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.

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.

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.

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.

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.

Advancing precision medicine for acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome. Understanding of the complex pathways involved in lung injury pathogenesis, resolution, and repair has grown considerably in recent decades. Nevertheless, to date, only therapies targeting ventilation-induced lung injury have consistently proven beneficial, and despite these gains, ARDS morbidity and mortality remain high. Many candidate therapies with promise in preclinical studies have been ineffective in human trials, probably at least in part due to clinical and biological heterogeneity that modifies treatment responsiveness in human ARDS. A precision medicine approach to ARDS seeks to better account for this heterogeneity by matching therapies to subgroups of patients that are anticipated to be most likely to benefit, which initially might be identified in part by assessing for heterogeneity of treatment effect in clinical trials. In October 2019, the US National Heart, Lung, and Blood Institute convened a workshop of multidisciplinary experts to explore research opportunities and challenges for accelerating precision medicine in ARDS. Topics of discussion included the rationale and challenges for a precision medicine approach in ARDS, the roles of preclinical ARDS models in precision medicine, essential features of cohort studies to advance precision medicine, and novel approaches to clinical trials to support development and validation of a precision medicine strategy. In this Position Paper, we summarise workshop discussions, recommendations, and unresolved questions for advancing precision medicine in ARDS. Although the workshop took place before the COVID-19 pandemic began, the pandemic has highlighted the urgent need for precision therapies for ARDS as the global scientific community grapples with many of the key concepts, innovations, and challenges discussed at this workshop.

Statistical projection of clinical subsample estimates to a survey population.

BACKGROUND: The goal of public health research often involves estimating clinical outcomes for a broad target population. The gold standard for the basis of such inference is a nationally representative survey that includes a clinical component. The cost of this gold standard can be prohibitive, and alternative approaches are needed. We propose a statistical methodology for projecting estimates from a clinical subsample to a larger survey population that has utility in periodontal research. METHODS: The statistical methodology consists of fitting prediction models to clinical outcome variables that are available only for a subset of the survey population. Variables measured on the larger survey population are included in the model as predictors. The resulting prediction equations are applied to the entire survey population to produce estimates of prevalence for the clinical outcomes of interest. Methods for computing variance estimates for the model-based predictions also are proposed. RESULTS: This projection methodology was developed originally for use with a nationally representative sample of the veteran population in the United States as part of the National Vietnam Veterans Readjustment Study. Details of the projection methodology and illustration of its use are provided. Issues associated with the application of this methodology to periodontal research are discussed. CONCLUSIONS: The proposed projection methodology supports valid inference about clinical outcomes to a broad population using data from a more narrowly defined clinical subsample. This approach is useful in addressing important public health questions until such time as a nationally representative clinical study can be undertaken.

Double-blind switch study of imipramine or sertraline treatment of antidepressant-resistant chronic depression.

BACKGROUND: Although various strategies have been proposed to treat antidepressant nonresponders, little controlled research has been published that examines prospectively the use of switching to an alternate antidepressant. METHODS: This was a multisite study in which outpatients with chronic major depression (with or without concurrent dysthymia), who failed to respond to 12 weeks of double-blind treatment with either sertraline hydrochloride (n = 117) or imipramine hydrochloride (n = 51), were crossed over or switched to 12 additional weeks of double-blind treatment with the alternate medication. Outcome measures included the 24-item Hamilton Rating Scale for Depression and the Clinical Global Impressions--Severity and Improvement scales. RESULTS: The switch from sertraline to imipramine (mean dosage, 221 mg/d) and from imipramine to sertraline (mean dosage, 163 mg/d) resulted in clinically and statistically significant improvements. The switch to sertraline treatment was associated with fewer adverse effect complaints and significantly less attrition owing to adverse effects. Although sertraline treatment also resulted in significantly higher response rates in the intent-to-treat samples (60% in the sertraline group and 44% in the imipramine group), neither the intent-to-treat remission rates nor the response and remission rates among study completers differed significantly. Moreover, after considering the effect of attrition, there were no significant treatment effects on the more comprehensive generalized estimating equation analyses of the continuous dependent measures. CONCLUSIONS: More than 50% of chronically depressed antidepressant nonresponders benefited from a switch from imipramine to sertraline, or vice versa, despite a high degree of chronicity. As in the initial trial, sertraline was generally better tolerated than imipramine. Switching to a standard antidepressant of a different class is a useful treatment strategy for antidepressant nonresponders and could be considered a standard of comparison for future studies of novel alternate strategies.

