An enriched approach to combining high-dimensional genomic and low-dimensional phenotypic data.

We describe an approach for combining and analyzing high-dimensional genomic and low-dimensional phenotypic data. The approach leverages a scheme of weights applied to the variables instead of observations and, hence, permits incorporation of the information provided by the low dimensional data source. It can also be incorporated into commonly used downstream techniques, such as random forest or penalized regression. Finally, the simulated lupus studies involving genetic and clinical data are used to illustrate the overall idea and show that the proposed enriched penalized method can select significant genetic variables while keeping several important clinical variables in the final model.

Bayesian hierarchical models incorporating study-level covariates for multivariate meta-analysis of diagnostic tests without a gold standard with application to COVID-19.

When evaluating a diagnostic test, it is common that a gold standard may not be available. One example is the diagnosis of SARS-CoV-2 infection using saliva sampling or nasopharyngeal swabs. Without a gold standard, a pragmatic approach is to postulate a "reference standard," defined as positive if either test is positive, or negative if both are negative. However, this pragmatic approach may overestimate sensitivities because subjects infected with SARS-CoV-2 may still have double-negative test results even when both tests exhibit perfect specificity. To address this limitation, we propose a Bayesian hierarchical model for simultaneously estimating sensitivity, specificity, and disease prevalence in the absence of a gold standard. The proposed model allows adjusting for study-level covariates. We evaluate the model performance using an example based on a recently published meta-analysis on the diagnosis of SARS-CoV-2 infection and extensive simulations. Compared with the pragmatic reference standard approach, we demonstrate that the proposed Bayesian method provides a more accurate evaluation of prevalence, specificity, and sensitivity in a meta-analytic framework.

Finite Mixture Models, a Flexible Alternative to Standard Modeling Techniques for Extrapolated Mean Survival Times Needed for Cost-Effectiveness Analyses.

OBJECTIVES: To compare finite mixture models with common survival models with respect to how well they fit heterogenous data used to estimate mean survival times required for cost-effectiveness analysis. METHODS: Publicly available overall survival (OS) and progression-free survival (PFS) curves were digitized to produce nonproprietary data. Regression models based on the following distributions were fit to the data: Weibull, lognormal, log-logistic, generalized F, generalized gamma, Gompertz, mixture of 2 Weibulls, and mixture of 3 Weibulls. A second set of analyses was performed based on data in which patients who had not experienced an event by 30 months were censored. Model performance was compared based on the Akaike information criterion (AIC). RESULTS: For PFS, the 3-Weibull mixture (AIC = 479.94) and 2-Weibull mixture (AIC = 488.24) models outperformed other models by more than 40 points and produced the most accurate estimates of mean survival times. For OS, the AIC values for all models were similar (all within 4 points). The means for the mixture 3-Weibulls mixture model (17.60 months) and the 2-Weibull mixture model (17.59 months) were the closest to the Kaplan-Meier mean estimate of (17.58 months). The results and conclusions from the censored analysis of PFS were similar to the uncensored PFS analysis. On the basis of extrapolated mean OS, all models produced estimates within 10% of the Kaplan-Meier mean survival time. CONCLUSIONS: Finite mixture models offer a flexible modeling approach that has benefits over standard parametric models when analyzing heterogenous data for estimating survival times needed for cost-effectiveness analysis.

Streamlining Safety Data Collection in Hospital-Acquired Bacterial Pneumonia and Ventilator-Associated Bacterial Pneumonia Trials: Recommendations of the Clinical Trials Transformation Initiative Antibacterial Drug Development Project Team.

BACKGROUND: Resistant bacteria are one of the leading causes of hospital-acquired/ventilator-associated bacterial pneumonia (HABP/VABP). HABP/VABP trials are complex and difficult to conduct due to the large number of medical procedures, adverse events, and concomitant medications involved. Differences in the legislative frameworks between different regions of the world may also lead to excessive data collection. The Clinical Trials Transformation Initiative (CTTI) seeks to advance antibacterial drug development (ABDD) by streamlining clinical trials to improve efficiency and feasibility while maintaining ethical rigor, patient safety, information value, and scientific validity. METHODS: In 2013, CTTI engaged a multidisciplinary group of experts to discuss challenges impeding the conduct of HABP/VABP trials. Separate workstreams identified challenges associated with current data collection processes. Experts defined "data collection" as the act of capturing and reporting certain data on the case report form as opposed to recording of data as part of routine clinical care. The ABDD Project Team developed strategies for streamlining safety data collection in HABP/VABP trials using a Quality by Design approach. RESULTS: Current safety data collection processes in HABP/VABP trials often include extraneous information. More targeted strategies for safety data collection in HABP/VABP trials will rely on optimal protocol design and prespecification of which safety data are essential to satisfy regulatory reporting requirements. CONCLUSIONS: A consensus and a cultural change in clinical trial design and conduct, which involve recognition of the need for more efficient data collection, are urgently needed to advance ABDD and to improve HABP/VABP trials in particular.

