the strengths of the network. The purpose of this paper is to serve as a blueprint for other consortia tasked with the advancement of pain related science., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Academy of Pain Medicine.)

Management of patients suffering from low back pain (LBP) is challenging and requires development of diagnostic techniques to identify specific patient subgroups and phenotypes in order to customize treatment and predict clinical outcome. The Back Pain Consortium (BACPAC) Research Program Spine Imaging Working Group has developed standard operating procedures (SOPs) for spinal imaging protocols to be used in all BACPAC studies. These SOPs include procedures to conduct spinal imaging assessments with guidelines for standardizing the collection, reading/grading (using structured reporting with semi-quantitative evaluation using ordinal rating scales), and storage of images. This article presents the approach to image acquisition and evaluation recommended by the BACPAC Spine Imaging Working Group. While the approach is specific to BACPAC studies, it is general enough to be applied at other centers performing magnetic resonance imaging (MRI) acquisitions in patients with LBP. The herein presented SOPs are meant to improve understanding of pain mechanisms and facilitate patient phenotyping by codifying MRI-based methods that provide standardized, non-invasive assessments of spinal pathologies. Finally, these recommended procedures may facilitate the integration of better harmonized MRI data of the lumbar spine across studies and sites within and outside of BACPAC studies., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Academy of Pain Medicine.)

Objective: One aim of the Back Pain Consortium (BACPAC) Research Program is to develop an integrated model of chronic low back pain that is informed by combined data from translational research and clinical trials. We describe efforts to maximize data harmonization and accessibility to facilitate Consortium-wide analyses., Methods: Consortium-wide working groups established harmonized data elements to be collected in all studies and developed standards for tabular and nontabular data (eg, imaging and omics). The BACPAC Data Portal was developed to facilitate research collaboration across the Consortium., Results: Clinical experts developed the BACPAC Minimum Dataset with required domains and outcome measures to be collected by use of questionnaires across projects. Other nonrequired domain-specific measures are collected by multiple studies. To optimize cross-study analyses, a modified data standard was developed on the basis of the Clinical Data Interchange Standards Consortium Study Data Tabulation Model to harmonize data structures and facilitate integration of baseline characteristics, participant-reported outcomes, chronic low back pain treatments, clinical exam, functional performance, psychosocial characteristics, quantitative sensory testing, imaging, and biomechanical data. Standards to accommodate the unique features of chronic low back pain data were adopted. Research units submit standardized study data to the BACPAC Data Portal, developed as a secure cloud-based central data repository and computing infrastructure for researchers to access and conduct analyses on data collected by or acquired for BACPAC., Conclusions: BACPAC harmonization efforts and data standards serve as an innovative model for data integration that could be used as a framework for other consortia with multiple, decentralized research programs., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Academy of Pain Medicine.)

Background: Availability of PrEP-providing clinics is low in the Southern U.S., a region at the center of the U.S. HIV epidemic with significant HIV disparities among minoritized populations, but little is known about state-level differences in PrEP implementation in the region. We explored state-level clustering of organizational constructs relevant to PrEP implementation in family planning (FP) clinics in the Southern U.S., Methods: We surveyed providers and administrators of FP clinics not providing PrEP in 18 Southern states (Feb-Jun 2018, N  = 414 respondents from 224 clinics) on these constructs: readiness to implement PrEP, PrEP knowledge/attitudes, implementation climate, leadership engagement, and available resources. We analyzed each construct using linear mixed models. A principal component analysis identified six principal components, which were inputted into a K-means clustering analysis to examine state-level clustering., Results: Three clusters (C1-3) were identified with five, three, and four states, respectively. Canonical variable 1 separated C1 and C2 from C3 and was primarily driven by PrEP readiness, HIV-specific implementation climate, PrEP-specific leadership engagement, PrEP attitudes, PrEP knowledge, and general resource availability. Canonical variable 2 distinguished C2 from C1 and was primarily driven by PrEP-specific resource availability, PrEP attitudes, and general implementation climate. All C3 states had expanded Medicaid, compared to 1 C1 state (none in C2)., Conclusion: Constructs relevant for PrEP implementation exhibited state-level clustering, suggesting that tailored strategies could be used by clustered states to improve PrEP provision in FP clinics. Medicaid expansion was a common feature of states within C3, which could explain the similarity of their implementation constructs. The role of Medicaid expansion and state-level policies on PrEP implementation warrants further exploration., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sheth, Enders, McCumber, Psioda, Ramakrishnan and Sales.)

Background: Stroke patients discharged home often require prolonged assistance from caregivers. Little is known about the real-world effectiveness of a comprehensive stroke transitional care intervention on relieving caregiver strain., Objectives: To describe the effect of the COMPASS transitional care (COMPASS-TC) intervention on caregiver strain and characterize the types, duration, and intensity of caregiving., Methods: The cluster-randomized COMPASS pragmatic trial evaluated the effectiveness of COMPASS-TC versus usual care with patients with mild stroke and TIA at 40 hospitals in North Carolina, USA. Of 5882 patients enrolled, 4208 (71%) identified a familial caregiver. A follow-up Caregiver Questionnaire, including the Modified Caregiver Strain Index, was administered at approximately three months post-discharge. Demographics and frequency, duration, and intensity of caregiving were compared between groups., Results: 1228 caregivers (29%) completed the questionnaire. Completion was positively associated with older patient age, white race, and spousal relationship. One-third of the caregivers provided ≥30 hours of care per week and 889 (79%) provided care ≥9 weeks. Average standardized caregiver strain was 21.9 (0-100), increasing with stroke severity and comorbidity burden. Women caregivers reported higher strain than men. Treatment allocation was not associated with caregiver strain., Conclusions: This sample of mild stroke and TIA survivors received significant assistance from familial caregivers. However, caregiver strain was relatively low. Findings support the importance of familial caregiving in stroke, the continued disproportionate burden on women within the family, and the need for future research on caregiver support.