Randomization-based nonparametric methods for the analysis of multicentre trials.

Multicentre trials offer several advantages over single centre trials in clinical research, including the ability to recruit patients at a faster rate over the course of the study, increased generalizability through the use of a broader patient population, and the ability to shed light on the replication of findings at multiple centres in a single study. A nonparametric approach to the analysis of multicentre trial data provides a convenient way for addressing the role of centres as well as baseline covariables during data analysis. With the use of randomization-based nonparametric methods, the strategy for evaluating the null hypothesis of no treatment effect can be prespecified during study planning without requiring a specific structure for the relationship of response criteria (or endpoints) to centres, covariables, or potential interaction terms. Further, the basis of inference for the application of these methods is the randomization mechanism, and the population to which inference can be directly made is the study population itself. No assumptions about underlying distributions, data structures, likelihood functions, or samples from super populations of inference are required. A three-step approach is proposed for handling centres via randomization-based nonparametric methods. In Step 1, a test of overall treatment effect is carried out using data from all centres simultaneously, without any assumption about treatment by centre interaction. In Step 2, the question of treatment by centre interaction is addressed, usually through the use of parametric multiple regression methods. In cases with suggestion of such interaction, Step 3 is conducted to evaluate different weighting schemes in forming pairwise treatment comparisons averaged across centres to assess the robustness of treatment effects observed in Step 1. An attractive inferential feature of this three-step approach is that the Type I error for the test of treatment effect is controlled by requiring statistical significance at each step to proceed to the next step. Extended Mantel-Haenszel methods with stratification adjustment for centre can be used to provide a nonparametric assessment of treatment effect. When adjustment for other covariates, such as baseline values, is desired, the more recent nonparametric analysis of covariance methods are available. Both methods are easy to use, require no assumptions beyond that of a valid randomization mechanism, and can be applied in a similar manner to dichotomous, ordinal, failure time, or continuous response criteria (endpoints). The methods are illustrated using data from a confirmatory clinical trial of a therapeutic agent for the treatment of dry eye disease.

Statistical approaches to effectiveness measurement and outcome-driven re-randomizations in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) studies.

The design of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia and Alzheimer's disease studies pose several statistical challenges, including issues related to performing multiple comparisons, defining effectiveness outcomes, and collecting and analyzing data from a design with multiple outcome-driven re-randomizations. We discuss the CATIE strategy for addressing many hypotheses within the context of one clinical trial while controlling the overall type I error rate. We provide motivation for the use of two effectiveness outcomes: time to all-cause discontinuation and composite endpoints that combine outcomes from multiple domains, such as efficacy, safety, cost-effectiveness, and quality of life. Methods for statistical analysis of an outcome-driven re-randomization trial are compared and evaluated. We describe analysis within each phase, analysis based on the first randomization or treatment algorithms, and repeated measures modeling. Finally, strategies are described for designing an electronic data collection system for trials with repeated outcome-driven re-randomizations.

The Role of Statistics in Regulatory Decision Making.

The number of statistical challenges facing regulators remains high, as does the importance of statistical thinking in the regulatory decision-making process. Statisticians in the Office of Biostatistics at the US Food and Drug Administration review hundreds of new drug and therapeutic biologic applications each year and advise sponsors on protocols numbering in the thousands. In addition to remaining up to date on the newest statistical methods, statisticians are often called upon for innovative approaches to difficult regulatory problems. This article presents the author's view of the important role that statisticians play in regulatory decision making, beginning with a broad overview of current office initiatives, including the development of guidance documents and a recent push for open and transparent collaboration with industry on methods development. Several recent examples are provided to illustrate the impact that statisticians can have on regulatory decisions through the use of strategic quantitative thinking. Also discussed are areas where it is believed that innovative statistical solutions or greater clarity on existing approaches is still needed.

Depression, anxiety, antidepressant use, and cardiovascular disease among Hispanic men and women of different national backgrounds: results from the Hispanic Community Health Study/Study of Latinos.