Improving Conduct and Feasibility of Clinical Trials to Evaluate Antibacterial Drugs to Treat Hospital-Acquired Bacterial Pneumonia and Ventilator-Associated Bacterial Pneumonia: Recommendations of the Clinical Trials Transformation Initiative Antibacterial Drug Development Project Team.

BACKGROUND: The etiology of hospital-acquired or ventilator-associated bacterial pneumonia (HABP/VABP) is often multidrug-resistant infections. The evaluation of new antibacterial drugs for efficacy in this population is important, as many antibacterial drugs have demonstrated limitations when studied in this population. HABP/VABP trials are expensive and challenging to conduct due to protocol complexity and low patient enrollment, among other factors. The Clinical Trials Transformation Initiative (CTTI) seeks to advance antibacterial drug development by streamlining HABP/VABP clinical trials to improve efficiency and feasibility while maintaining ethical rigor, patient safety, information value, and scientific validity. METHODS: In 2013, CTTI engaged a multidisciplinary group of experts to discuss challenges impeding the conduct of HABP/VABP trials. Separate workstreams identified challenges associated with HABP/VABP protocol complexity. The Project Team developed potential solutions to streamline HABP/VABP trials using a Quality by Design approach. RESULTS: CTTI recommendations focus on 4 key areas to improve HABP/VABP trials: informed consent processes/practices, protocol design, choice of an institutional review board (IRB), and trial outcomes. Informed consent processes should include legally authorized representatives. Protocol design decisions should focus on eligibility criteria, prestudy antibacterial therapy considerations, use of new diagnostics, and sample size. CTTI recommends that sponsors use a central IRB and discuss trial endpoints with regulators, including defining a clinical failure and evaluating the impact of concomitant antibacterial drugs. CONCLUSIONS: Streamlining HABP/VABP trials by addressing key protocol elements can improve trial startup and patient recruitment/retention, reduce trial complexity and costs, and ensure patient safety while advancing antibacterial drug development.

Borrowing Concurrent Information from Non-Concurrent Control to Enhance Statistical Efficiency in Platform Trials.

A platform trial is a trial involving an innovative adaptive design with a single master protocol to efficiently evaluate multiple interventions. It offers flexible features such as dropping interventions for futility and adding new interventions to be evaluated during the course of a trial. Although there is a consensus that platform trials can identify beneficial interventions with fewer patients, less time, and a higher probability of success than traditional trials, there remains debate on certain issues, one of which is whether (and how) the non-concurrent control (NCC) (i.e., patients in the control group recruited prior to the new interventions) can be combined with the current control (CC) in the analysis, especially if there is a change of standard of care during the trial. METHODS: In this paper, considering time-to-event endpoints under the proportional hazard model assumption, we introduce a new concept of NCC concurrent observation time (NCC COT), and propose to borrow NCC COT through left truncation. This assumes that the NCC COT and CC are comparable. If the protocol does not prohibit NCC patients to change the standard of care while on study, NCC COT and CC likely will share the same standard of care. A simulated example is provided to demonstrate the approach. RESULTS: Using exponential distributions, the simulated example assumes that NCC COT and CC have the same hazard, and the treatment group has a lower hazard. The estimated HR comparing treatment to the pooled control group is 0.744 (95% CI 0.575, 0.962), whereas the comparison to the CC group alone is 0.755 (95% CI 0.566, 1.008), with corresponding p-values of 0.024 versus 0.057, respectively. This suggests that borrowing NCC COT can improve statistical efficiency when the exchangeability assumption holds. CONCLUSION: This article proposes an innovative approach of borrowing NCC COT to enhance statistical inference in platform trials under appropriate scenarios.

A causal roadmap for generating high-quality real-world evidence.

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases.

A paradigm shift in drug development for treatment of rare multidrug-resistant gram-negative pathogens.

Multidrug-resistant (MDR) gram-negative pathogens pose a major threat to patients worldwide. Although the organisms remain relatively uncommon overall, their incidence is steadily increasing with associated increases in mortality and pharmacoeconomic impact. As evidenced by the dearth of new products in the pipeline or in clinical use, the conventional paradigm for the development of drugs against such pathogens is generally ineffectual. We advocate the need for a shift in the current paradigm and propose innovative development programs that involve implementation of a graduated approval process. The initial phase of the proposed regulatory paradigm includes early approval of a new drug based on a robust nonrandomized study, buttressed by data from concurrent controls and a pharmacokinetic-pharmacodynamic package generated from nonclinical studies. The postapproval commitment phase will include a randomized controlled trial, when disease prevalence permits, as well as continued assessment of risks and benefits under "real world" settings.