There has been increased interest in the design and analysis of studies consisting of multiple response variables of mixed types. For example, in clinical trials, it is desirable to establish efficacy for a treatment effect in primary and secondary outcomes. In this article, we develop Bayesian approaches for hypothesis testing and study planning for data consisting of multiple response variables of mixed types with covariates. We assume that the responses are correlated via a Gaussian copula, and that the model for each response is, marginally, a generalized linear model (GLM). Taking a fully Bayesian approach, the proposed method enables inference based on the joint posterior distribution of the parameters. Under some mild conditions, we show that the joint distribution of the posterior probabilities under any Bayesian analysis converges to a Gaussian copula distribution as the sample size tends to infinity. Using this result, we develop an approach to control the type I error rate under multiple testing. Simulation results indicate that the method is more powerful than conducting marginal regression models and correcting for multiplicity using the Bonferroni-Holm Method. We also develop a Bayesian approach to sample size determination in the presence of response variables of mixed types, extending the concept of probability of success (POS) to multiple response variables of mixed types., (© 2023 John Wiley & Sons Ltd.)

Multiregional clinical trials (MRCTs) provide the benefit of more rapidly introducing drugs to the global market; however, small regional sample sizes can lead to poor estimation quality of region-specific effects when using current statistical methods. With the publication of the International Conference for Harmonisation E17 guideline in 2017, the MRCT design is recognized as a viable strategy that can be accepted by regional regulatory authorities, necessitating new statistical methods that improve the quality of region-specific inference. In this article, we develop a novel methodology for estimating region-specific and global treatment effects for MRCTs using Bayesian model averaging. This approach can be used for trials that compare two treatment groups with respect to a continuous outcome, and it allows for the incorporation of patient characteristics through the inclusion of covariates. We propose an approach that uses posterior model probabilities to quantify evidence in favor of consistency of treatment effects across all regions, and this metric can be used by regulatory authorities for drug approval. We show through simulations that the proposed modeling approach results in lower MSE than a fixed-effects linear regression model and better control of type I error rates than a Bayesian hierarchical model., (© The Author 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background and Objectives: We examined transitional care management within 90 days and 1 year following discharge home among acute stroke and transient ischemic attack patients from the Comprehensive Post-Acute Stroke Services (COMPASS) Study, a cluster-randomized pragmatic trial of early supported discharge conducted in 41 hospitals (40 hospital units) in North Carolina, United States., Methods: Data for 2262 of the total 6024 (37.6%; 1069 intervention and 1193 usual care) COMPASS patients were linked with the Centers for Medicare and Medicaid Services fee-for-service Medicare claims. Time to the first ambulatory care visit was examined using Cox proportional hazard models adjusted for patient characteristics not included in the randomization protocol., Results: Only 6% of the patients [mean (SD) age 74.9 (10.2) years, 52.1% women, 80.3% White)] did not have an ambulatory care visit within 90 days postdischarge. Mean time (SD) to first ambulatory care visit was 12.0 (26.0) and 16.3 (35.1) days in intervention and usual care arms, respectively, with the majority of visits in both study arms to primary care providers. The COMPASS intervention resulted in a 27% greater use of ambulatory care services within 1 year postdischarge, relative to usual care [HR=1.27 (95% CI: 1.14-1.41)]. The use of transitional care billing codes was significantly greater in the intervention arm as compared with usual care [OR=1.87 (95% CI: 1.54-2.27)]., Discussion: The COMPASS intervention, which was aimed at improving stroke post-acute care, was associated with an increase in the use of ambulatory care services by stroke and transient ischemic attack patients discharged home and an increased use of transitional care billing codes by ambulatory providers., Competing Interests: P.W.D. and C.D.B. report ownership interest in Care Directions, Inc.; Duncan is a research advisor for BQ Technologies. The remaining authors declare no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

We develop the scale transformed power prior for settings where historical and current data involve different data types, such as binary and continuous data. This situation arises often in clinical trials, for example, when historical data involve binary responses and the current data involve some other type of continuous or discrete outcome. The power prior, proposed by Ibrahim and Chen, does not address the issue of different data types. Herein, we develop a new type of power prior, which we call the scale transformed power prior (straPP). The straPP is constructed by transforming the power prior for the historical data by rescaling the parameter using a function of the Fisher information matrices for the historical and current data models, thereby shifting the scale of the parameter vector from that of the historical to that of the current data. Examples are presented to motivate the need for such a transformation, and simulation studies are presented to illustrate the performance advantages of the straPP over the power prior and other informative and noninformative priors. A real dataset from a clinical trial undertaken to study a novel transitional care model for stroke survivors is used to illustrate the methodology., (© 2022 John Wiley & Sons Ltd.)

