The Back Pain Consortium (BACPAC) Research Program: Structure, Research Priorities, and Methods.

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 Precision Interventions for Severe and/or Exacerbation-Prone (PrecISE) Asthma Network: An overview of Network organization, procedures, and interventions.

Asthma is a heterogeneous disease, with multiple underlying inflammatory pathways and structural airway abnormalities that impact disease persistence and severity. Recent progress has been made in developing targeted asthma therapeutics, especially for subjects with eosinophilic asthma. However, there is an unmet need for new approaches to treat patients with severe and exacerbation-prone asthma, who contribute disproportionately to disease burden. Extensive deep phenotyping has revealed the heterogeneous nature of severe asthma and identified distinct disease subtypes. A current challenge in the field is to translate new and emerging knowledge about different pathobiologic mechanisms in asthma into patient-specific therapies, with the ultimate goal of modifying the natural history of disease. Here, we describe the Precision Interventions for Severe and/or Exacerbation-Prone Asthma (PrecISE) Network, a groundbreaking collaborative effort of asthma researchers and biostatisticians from around the United States. The PrecISE Network was designed to conduct phase II/proof-of-concept clinical trials of precision interventions in the population with severe asthma, and is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health. Using an innovative adaptive platform trial design, the PrecISE Network will evaluate up to 6 interventions simultaneously in biomarker-defined subgroups of subjects. We review the development and organizational structure of the PrecISE Network, and choice of interventions being studied. We hope that the PrecISE Network will enhance our understanding of asthma subtypes and accelerate the development of therapeutics for severe asthma.

Abatacept, Cenicriviroc, or Infliximab for Treatment of Adults Hospitalized With COVID-19 Pneumonia: A Randomized Clinical Trial.

Importance: Immune dysregulation contributes to poorer outcomes in COVID-19. Objective: To investigate whether abatacept, cenicriviroc, or infliximab provides benefit when added to standard care for COVID-19 pneumonia. Design, Setting, and Participants: Randomized, double-masked, placebo-controlled clinical trial using a master protocol to investigate immunomodulators added to standard care for treatment of participants hospitalized with COVID-19 pneumonia. The results of 3 substudies are reported from 95 hospitals at 85 clinical research sites in the US and Latin America. Hospitalized patients 18 years or older with confirmed SARS-CoV-2 infection within 14 days and evidence of pulmonary involvement underwent randomization between October 2020 and December 2021. Interventions: Single infusion of abatacept (10 mg/kg; maximum dose, 1000 mg) or infliximab (5 mg/kg) or a 28-day oral course of cenicriviroc (300-mg loading dose followed by 150 mg twice per day). Main Outcomes and Measures: The primary outcome was time to recovery by day 28 evaluated using an 8-point ordinal scale (higher scores indicate better health). Recovery was defined as the first day the participant scored at least 6 on the ordinal scale. Results: Of the 1971 participants randomized across the 3 substudies, the mean (SD) age was 54.8 (14.6) years and 1218 (61.8%) were men. The primary end point of time to recovery from COVID-19 pneumonia was not significantly different for abatacept (recovery rate ratio [RRR], 1.12 [95% CI, 0.98-1.28]; P = .09), cenicriviroc (RRR, 1.01 [95% CI, 0.86-1.18]; P = .94), or infliximab (RRR, 1.12 [95% CI, 0.99-1.28]; P = .08) compared with placebo. All-cause 28-day mortality was 11.0% for abatacept vs 15.1% for placebo (odds ratio [OR], 0.62 [95% CI, 0.41-0.94]), 13.8% for cenicriviroc vs 11.9% for placebo (OR, 1.18 [95% CI 0.72-1.94]), and 10.1% for infliximab vs 14.5% for placebo (OR, 0.59 [95% CI, 0.39-0.90]). Safety outcomes were comparable between active treatment and placebo, including secondary infections, in all 3 substudies. Conclusions and Relevance: Time to recovery from COVID-19 pneumonia among hospitalized participants was not significantly different for abatacept, cenicriviroc, or infliximab vs placebo. Trial Registration: ClinicalTrials.gov Identifier: NCT04593940.

Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV): Designing Master Protocols for Evaluation of Candidate COVID-19 Therapeutics.