Perspectives on Virtual (Remote) Clinical Trials as the "New Normal" to Accelerate Drug Development.

Although the digital revolution has transformed many areas of human endeavor, pharmaceutical drug development has been relatively slow to embrace the emerging technologies to enhance efficiency and optimize value in clinical trials. The topic has garnered even greater attention in the face of the coronavirus disease 2019 (COVID-19) outbreak, which has caused unprecedented disruption in the conduct of clinical trials and presented considerable challenges and opportunities for clinical trialists and data analysts. In this paper, we highlight the potential opportunity with virtual or digital clinical trials as viable options to enhance efficiency in drug development and, more importantly, in offering diverse patients easier and attractive means to participate in clinical trials. Special reference is made to the implication of artificial intelligence and machine-learning tools in trial execution and data acquisition, processing, and analysis in a virtual trial setting. Issues of patient safety, measurement validity, and data integrity are reviewed, and considerations are put forth with reference to the mitigation of underlying regulatory and operational barriers.

Current issues with covariate adjustment in the analysis of data from randomized controlled trials.

Covariate adjustment helps to minimize bias by eliminating the influence of one variable on another and generally improves precision of estimators in comparative studies. However, despite considerable discussion in the literature on the topic, there is continued controversy and misunderstanding about its valid use and impact in medical research. We review current issues and suggest steps to be taken in the course of the design, analysis, and reporting of studies to further heighten awareness of the problem particularly among nonstatisticians. It is reemphasized that prespecification of the variables of interest and parsimony of the analytical models are essential elements for valid use and interpretation of results of analysis involving covariates.

The Weight of Evidence From Electrophysiology, Observational, and Cardiovascular End Point Studies Demonstrates the Safety of Azithromycin.

Increased use of azithromycin (AZ) in treating infections associated with coronavirus disease 2019 (COVID-19) and reports of increased incidence of prolonged corrected QT (QTc) interval associated with AZ used with hydroxychloroquine prompted us to review the latest evidence in the literature, present additional analyses of human cardiovascular (CV) electrophysiology studies, and to describe sequential steps in research and development that were undertaken to characterize the benefit-risk profile of AZ. Combined QTc findings from electrocardiograms taken during oral and i.v. pharmacokinetic-pharmacodynamic studies of AZ suggest that clinically meaningful QTc prolongation is unlikely. Findings from several observational studies were heterogeneous and not as consistent as results from at least two large randomized controlled trials (RCTs). The QTc findings presented and observational data from studies with large numbers of events are not consistent with either a proarrhythmic action of AZ or an increase in frequency of CV deaths. Well-powered RCTs do not suggest a presence of increased risk of CV or sudden cardiac death after short-term or protracted periods of AZ usage, even in patients at higher risk from pre-existing coronary disease.

A comparative study of subgroup identification methods for differential treatment effect: Performance metrics and recommendations.

Subgroup identification with differential treatment effects serves as an important step towards precision medicine, as it provides evidence regarding how individuals with specific characteristics respond to a given treatment. This knowledge not only supports the tailoring of treatment strategies but also prompts the development of new treatments. This manuscript provides a brief overview of the issues associated with the methodologies aimed at identifying subgroups with differential treatment effects, and studies in depth the operational characteristics of five data-driven methods that have appeared recently in the literature. The performance of the methods under study to identify correctly the covariates affecting treatment effects is evaluated via simulation and under various conditions. Two clinical trial data sets are also used to illustrate the application of these methods. Discussion and recommendations pertaining to the use of these methods are provided, with emphasis on the relative performance of the methods under the conditions studied.

A commentary on Satoshi Kanazawa's study of intelligence and health.

We provide an exposition of the major conceptual, methodological and statistical shortcomings of the paper by Kanazawa (2006), concerning the relationship between intelligence and life expectancy. It is argued that the validity and robustness of the conclusions of the paper are compromised by fundamental problems, including failure to present competing views with fair balance, use of samples of convenience to draw conclusions about populations, performing tests of significance when there is no theoretical basis to do so and confusing association with causation. Given the far-reaching socio-economic implications of the conclusions, it is suggested that the messages of the paper be critically evaluated in relation to the major limitations of the research.

Big Data: transforming drug development and health policy decision making.

The explosion of data sources, accompanied by the evolution of technology and analytical techniques, has created considerable challenges and opportunities for drug development and healthcare resource utilization. We present a systematic overview these phenomena, and suggest measures to be taken for effective integration of the new developments in the traditional medical research paradigm and health policy decision making. Special attention is paid to pertinent issues in emerging areas, including rare disease drug development, personalized medicine, Comparative Effectiveness Research, and privacy and confidentiality concerns.