For clinical trial design and analysis, there has been extensive work related to using joint models for longitudinal and time-to-event data without a cure fraction (i.e., when all patients are at risk for the event of interest), but comparatively little treatment has been given to design and analysis of clinical trials using joint models that incorporate a cure fraction. In this paper, we develop a Bayesian clinical trial design methodology focused on evaluating the treatment's effect on a time-to-event endpoint using a promotion time cure rate model, where the longitudinal process is incorporated into the hazard model for the promotion times. A piecewise linear hazard model for the period after assessment of the longitudinal measure ends is proposed as an alternative to extrapolating the longitudinal trajectory. This may be advantageous in scenarios where the period of time from the end of longitudinal measurements until the end of observation is substantial. Inference for the time-to-event endpoint is based on a novel estimand which combines the treatment's effect on the probability of cure and its effect on the promotion time distribution, mediated by the longitudinal outcome. We propose an approach for sample size determination such that the design has a high power and a well-controlled type I error rate with both operating characteristics defined from a Bayesian perspective. We demonstrate the methodology by designing a breast cancer clinical trial with a primary time-to-event endpoint where longitudinal outcomes are measured periodically during follow up., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

In clinical trials, it is common to have multiple clinical outcomes (e.g., coprimary endpoints or a primary and multiple secondary endpoints). It is often desirable to establish efficacy in at least one of multiple clinical outcomes, which leads to a multiplicity problem. In the frequentist paradigm, the most popular methods to correct for multiplicity are typically conservative. Moreover, despite guidance from regulators, it is difficult to determine the sample size of a future study with multiple clinical outcomes. In this article, we introduce a Bayesian methodology for multiple testing that asymptotically guarantees type I error control. Using a seemingly unrelated regression model, correlations between outcomes are specifically modeled, which enables inference on the joint posterior distribution of the treatment effects. Simulation results suggest that the proposed Bayesian approach is more powerful than the method of Holm (1979), which is commonly utilized in practice as a more powerful alternative to the ubiquitous Bonferroni correction. We further develop multivariate probability of success, a Bayesian method to robustly determine sample size in the presence of multiple outcomes., (© he Author 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

There has been increased interest in using prior information in statistical analyses. For example, in rare diseases, it can be difficult to establish treatment efficacy based solely on data from a prospective study due to low sample sizes. To overcome this issue, an informative prior to the treatment effect may be elicited. We develop a novel extension of the conjugate prior of Chen and Ibrahim (2003) that enables practitioners to elicit a prior prediction for the mean response for generalized linear models, treating the prediction as random. We refer to the hierarchical prior as the hierarchical prediction prior (HPP). For independent and identically distributed settings and the normal linear model, we derive cases for which the hyperprior is a conjugate prior. We also develop an extension of the HPP in situations where summary statistics from a previous study are available. The HPP allows for discounting based on the quality of individual level predictions, and simulation results suggest that, compared to the conjugate prior and the power prior, the HPP efficiency gains (e.g., lower mean squared error) where predictions are incompatible with the data. An efficient Monte Carlo Markov chain algorithm is developed. Applications illustrate that inferences under the HPP are more robust to prior-data conflict compared to selected nonhierarchical priors., (© The Author 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

We present a Bayesian framework for sequential monitoring that allows for use of external data, and that can be applied in a wide range of clinical trial applications. The basis for this framework is the idea that, in many cases, specification of priors used for sequential monitoring and the stopping criteria can be semi-algorithmic byproducts of the trial hypotheses and relevant external data, simplifying the process of prior elicitation. Monitoring priors are defined using the family of generalized normal distributions, which comprise a flexible class of priors, naturally allowing one to construct a prior that is peaked or flat about the parameter values thought to be most likely. External data are incorporated into the monitoring process through mixing an a priori skeptical prior with an enthusiastic prior using a weight that can be fixed or adaptively estimated. In particular, we introduce an adaptive monitoring prior for efficacy evaluation that dynamically weighs skeptical and enthusiastic prior components based on the degree to which observed data are consistent with an enthusiastic perspective. The proposed approach allows for prospective and pre-specified use of external data in the monitoring procedure. We illustrate the method for both single-arm and two-arm randomized controlled trials. For the latter case, we also include a retrospective analysis of actual trial data using the proposed adaptive sequential monitoring procedure. Both examples are motivated by completed pediatric trials, and the designs incorporate information from adult trials to varying degrees. Preposterior analysis and frequentist operating characteristics of each trial design are discussed.

Objectives: To examine the effect of a comprehensive transitional care model on the use of skilled nursing facility (SNF) and inpatient rehabilitation facility (IRF) care in the 12 months after acute care discharge home following stroke; and to identify predictors of experiencing a SNF or IRF admission following discharge home after stroke., Design: Cluster randomized pragmatic trial SETTING: Forty-one acute care hospitals in North Carolina., Participants: 2262 Medicare fee-for-service beneficiaries with transient ischemic attack or stroke discharged home. The sample was 80.3% White and 52.1% female, with a mean (SD) age of 74.9 (10.2) years and a mean ± SD National Institutes of Health stroke scale score of 2.3 (3.7)., Intervention: Comprehensive transitional care model (COMPASS-TC), which consisted of a 2-day follow-up phone call from the postacute care coordinator and 14-day in-person visit with the postacute care coordinator and advanced practice provider., Main Outcome Measures: Time to first SNF or IRF and SNF or IRF admission (yes/no) in the 12 months following discharge home. All analyses utilized multivariable mixed models including a hospital-specific random effect to account for the non-independence of measures within hospital. Intent to treat analyses using Cox proportional hazards regression assessed the effect of COMPASS-TC on time to SNF/IRF admission. Logistic regression was used to identify clinical and non-clinical predictors of SNF/IRF admission., Results: Only 34% of patients in the intervention arm received COMPASS-TC per protocol. COMPASS-TC was not associated with a reduced hazard of a SNF/ IRF admission in the 12 months post-discharge (hazard ratio, 1.20, with a range of 0.95-1.52) compared to usual care. This estimate was robust to additional covariate adjustment (hazard ratio, 1.23) (0.93-1.64). Both clinical and non-clinical factors (ie, insurance, geography) were predictors of SNF/IRF use., Conclusions: COMPASS-TC was not consistently incorporated into real-world clinical practice. The use of a comprehensive transitional care model for patients discharged home after stroke was not associated with SNF or IRF admissions in a 12-month follow-up period. Non-clinical factors predictive of SNF/IRF use suggest potential issues with access to this type of care., (Copyright © 2021 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