PURPOSE: To describe prevalence and relationships to cardiovascular morbidity of depression, anxiety, and medication use among Hispanic/Latinos of different ethnic backgrounds. METHODS: Cross-sectional analysis of 15,864 men and women aged 18 to 74 years in the population-based Hispanic Community Health Study/Study of Latinos. Depressive and anxiety symptoms were assessed with shortened Center for Epidemiological Studies Depression Scale and Spielberger Trait Anxiety Scale. RESULTS: Prevalence of high depressive symptoms ranged from low of 22.3% (95% confidence interval [CI], 20.4-24.3) to high of 38.0% (95% CI, 35.2-41.0) among those of Mexican or Puerto Rican background, respectively. Adjusted odds ratios for depression rose monotonically with number of cardiovascular disease (CVD) risk factor from 1.46 (95% CI, 1.18-1.75) for those with one risk factors to 4.36 (95% CI, 2.47-7.70) for those with five risk factors. Antidepressant medication was used by 5% with striking differences between those with and without history of CVD (15.4% and 4.6%, respectively) and between insured (8.2%) and uninsured (1.8%). CONCLUSIONS: Among US Hispanics/Latinos, high depression and anxiety symptoms varied nearly twofold by Hispanic background and sex, history of CVD, and increasing number of CVD risk factors. Antidepressant medication use was lower than in the general population, suggesting under treatment especially among those who had no health insurance.

Prevalence of hypertension, awareness, treatment, and control in the Hispanic Community Health Study/Study of Latinos.

BACKGROUND: The prevention and control of hypertension is an essential component for reducing the burden of cardiovascular diseases. Here we describe the prevalence of hypertension in diverse Hispanic/Latino background groups and describe the proportion who are aware of their diagnosis, receiving treatment, and having their hypertension under control. METHODS: The Hispanic Community Health Study/Study of Latinos is a longitudinal cohort study of 16,415 Hispanics/Latinos, aged 18-74 years from 4 US communities (Bronx, NY; Chicago, IL; Miami, FL; and San Diego, CA). At baseline (2008-2011) the study collected extensive measurements and completed questionnaires related to research on cardiovascular diseases. Hypertension was defined as measured blood pressure ≥140/90mm Hg or use of antihypertensive medication. RESULTS: The total age-adjusted prevalence of hypertension in this study was 25.5% as compared with 27.4% in non-Hispanic whites in the National Health and Nutrition Examination Survey. Prevalence of hypertension increased with increasing age groups and was highest in Cuban, Puerto Rican, and Dominican background groups. The percent with hypertension who were aware, being treated with medication, or had their hypertension controlled was lower compared with US non-Hispanic whites with hypertension and it was lowest in those without health insurance. CONCLUSIONS: These findings indicate a significant deficit in treatment and control of hypertension among Hispanics/Latinos residing in the United States, particularly those without health insurance. Given the relative ease of identification of hypertension and the availability of low-cost medications, enabling better access to diagnostic and treatment services should reduce the burden of hypertension in Hispanic populations.

Prevalence of diabetes among Hispanics/Latinos from diverse backgrounds: the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

OBJECTIVE: We examine differences in prevalence of diabetes and rates of awareness and control among adults from diverse Hispanic/Latino backgrounds in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). RESEARCH DESIGN AND METHODS: The HCHS/SOL, a prospective, multicenter, population-based study, enrolled from four U.S. metropolitan areas from 2008 to 2011 16,415 18-74-year-old people of Hispanic/Latino descent. Diabetes was defined by either fasting plasma glucose, impaired glucose tolerance 2 h after a glucose load, glycosylated hemoglobin (A1C), or documented use of hypoglycemic agents (scanned medications). RESULTS: Diabetes prevalence varied from 10.2% in South Americans and 13.4% in Cubans to 17.7% in Central Americans, 18.0% in Dominicans and Puerto Ricans, and 18.3% in Mexicans (P < 0.0001). Prevalence related positively to age (P < 0.0001), BMI (P < 0.0001), and years living in the U.S. (P = 0.0010) but was negatively related to education (P = 0.0005) and household income (P = 0.0043). Rate of diabetes awareness was 58.7%, adequate glycemic control (A1C <7%, 53 mmol/mol) was 48.0%, and having health insurance among those with diabetes was 52.4%. CONCLUSIONS: Present findings indicate a high prevalence of diabetes but considerable diversity as a function of Hispanic background. The low rates of diabetes awareness, diabetes control, and health insurance in conjunction with the negative associations between diabetes prevalence and both household income and education among Hispanics/Latinos in the U.S. have important implications for public health policies.