Vaccines: A review of immune-based interventions to prevent and treat disease.

The enormous gains made in public health during the 20th century, through the prevention and treatment of infectious disease, have contributed to dramatic improvements in the quality and length of the human lifespan. Continued advances in medicine are dependent on addressing several challenges including the increase in existing and new resistance to antibiotics, the decrease in productivity of the research and development (R&D) ecosystem, uncertain regulatory pathways, and an economic environment that rewards innovation for developing therapeutics that involve long cycle times from idea to a product. In this article, we consider important issues pertaining to the development of vaccines with particular emphasis on preclinical requirements, optimal dose selection, design, execution, and reporting of clinical trials for regulatory submission, planning and implementation of post-approval life-cycle programs, and emerging themes in therapeutic vaccines.

Evaluating methodological assumptions in comparative effectiveness research: overcoming pitfalls.

The scope of comparative effectiveness research (CER) is wide and therefore requires the application of complex statistical tools and nonstandard procedures. The commonly used methods presuppose the realization of important, and often untestable, assumptions pertaining to the underlying distribution, study heterogeneity and targeted population. Accordingly, the value of the results obtained based on such tools is in large part dependent on the validity of the underlying assumptions relating to the operating characteristics of the procedures. In this article, we elucidate some of the pitfalls that may arise with use of the most commonly used techniques, including those that are applied in network meta-analysis, observational data analysis and patient-reported outcome evaluation. In addition, reference is made to the impact of data quality and database heterogeneity on the performance of commonly used CER tools and the need for standards in order to inform researchers engaged in CER.

Perspectives on clinical trial data transparency and disclosure.

The increased demand for transparency and disclosure of data from clinical trials sponsored by pharmaceutical companies poses considerable challenges and opportunities from a statistical perspective. A central issue is the need to protect patient privacy and adhere to Good Clinical and Statistical Practices, while ensuring access to patient-level data from clinical trials to the wider research community. This paper offers options to navigate this dilemma and balance competing priorities, with emphasis on the role of good clinical and statistical practices as proven safeguards for scientific integrity, the importance of adopting best practices for reporting of data from secondary analyses, and the need for optimal collaboration among stakeholders to facilitate data sharing.

Revisiting issues, drawbacks and opportunities with observational studies in comparative effectiveness research.

RATIONALE: Despite their inherently pervasive limitations, data from observational studies are increasingly relied upon by health care decision makers to fill critical information gaps created by lack of evidence from randomized controlled trials. AIM AND OBJECTIVE: The aim and objective of this article was to revisit the major issues associated with observational studies from secondary data sources. METHOD: The method of this article was canvass of the literature. RESULTS: Sources of bias are highlighted and steps intended to minimize bias are summarized. CONCLUSION: Efforts should be made to improve causal inference of treatment effects from observational studies found in secondary data sources. Extra care and caution should be exercised in the interpretation and reporting of results from these studies.

A data-driven approach to quality risk management.

AIM: An effective clinical trial strategy to ensure patient safety as well as trial quality and efficiency involves an integrated approach, including prospective identification of risk factors, mitigation of the risks through proper study design and execution, and assessment of quality metrics in real-time. Such an integrated quality management plan may also be enhanced by using data-driven techniques to identify risk factors that are most relevant in predicting quality issues associated with a trial. In this paper, we illustrate such an approach using data collected from actual clinical trials. MATERIALS AND METHODS: Several statistical methods were employed, including the Wilcoxon rank-sum test and logistic regression, to identify the presence of association between risk factors and the occurrence of quality issues, applied to data on quality of clinical trials sponsored by Pfizer. RESULTS: ONLY A SUBSET OF THE RISK FACTORS HAD A SIGNIFICANT ASSOCIATION WITH QUALITY ISSUES, AND INCLUDED: Whether study used Placebo, whether an agent was a biologic, unusual packaging label, complex dosing, and over 25 planned procedures. CONCLUSION: Proper implementation of the strategy can help to optimize resource utilization without compromising trial integrity and patient safety.

Applications of ROC analysis in medical research: recent developments and future directions.

With the growing focus on comparative effectiveness research and personalized medicine, receiver-operating characteristic analysis can continue to play an important role in health care decision making. Specific applications of receiver-operating characteristic analysis include predictive model assessment and validation, biomarker diagnostics, responder analysis in patient-reported outcomes, and comparison of alternative treatment options. The authors present a survey of the potential applications of the method and briefly review several relevant extensions. Given the level of attention paid to biomarker validation, personalized medicine and comparative effectiveness research, it is highly likely that the receiver-operating characteristic analysis will remain an important visual and analytic tool for medical research and evidence-based medicine in the foreseeable future.