Joint models for longitudinal and time-to-event data are increasingly used for the analysis of clinical trial data. However, few methods have been proposed for designing clinical trials using these models. In this article, we develop a Bayesian clinical trial design methodology focused on evaluating the treatment's effect on the time-to-event endpoint using a flexible trajectory joint model. By incorporating the longitudinal outcome trajectory into the hazard model for the time-to-event endpoint, the joint modeling framework allows for non-proportional hazards (e.g., an increasing hazard ratio over time). Inference for the time-to-event endpoint is based on an average of a time-varying hazard ratio which can be decomposed according to the treatment's direct effect on the time-to-event endpoint and its indirect effect, mediated through the longitudinal outcome. We propose an approach for sample size determination for a trial such that the design has high power and a well-controlled type I error rate with both operating characteristics defined from a Bayesian perspective. We demonstrate the methodology by designing a breast cancer clinical trial with a primary time-to-event endpoint and where predictive longitudinal outcome measures are also collected periodically during follow-up., (© The Author 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background Mortality and hospital readmission rates may reflect the quality of acute and postacute stroke care. Our aim was to investigate if, compared with usual care (UC), the COMPASS-TC (Comprehensive Post-Acute Stroke Services Transitional Care) intervention (INV) resulted in lower all-cause and stroke-specific readmissions and mortality among patients with minor stroke and transient ischemic attack discharged from 40 diverse North Carolina hospitals from 2016 to 2018. Methods and Results Using Medicare fee-for-service claims linked with COMPASS cluster-randomized trial data, we performed intention-to-treat analyses for 30-day, 90-day, and 1-year unplanned all-cause and stroke-specific readmissions and all-cause mortality between INV and UC groups, with 90-day unplanned all-cause readmissions as the primary outcome. Effect estimates were determined via mixed logistic or Cox proportional hazards regression models adjusted for age, sex, race, stroke severity, stroke diagnosis, and documented history of stroke. The final analysis cohort included 1069 INV and 1193 UC patients (median age 74 years, 80% White, 52% women, 40% with transient ischemic attack) with median length of hospital stay of 2 days. The risk of unplanned all-cause readmission was similar between INV versus UC at 30 (9.9% versus 8.7%) and 90 days (19.9% versus 18.9%), respectively. No significant differences between randomization groups were seen in 1-year all-cause readmissions, stroke-specific readmissions, or mortality. Conclusions In this pragmatic trial of patients with complex minor stroke/transient ischemic attack, there was no difference in the risk of readmission or mortality with COMPASS-TC relative to UC. Our study could not conclusively determine the reason for the lack of effectiveness of the INV. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02588664.

Background: The COMprehensive Post-Acute Stroke Services study was a cluster-randomized pragmatic trial designed to evaluate a comprehensive care transitions model versus usual care. The data collected during this trial were complex and analysis methodology was required that could simultaneously account for the cluster-randomized design, missing patient-level covariates, outcome nonresponse, and substantial nonadherence to the intervention., Objective: The objective of this study was to discuss an array of complementary statistical methods to evaluate treatment effectiveness that appropriately addressed the challenges presented by the complex data arising from this pragmatic trial., Methods: We utilized multiple imputation combined with inverse probability weighting to account for missing covariate and outcome data in the estimation of intention-to-treat effects (ITT). The ITT estimand reflects the effectiveness of assignment to the COMprehensive Post-Acute Stroke Services intervention compared with usual care (ie, it does not take into account intervention adherence). Per-protocol analyses provide complementary information about the effect of treatment, and therefore are relevant for patients to inform their decision-making. We describe estimation of the complier average causal effect using an instrumental variables approach through 2-stage least squares estimation. For all preplanned analyses, we also discuss additional sensitivity analyses., Discussion: Pragmatic trials are well suited to inform clinical practice. Care should be taken to proactively identify the appropriate balance between control and pragmatism in trial design. Valid estimation of ITT and per-protocol effects in the presence of complex data requires application of appropriate statistical methods and concerted efforts to ensure high-quality data are collected., Competing Interests: R.B.D.: Ownership Interest, Care Directions. The remaining authors declare no conflict of interest., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)

To improve women's access to pre-exposure prophylaxis (PrEP) in family planning (FP) clinics, we examined readiness to provide PrEP, and barriers and facilitators at the clinic level to integrate PrEP services into Title X-funded FP clinics across the Southern US. Title X-funded FP clinics across DHHS regions III (Mid-Atlantic), IV (Southeast), and VI (Southwest), comprising the Southern US. From February to June, 2018, we conducted a web-based, geographically targeted survey of medical staff, providers and administrators of Title X-funded FP clinics in DHHS regions III (Mid-Atlantic), IV (Southeast), and VI (Southwest). Survey items were developed using the Consolidated Framework for Implementation Research to assess constructs relevant to PrEP implementation. One-fifth of 283 unique Title X clinics across the South provided PrEP. Readiness for PrEP implementation was positively associated with a climate supportive of HIV prevention, leadership engagement, and availability of resources, and negatively associated with providers holding negative attitudes about PrEP's suitability for FP. The Title X FP network is a vital source of sexual health care for millions of individuals across the US. Clinic-level barriers to providing PrEP must be addressed to expand onsite PrEP delivery in Title X FP clinics in the Southern US.