Working in an unprecedented time frame, the Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) public-private partnership developed and launched 9 master protocols between 14 April 2020 and 31 May 2021 to allow for the coordinated and efficient evaluation of multiple investigational therapeutic agents for COVID-19. The ACTIV master protocols were designed with a portfolio approach to serve the following patient populations with COVID-19: mild to moderately ill outpatients, moderately ill inpatients, and critically ill inpatients. To facilitate the execution of these studies and minimize start-up time, ACTIV selected several existing networks to launch the master protocols. The master protocols were also designed to test several agent classes prioritized by ACTIV that covered the spectrum of the disease pathophysiology. Each protocol, either adaptive or pragmatic, was designed to efficiently select those treatments that provide benefit to patients while rapidly eliminating those that were either ineffective or unsafe. The ACTIV Therapeutics-Clinical Working Group members describe the process by which these master protocols were designed, developed, and launched. Lessons learned that may be useful in meeting the challenges of a future pandemic are also described.

PrecISE: Precision Medicine in Severe Asthma: An adaptive platform trial with biomarker ascertainment.

Severe asthma accounts for almost half the cost associated with asthma. Severe asthma is driven by heterogeneous molecular mechanisms. Conventional clinical trial design often lacks the power and efficiency to target subgroups with specific pathobiological mechanisms. Furthermore, the validation and approval of new asthma therapies is a lengthy process. A large proportion of that time is taken by clinical trials to validate asthma interventions. The National Institutes of Health Precision Medicine in Severe and/or Exacerbation Prone Asthma (PrecISE) program was established with the goal of designing and executing a trial that uses adaptive design techniques to rapidly evaluate novel interventions in biomarker-defined subgroups of severe asthma, while seeking to refine these biomarker subgroups, and to identify early markers of response to therapy. The novel trial design is an adaptive platform trial conducted under a single master protocol that incorporates precision medicine components. Furthermore, it includes innovative applications of futility analysis, cross-over design with use of shared placebo groups, and early futility analysis to permit more rapid identification of effective interventions. The development and rationale behind the study design are described. The interventions chosen for the initial investigation and the criteria used to identify these interventions are enumerated. The biomarker-based adaptive design and analytic scheme are detailed as well as special considerations involved in the final trial design.

The precision interventions for severe and/or exacerbation-prone asthma (PrecISE) adaptive platform trial: statistical considerations.

The Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) study is an adaptive platform trial designed to investigate novel interventions to severe asthma. The study is conducted under a master protocol and utilizes a crossover design with each participant receiving up to five interventions and at least one placebo. Treatment assignments are based on the patients' biomarker profiles and precision health methods are incorporated into the interim and final analyses. We describe key elements of the PrecISE study including the multistage adaptive enrichment strategy, early stopping of an intervention for futility, power calculations, and the primary analysis strategy.

The changing landscape of data monitoring committees-Perspectives from regulators, members, and sponsors.

Data Monitoring Committees (DMCs) are an integral part of clinical drug development. Their use has evolved along with changing study designs and regulatory expectations, which has associated statistical and ethical implications. Although there is guidance from the different regulatory agencies, there are opportunities to bring more consistency to address practical issues of establishing and operating a DMC. Challenging issues include defining the scope of DMC decisions, the regulatory requirements and expectations, the perceived independence of DMCs, the specific focus primarily on safety, etc. Wider use of adaptive clinical trial designs in recent years introduce additional challenges in terms of trial governance and the complexity of DMC activities. A panel comprised of clinical and statistical experts from across academia, industry, and regulatory agencies shared their experience and thoughts on the importance of these aspects and offered perspectives on the future of the DMCs. This paper documents the thinking from the panel session at the CEN-ISBS conference held in Vienna, Austria, 2017.

Spirometry Reference Equations from the HCHS/SOL (Hispanic Community Health Study/Study of Latinos).