In this article, we develop a Bayesian adaptive design methodology for oncology basket trials with binary endpoints using a Bayesian model averaging framework. Most existing methods seek to borrow information based on the degree of homogeneity of estimated response rates across all baskets. In reality, an investigational product may only demonstrate activity for a subset of baskets, and the degree of activity may vary across the subset. A key benefit of our Bayesian model averaging approach is that it explicitly accounts for the possibility that any subset of baskets may have similar activity and that some may not. Our proposed approach performs inference on the basket-specific response rates by averaging over the complete model space for the response rates, which can include thousands of models. We present results that demonstrate that this computationally feasible Bayesian approach performs favorably compared to existing state-of-the-art approaches, even when held to stringent requirements regarding false positive rates., (© The Authors 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

A Data Monitoring Committee (DMC) evaluates patient safety in a clinical trial of an investigational intervention through periodic review of adverse events (AEs) and clinical safety assessments. Our aim was to construct DMC report displays to enhance the DMC safety review through use of graphics and clear identification and adjustment for missing data caused by early discontinuations and ongoing study participation. Suggested displays include a study snapshot graph, enhanced adverse event incidence tables including the incidence density and plotted incidence proportions, line graphs in place of by-patient listings, and trend plots in place of tables for continuous assessments.

We develop a novel two-stage Bayesian adaptive trial design for pediatric settings which borrows information from previously completed trials in adults to support establishing substantial evidence of efficacy for the pediatric population in situations where information extrapolation from adults is justifiable. At the time of the stage I analysis, the extent of information borrowing from adult data is determined by assessing compatibility of the observed pediatric data with its prior predictive distribution, derived using the adult trial data. At this time, the trial may be stopped for futility, enrollment may be stopped (with ongoing patients followed up for primary outcome ascertainment), or enrollment may proceed into stage II to reach a prespecified maximum sample size. We provide guidance on how practitioners can approach answering the question "How much information should be borrowed?" through balancing use of the adult data (when compatible with the pediatric data) with the need to ensure the design leads to reasonable recommendations regarding key actions that might be taken regarding the trial (e.g., when to stop early for efficacy). Type I error control is considered secondary to these considerations as type I error rate inflation above typical levels is unavoidable in these settings. We illustrate how the method can be applied using the Pediatric Lupus Trial of Belimumab Plus Background Standard Therapy as motivation.

Background: Adolescent and young adult women (AYAW), particularly racial and ethnic minorities, in the Southern United States are disproportionately affected by HIV. Pre-exposure prophylaxis (PrEP) is an effective, scalable, individual-controlled HIV prevention strategy that is grossly underutilized among women of all ages and requires innovative delivery approaches to optimize its benefit. Anchoring PrEP delivery to family planning (FP) services that AYAW already trust, access routinely, and deem useful for their sexual health may offer an ideal opportunity to reach women at risk for HIV and to enhance their PrEP uptake and adherence. However, PrEP has not been widely integrated into FP services, including Title X-funded FP clinics that provide safety net sources of care for AYAW. To overcome potential implementation challenges for AYAW, Title X clinics in the Southern United States are uniquely positioned to be focal sites for conceptually informed and thoroughly evaluated PrEP implementation science studies., Objective: The objective of this study is two-fold: to evaluate multilevel factors associated with the level of PrEP adoption and implementation (eg, PrEP screening, counseling, and prescription) within and across 3 FP clinics and to evaluate PrEP uptake, persistence, and adherence among female patients in these clinics over a 6-month follow-up period., Methods: Phase 2 of Planning4PrEP (Adolescent Medicine Trials Network for HIV/AIDS Interventions 155) is a mixed methods hybrid type 1 effectiveness implementation study to be conducted in three clinics in Metro Atlanta, Georgia, United States. Guided by the Exploration, Preparation, Implementation, and Sustainment framework, this study will prepare clinics for PrEP integration via clinic-wide trainings and technical assistance and will develop clinic-specific PrEP implementation plans. We will monitor and evaluate PrEP implementation as well as female patient PrEP uptake, persistence, and adherence over a 6-month follow-up period., Results: Phase 2 of Planning4PrEP research activities began in February 2018 and are ongoing. Qualitative data analysis is scheduled to begin in Fall 2020., Conclusions: This study seeks to evaluate factors associated with the level of PrEP adoption and implementation (eg, PrEP screening, counseling, and prescription) within and across 3 FP clinics following training and implementation planning and to evaluate PrEP uptake, persistence, and adherence among female patients over a 6-month follow-up period. This will guide future strategies to support PrEP integration in Title X-funded clinics across the Southern United States., Trial Registration: ClinicalTrials.gov NCT04097834; https://clinicaltrials.gov/ct2/show/NCT04097834., International Registered Report Identifier (irrid): DERR1-10.2196/18784., (©Anandi N Sheth, Sophia A Hussen, Cam Escoffery, Lisa B Haddad, Leah Powell, Nakita Brown, Teresa R Filipowicz, Micah McCumber, Maria Sanchez, Laura Renshaw, Matthew A Psioda, Jessica M Sales. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 25.09.2020.)

In this paper, we propose a Bayesian design framework for a biosimilars clinical program that entails conducting concurrent trials in multiple therapeutic indications to establish equivalent efficacy for a proposed biologic compared to a reference biologic in each indication to support approval of the proposed biologic as a biosimilar. Our method facilitates information borrowing across indications through the use of a multivariate normal correlated parameter prior (CPP), which is constructed from easily interpretable hyperparameters that represent direct statements about the equivalence hypotheses to be tested. The CPP accommodates different endpoints and data types across indications (eg, binary and continuous) and can, therefore, be used in a wide context of models without having to modify the data (eg, rescaling) to provide reasonable information-borrowing properties. We illustrate how one can evaluate the design using Bayesian versions of the type I error rate and power with the objective of determining the sample size required for each indication such that the design has high power to demonstrate equivalent efficacy in each indication, reasonably high power to demonstrate equivalent efficacy simultaneously in all indications (ie, globally), and reasonable type I error control from a Bayesian perspective. We illustrate the method with several examples, including designing biosimilars trials for follicular lymphoma and rheumatoid arthritis using binary and continuous endpoints, respectively., (© 2019 The International Biometric Society.)

Background The objectives of this study were to develop and test in real-world clinical practice the effectiveness of a comprehensive postacute stroke transitional care (TC) management program. Methods and Results The COMPASS study (Comprehensive Post-Acute Stroke Services) was a pragmatic cluster-randomized trial where the hospital was the unit of randomization. The intervention (COMPASS-TC) was initiated at 20 hospitals, and 20 hospitals provided their usual care. Hospital staff enrolled 6024 adult stroke and transient ischemic attack patients discharged home between 2016 and 2018. COMPASS-TC was patient-centered and assessed social and functional determinates of health to inform individualized care plans. Ninety-day outcomes were evaluated by blinded telephone interviewers. The primary outcome was functional status (Stroke Impact Scale-16); secondary outcomes were mortality, disability, medication adherence, depression, cognition, self-rated health, fatigue, care satisfaction, home blood pressure monitoring, and falls. The primary analysis was intention to treat. Of intervention hospitals, 58% had uninterrupted intervention delivery. Thirty-five percent of patients at intervention hospitals attended a COMPASS clinic visit. The primary outcome was measured for 59% of patients and was not significantly influenced by the intervention. Mean Stroke Impact Scale-16 (±SD) was 80.6±21.1 in TC versus 79.9±21.4 in usual care. Home blood pressure monitoring was self-reported by 72% of intervention patients versus 64% of usual care patients (adjusted odds ratio, 1.43 [95% CI, 1.21-1.70]). No other secondary outcomes differed. Conclusions Although designed according to the best available evidence with input from various stakeholders and consistent with Centers for Medicare and Medicaid Services TC policies, the COMPASS model of TC was not consistently incorporated into real-world health care. We found no significant effect of the intervention on functional status at 90 days post-discharge. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02588664.

We develop a Bayesian design method for a clinical program where an investigational product is to be studied concurrently in a set of clinical trials involving related diseases with the goal of demonstrating superiority to a control in each. The approach borrows information on treatment effectiveness using correlated mixture priors using an analysis procedure that is closely related Bayesian model averaging. Mixture priors are constructed by eliciting conjugate priors based on pessimistic and enthusiastic predictions for the data to be observed for each disease and then by eliciting mixture weights for all possible configurations of the pessimistic and enthusiastic priors across the diseases to be studied. The proposed approach provides a robust framework for information borrowing in settings where the diseases may have endpoints based on different data types. We show via simulation that operating characteristics based on the proposed design framework are favorable compared to those based on information borrowing designs using the Bayesian hierarchical model which is poorly suited for information borrowing when there are different data types underpinning the endpoints across which information is to be borrowed., (© The Author 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The COMprehensive Post-Acute Stroke Services (COMPASS) pragmatic trial compared the effectiveness of comprehensive transitional care (COMPASS-TC) versus usual care among stroke and transient ischemic attack (TIA) patients discharged home from North Carolina hospitals. We evaluated implementation of COMPASS-TC in 20 hospitals randomized to the intervention using the RE-AIM framework., Methods: We evaluated hospital-level Adoption of COMPASS-TC; patient Reach (meeting transitional care management requirements of timely telephone and face-to-face follow-up); Implementation using hospital quality measures (concurrent enrollment, two-day telephone follow-up, 14-day clinic visit scheduling); and hospital-level sustainability (Maintenance). Effectiveness compared 90-day physical function (Stroke Impact Scale-16), between patients receiving COMPASS-TC versus not. Associations between hospital and patient characteristics with Implementation and Reach measures were estimated with mixed logistic regression models., Results: Adoption: Of 95 eligible hospitals, 41 (43%) participated in the trial. Of the 20 hospitals randomized to the intervention, 19 (95%) initiated COMPASS-TC. Reach: A total of 24% (656/2751) of patients enrolled received a billable TC intervention, ranging from 6 to 66% across hospitals., Implementation: Of eligible patients enrolled, 75.9% received two-day calls (or two attempts) and 77.5% were scheduled/offered clinic visits. Most completed visits (78% of 975) occurred within 14 days. Effectiveness: Physical function was better among patients who attended a 14-day visit versus those who did not (adjusted mean difference: 3.84, 95% CI 1.42-6.27, p = 0.002). Maintenance: Of the 19 adopting hospitals, 14 (74%) sustained COMPASS-TC., Conclusions: COMPASS-TC implementation varied widely. The greatest challenge was reaching patients because of system difficulties maintaining consistent delivery of follow-up visits and patient preferences to pursue alternate post-acute care. Receiving COMPASS-TC was associated with better functional status., Trial Registration: ClinicalTrials.gov number: NCT02588664. Registered 28 October 2015.