RATIONALE: Accurate reference values for spirometry are important because the results are used for diagnosing common chronic lung diseases such as asthma and chronic obstructive pulmonary disease, estimating physiologic impairment, and predicting all-cause mortality. Reference equations have been established for Mexican Americans but not for others with Hispanic/Latino backgrounds. OBJECTIVES: To develop spirometry reference equations for adult Hispanic/Latino background groups in the United States. METHODS: The HCHS/SOL (Hispanic Community Health Study/Study of Latinos) recruited a population-based probability sample of 16,415 Hispanics/Latinos aged 18-74 years living in the Bronx, Chicago, Miami, and San Diego. Participants self-identified as being of Puerto Rican, Cuban, Dominican, Mexican, or Central or South American background. Spirometry was performed using standardized methods with central quality control monitoring. Spirometric measures from a subset of 6,425 never-smoking participants without respiratory symptoms or disease were modeled as a function of sex, age, height, and Hispanic/Latino background to produce background-specific reference equations for the predicted value and lower limit of normal. MEASUREMENTS AND MAIN RESULTS: Dominican and Puerto Rican Americans had substantially lower predicted and lower limit of normal values for FVC and FEV1 than those in other Hispanic/Latino background groups and also than Mexican American values from NHANES III (Third National Health and Nutrition Examination Survey). CONCLUSIONS: For patients of Dominican and Puerto Rican background who present with pulmonary symptoms in clinical practice, use of background-specific spirometry reference equations may provide more appropriate predicted and lower limit of normal values, enabling more accurate diagnoses of abnormality and physiologic impairment.

Pulmonary Disease and Age at Immigration among Hispanics. Results from the Hispanic Community Health Study/Study of Latinos.

RATIONALE: Asthma has been reported to be more prevalent among Hispanics of Puerto Rican heritage than among other Hispanics and among Hispanics born in the United States or who immigrated as children than among those who came as adults; however, direct comparisons across Hispanic groups are lacking. OBJECTIVES: To test whether asthma is more prevalent among Hispanics of Puerto Rican heritage than among other Hispanic groups, whether asthma is associated with age of immigration, and whether chronic obstructive pulmonary disease varies by heritage in a large, population-based cohort of Hispanics in the United States. METHODS: The Hispanic Community Health Study/Study of Latinos researchers recruited a population-based probability sample of 16,415 Hispanics/Latinos, 18-74 years of age, in New York City, Chicago, Miami, and San Diego. Participants self-reported Puerto Rican, Cuban, Dominican, Mexican, Central American, or South American heritage; birthplace; and, if relevant, age at immigration. A respiratory questionnaire and standardized spirometry were performed with post-bronchodilator measures for those with airflow limitation. MEASUREMENTS AND MAIN RESULTS: The prevalence of physician-diagnosed asthma among Puerto Ricans (36.5%; 95% confidence interval, 33.6-39.5%) was higher than among other Hispanics (odds ratio, 3.9; 95% confidence interval, 3.3-4.6). Hispanics who were born in the mainland United States or had immigrated as children had a higher asthma prevalence than those who had immigrated as adults (19.6, 19.4, and 14.1%, respectively; P < 0.001). Current asthma, bronchodilator responsiveness, and wheeze followed similar patterns. Chronic obstructive pulmonary disease prevalence was higher among Puerto Ricans (14.1%) and Cubans (9.8%) than among other Hispanics (<6.0%), but it did not vary across Hispanic heritages after adjustment for smoking and prior asthma (P = 0.22), by country of birth, or by age at immigration. CONCLUSIONS: Asthma was more prevalent among Puerto Ricans, other Hispanics born in the United States, and those who had immigrated as children than among other Hispanics. In contrast, the higher prevalence of chronic obstructive pulmonary disease among Puerto Ricans and Cubans was largely reflective of differential smoking patterns and asthma.

A regulatory perspective on missing data in the aftermath of the NRC report.

The issuance of a report in 2010 by the National Research Council (NRC) of the National Academy of Sciences entitled 'The Prevention and Treatment of Missing Data in Clinical Trials,' commissioned by the US Food and Drug Administration, had an immediate impact on the way that statisticians and clinical researchers in both industry and regulatory agencies think about the missing data problem. We believe that there is currently great potential to improve study quality and interpretability-by reducing the amount of missing data through changes in trial design and conduct and by planning and conducting analyses that better account for the missing information. Here, we describe our view on some of the recommendations in the report and suggest ways in which these recommendations can be incorporated into new or ongoing clinical trials in order to improve their chance of success. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Flexible shrinkage estimation of subgroup effects through Dirichlet process priors.