We consider the problem of Bayesian sample size determination for a clinical trial in the presence of historical data that inform the treatment effect. Our broadly applicable, simulation-based methodology provides a framework for calibrating the informativeness of a prior while simultaneously identifying the minimum sample size required for a new trial such that the overall design has appropriate power to detect a non-null treatment effect and reasonable type I error control. We develop a comprehensive strategy for eliciting null and alternative sampling prior distributions which are used to define Bayesian generalizations of the traditional notions of type I error control and power. Bayesian type I error control requires that a weighted-average type I error rate not exceed a prespecified threshold. We develop a procedure for generating an appropriately sized Bayesian hypothesis test using a simple partial-borrowing power prior which summarizes the fraction of information borrowed from the historical trial. We present results from simulation studies that demonstrate that a hypothesis test procedure based on this simple power prior is as efficient as those based on more complicated meta-analytic priors, such as normalized power priors or robust mixture priors, when all are held to precise type I error control requirements. We demonstrate our methodology using a real data set to design a follow-up clinical trial with time-to-event endpoint for an investigational treatment in high-risk melanoma., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Black adolescent and young adult women (AYAW) in the Southern United States are disproportionately affected by HIV. Pre-exposure prophylaxis (PrEP) is an effective, scalable, individual-controlled HIV prevention strategy that is grossly underutilized among women of all ages and requires innovative delivery approaches to optimize its benefit. Anchoring PrEP delivery to health services that AYAW already trust, access routinely, and deem useful for their sexual health may offer an ideal opportunity to reach women at risk for HIV and to enhance their PrEP uptake and adherence. These services include those of family planning (FP) providers in high HIV incidence settings. However, PrEP has not been widely integrated into FP services, including Title X-funded FP clinics that provide safety net sources of care for AYAW. To overcome potential implementation challenges for AYAW, Title X clinics in the Southern United States are uniquely positioned to be focal sites for conceptually informed and thoroughly evaluated PrEP implementation science studies., Objective: The aim of this study is to assess inner and outer context factors (barriers and facilitators) that may influence the adoption of PrEP prescription and treatment services in Title X clinics serving AYAW in the Southern United States., Methods: Phase 1 of Planning4PrEP is an explanatory sequential, mixed methods study consisting of a geographically-targeted Web-based survey of Title X clinic administrators and providers in the Southern United States, followed by key informant interviews among a purposively selected subset of responders to more comprehensively assess inner and outer context factors that may influence adoption and implementation of PrEP in Title X FP clinics in the South., Results: Phase 1 of Planning4PrEP research activities began in October 2017 and are ongoing. To date, survey and key informant interview administration is near completion, with quantitative and qualitative data analysis scheduled to begin soon after data collection completion., Conclusions: This study seeks to assess inner and outer contextual factors (barriers and facilitators) that may influence the adoption and integration of PrEP prescription and treatment services in Title X clinics serving AYAW in the Southern United States. Data gained from this study will inform a type 1 hybrid effectiveness implementation study, which will evaluate the multilevel factors associated with successful PrEP implementation while evaluating the degree of PrEP uptake, continuation, and adherence among women seen in Title X clinics., International Registered Report Identifier (irrid): DERR1-10.2196/12774., (©Jessica M Sales, Cam Escoffery, Sophia A Hussen, Lisa B Haddad, Ashley Phillips, Teresa Filipowicz, Maria Sanchez, Micah McCumber, Betty Rupp, Evan Kwiatkowski, Matthew A Psioda, Anandi N Sheth. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 06.05.2019.)

Background: The Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) research program aims to defeat the rising HIV epidemic among adolescents and young adults in the United States., Objective: This study aims to optimize cross-study analyses and comparisons of standardized measures (variables) collected in the ATN., Methods: Guidelines were developed for harmonizing measures to be collected across ATN studies., Results: Eight domains were identified for harmonization-Demographics and Socioeconomic Characteristics, Sexual Behavior and Risk, Substance Use and Abuse, HIV-Positive Cascade, HIV-Negative Cascade, Mental Health, Social Support and Isolation, and Pre-exposure Prophylaxis Cascade., Conclusions: The collection of selected key measures in a uniform manner across studies facilitates the characterization of participant populations, comparisons between studies, and pooled analysis of data from multiple studies., (©Micah McCumber, Demetria Cain, Sara LeGrand, Kenneth H Mayer, Debra A Murphy, Matthew A Psioda, Arlene C Seña, Tyrel J Starks, Michael Hudgens. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 21.12.2018.)

In this paper, we develop the fixed-borrowing adaptive design, a Bayesian adaptive design which facilitates information borrowing from a historical trial using subject-level control data while assuring a reasonable upper bound on the maximum type I error rate and lower bound on the minimum power. First, one constructs an informative power prior from the historical data to be used for design and analysis of the new trial. At an interim analysis opportunity, one evaluates the degree of prior-data conflict. If there is too much conflict between the new trial data and the historical control data, the prior information is discarded and the study proceeds to the final analysis opportunity at which time a noninformative prior is used for analysis. Otherwise, the trial is stopped early and the informative power prior is used for analysis. Simulation studies are used to calibrate the early stopping rule. The proposed design methodology seamlessly accommodates covariates in the statistical model, which the authors argue is necessary to justify borrowing information from historical controls. Implementation of the proposed methodology is straightforward for many common data models, including linear regression models, generalized linear regression models, and proportional hazards models. We demonstrate the methodology to design a cardiovascular outcomes trial for a hypothetical new therapy for treatment of type 2 diabetes mellitus and borrow information from the SAVOR trial, one of the earliest cardiovascular outcomes trials designed to assess cardiovascular risk in antidiabetic therapies., (© 2018 John Wiley & Sons, Ltd.)

In this paper, we develop a general Bayesian clinical trial design methodology, tailored for time-to-event trials with a cured fraction in scenarios where a previously completed clinical trial is available to inform the design and analysis of the new trial. Our methodology provides a conceptually appealing and computationally feasible framework that allows one to construct a fixed, maximally informative prior a priori while simultaneously identifying the minimum sample size required for the new trial so that the design has high power and reasonable type I error control from a Bayesian perspective. This strategy is particularly well suited for scenarios where adaptive borrowing approaches are not practical due to the nature of the trial, complexity of the model, or the source of the prior information. Control of a Bayesian type I error rate offers a sensible balance between wanting to use high-quality information in the design and analysis of future trials while still controlling type I errors in an equitable way. Moreover, sample size determination based on our Bayesian view of power can lead to a more adequately sized trial by virtue of taking into account all the uncertainty in the treatment effect. We demonstrate our methodology by designing a cancer clinical trial in high-risk melanoma., (© 2018 John Wiley & Sons, Ltd.)

Background: Accessing the emergency medical services system via 9-1-1 operators is an effective way for patients to seek urgent health care; however, technological advances and telecommunication practices inundate the 9-1-1 and emergency services infrastructure with unintentional calls that delay response efforts to legitimate medical emergencies., Objective: To determine whether the change in university-wide dial-out prefix from "9" to "7" reduced unnecessary calls to a 9-1-1 call center., Methods: This is a retrospective study conducted utilizing information obtained from the University of North Carolina at Chapel Hill (UNC) Department of Public Safety (DPS) call center. Call center calls received during pre-change, intervening, and post-change periods were included in the study. The cost savings, defined in time and money, resulting from the prefix change were also examined., Results: A total of 33,646 calls were made during the study period (January 11, 2010 through December 31, 2012) and included in the analysis. The prefix change was found to reduce the rate of invalid calls to the call center by 319 calls per month, resulting in a 43% reduction in total calls to the call center while preserving the rate of valid calls. The largest decrease occurred in hang-up calls (a decrease of 232 calls per month), especially those originating from the university. The prefix change was found to save the UNC DPS telecommunications division approximately $798.82 per month and the police officer division approximately $3,874.95 per month., Conclusion: A prefix change was not only beneficial to the UNC community but it also has potentially wide-reaching effects. A reduction of invalid 9-1-1 calls translates to telecommunicators having more time available to handle true emergencies, phone lines remaining available for true emergencies, and police officers dedicating more time and effort to matters that necessitate officer assistance. Based on the call decrease seen with the prefix change, this study may be used as evidence to advocate for a change of dial-out codes beginning with "9."

Background Patient-reported outcomes (PROs) are clinical tools that measure patients' goals of care and assess patient-reported physical, mental, and social well-being. Despite their value in advancing patient-centered care, routine use of PROs in stroke management has lagged. As part of the pragmatic COMPASS (Comprehensive Post-Acute Stroke Services) trial, we developed COMPASS-Care Plan (CP), a clinician-facing application that captures and analyzes PROs for stroke and transient ischemic attack patients discharged home and immediately generates individualized electronic CP. In this report, we (1) present our methods for developing and implementing COMPASS-CP PROs, (2) provide examples of CP generated from COMPASS-CP, (3) describe key functional, social, and behavioral determinants of health captured by COMPASS-CP, and (4) report on clinician experience with using COMPASS-CP in routine clinical practice for care planning and engagement of stroke and transient ischemic attack patients discharged home. Methods and Results We report on the first 871 patients enrolled in 20 North Carolina hospitals randomized to the intervention arm of COMPASS between July 2016 and February 2018; these patients completed a COMPASS follow-up visit within 14 days of hospital discharge. We also report user satisfaction results from 56 clinicians who used COMPASS-CP during these visits. COMPASS-CP identified more cognitive and depression deficits than physical deficits. Within 14-day posthospitalization, less than half of patients could list the major risk factors for stroke, 36% did not recognize blood pressure as a stroke risk factor, and 19% of patients were nonadherent with prescribed medications. Three-fourths of clinicians reported that COMPASS-CP identifies important factors impacting patients' recovery that they otherwise may have missed, and two-thirds were highly satisfied with COMPASS-CP. Conclusions The COMPASS-CP application meets an immediate need to incorporate PROs into the clinical workflow to develop patient-centered CP for stroke patients and has high user satisfaction. Clinical Trial Registration URL: https://www.clinicaltrials.gov . Unique identifier: NCT02588664.