The paradigm shift towards precision medicine reignited interest in determining whether there are differential treatment effects in subgroups of trial participants. Intrinsic to this problem is that any assessment of a differential treatment effect is predicated on being able to estimate the treatment response accurately while satisfying constraints of balancing the risk of overlooking an important subgroup with the potential to make a decision based on a false discovery. While shrinkage models have been widely used to improve accuracy of subgroup parameter estimates by leveraging the relationship between them, there is still a possibility that it can lead to excessively conservative or anti-conservative results. This can possibly be due to the use of the normal distribution as prior, which forces outlying subjects to have their means over-shrunk towards the population mean, and the data from such subjects may be excessively influential in estimation of both the overall mean response and the mean response for each subgroup, or a model misspecification due to unaccounted variation or clustering. To address this issue, we investigate the use of nonparametric Bayes, particularly Dirichlet process priors, to create a flexible shrinkage model. This model represents uncertainty in the prior distribution for the overall response while accommodating heterogeneity among individual subgroups. We simulated data to compare estimates when there is no differential subgroup effect and when there is a differential subgroup effect. In either of these scenarios, the flexible shrinkage model does not force estimates to shrink excessively when similarity of treatment effects is not supported but still retains the attractiveness of improved precision given by the narrower credible intervals. We also applied the same method to a dataset based on trials conducted for an antimicrobial therapy on several related indications.

Design of the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS).

Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS) is a multicentre observational study of chronic obstructive pulmonary disease (COPD) designed to guide future development of therapies for COPD by providing robust criteria for subclassifying COPD participants into groups most likely to benefit from a given therapy during a clinical trial, and identifying biomarkers/phenotypes that can be used as intermediate outcomes to reliably predict clinical benefit during therapeutic trials. The goal is to enrol 3200 participants in four strata. Participants undergo a baseline visit and three annual follow-up examinations, with quarterly telephone calls. Adjudication of exacerbations and mortality will be undertaken.

Bayesian approach to the design and analysis of non-inferiority trials for anti-infective products.

In the absence of placebo-controlled trials, determining the non-inferiority (NI) margin for comparing an experimental treatment with an active comparator is based on carefully selected well-controlled historical clinical trials. With this approach, information on the effect of the active comparator from other sources including observational studies and early phase trials is usually ignored because of the need to maintain active comparator effect across trials. This may lead to conservative estimates of the margin that translate into larger sample-size requirements for the design and subsequent frequentist analysis, longer trial durations, and higher drug development costs. In this article, we provide methodological approaches to determine NI margins that can utilize all relevant historical data through a novel power adjusted Bayesian meta-analysis, with Dirichlet process priors, that puts ordered weights on the amount of information a set of data contributes. We also provide a Bayesian decision rule for the non-inferiority analysis that is based on a broader use of available prior information and a sample-size determination that is based on this Bayesian decision rule. Finally, the methodology is illustrated through several examples.

Efficacy of Infliximab, Abatacept, and Cenicriviroc for the Treatment of Adults Hospitalized with COVID-19 Pneumonia.

A randomized, double-blind, placebo-controlled clinical trial was conducted to investigate the efficacy of infliximab, abatacept, and cenicriviroc in treating patients hospitalized with COVID-19. The patient's clinical status was assessed daily on an 8-point ordinal scale. We evaluated the totality of evidence on the efficacy of the 3 immunomodulators by considering all possible changes in the clinical status of each patient over time. We demonstrated that infliximab accelerated improvement and reduced deterioration of clinical status when added to standard of care. There was also evidence for the benefit of abatacept. There was no evidence for the benefit of cenicriviroc.

Medical records-based postmarketing safety evaluation of rare events with uncertain status.

We develop a simple statistic for comparing rates of rare adverse events between treatment groups in postmarketing safety studies where the events have uncertain status. In this setting, the statistic is asymptotically equivalent to the logrank statistic, but the limiting distribution has Poisson and binomial components instead of being Gaussian. We develop two new procedures for computing critical values, a Gaussian approximation and a parametric bootstrap. Both numerical and asymptotic properties of the procedures are studied. The test procedures are demonstrated on a postmarketing safety study of the RotaTeq vaccine. This vaccine was developed to reduce the incidence of severe diarrhea in infants.

Medical records-based postmarketing safety evaluation of rare events with uncertain status.

We develop a simple statistic for comparing rates of rare adverse events between treatment groups in postmarketing safety studies where the events have uncertain status. In this setting, the statistic is asymptotically equivalent to the logrank statistic, but the limiting distribution has Poisson and binomial components instead of being Gaussian. We develop two new procedures for computing critical values: a Gaussian approximation and a parametric bootstrap. Both numerical and asymptotic properties of the procedures are studied. The test procedures are demonstrated on a postmarketing safety study of the RotaTeq vaccine. This vaccine was developed to reduce the incidence of severe diarrhea in infants.