Bayesian adaptive design for concurrent trials involving biologically related diseases.

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.

Statistical modeling for Bayesian extrapolation of adult clinical trial information in pediatric drug evaluation.

Children represent a large underserved population of "therapeutic orphans," as an estimated 80% of children are treated off-label. However, pediatric drug development often faces substantial challenges, including economic, logistical, technical, and ethical barriers, among others. Among many efforts trying to remove these barriers, increased recent attention has been paid to extrapolation; that is, the leveraging of available data from adults or older age groups to draw conclusions for the pediatric population. The Bayesian statistical paradigm is natural in this setting, as it permits the combining (or "borrowing") of information across disparate sources, such as the adult and pediatric data. In this paper, authored by the pediatric subteam of the Drug Information Association Bayesian Scientific Working Group and Adaptive Design Working Group, we develop, illustrate, and provide suggestions on Bayesian statistical methods that could be used to design improved pediatric development programs that use all available information in the most efficient manner. A variety of relevant Bayesian approaches are described, several of which are illustrated through 2 case studies: extrapolating adult efficacy data to expand the labeling for Remicade to include pediatric ulcerative colitis and extrapolating adult exposure-response information for antiepileptic drugs to pediatrics.

Guidance on the implementation and reporting of a drug safety Bayesian network meta-analysis.

The Drug Information Association Bayesian Scientific Working Group (BSWG) was formed in 2011 with a vision to ensure that Bayesian methods are well understood and broadly utilized for design and analysis and throughout the medical product development process, and to improve industrial, regulatory, and economic decision making. The group, composed of individuals from academia, industry, and regulatory, has as its mission to facilitate the appropriate use and contribute to the progress of Bayesian methodology. In this paper, the safety sub-team of the BSWG explores the use of Bayesian methods when applied to drug safety meta-analysis and network meta-analysis. Guidance is presented on the conduct and reporting of such analyses. We also discuss different structural model assumptions and provide discussion on prior specification. The work is illustrated through a case study involving a network meta-analysis related to the cardiovascular safety of non-steroidal anti-inflammatory drugs.

Bayesian methods for design and analysis of safety trials.

Safety assessment is essential throughout medical product development. There has been increased awareness of the importance of safety trials recently, in part due to recent US Food and Drug Administration guidance related to thorough assessment of cardiovascular risk in the treatment of type 2 diabetes. Bayesian methods provide great promise for improving the conduct of safety trials. In this paper, the safety subteam of the Drug Information Association Bayesian Scientific Working Group evaluates challenges associated with current methods for designing and analyzing safety trials and provides an overview of several suggested Bayesian opportunities that may increase efficiency of safety trials along with relevant case examples.

Meta-analysis of clinical trial safety data in a drug development program: answers to frequently asked questions.

BACKGROUND: Meta-analyses of clinical trial safety data have risen in importance beyond regulatory submissions. During drug development, sponsors need to recognize safety signals early and adjust the development program accordingly, so as to facilitate the assessment of causality. Once a product is marketed, sponsors add postapproval clinical trial data to the body of information to help understand existing safety concerns or those that arise from other postapproval data sources, such as spontaneous reports. PURPOSE: This article focuses on common questions encountered when designing and performing a meta-analysis of clinical trial safety data. Although far from an exhaustive set of questions, they touch on some basic and often misunderstood features of conducting such meta-analyses. METHODS: The authors reviewed the current literature and used their combined experience with regulatory and other uses of meta-analysis to answer common questions that arise when performing meta-analyses of safety data. RESULTS: We addressed the following topics: choice of studies to pool, effects of the method of ascertainment, use of patient-level data compared to trial-level data, the need (or not) for multiplicity adjustments, heterogeneity of effects and sources of it, and choice of fixed effects versus random effects. LIMITATIONS: The list of topics is not exhaustive and the opinions offered represent only our perspective; we recognize that there may be other valid perspectives. CONCLUSIONS: Meta-analysis can be a valuable tool for evaluating safety questions, but a number of methodological choices need to be made in designing and conducting any meta-analysis. This article provides advice on some of the more commonly encountered choices.

The practice of pre-marketing safety assessment in drug development.

The last 15 years have seen a substantial increase in efforts devoted to safety assessment by statisticians in the pharmaceutical industry. While some of these efforts were driven by regulations and public demand for safer products, much of the motivation came from the realization that there is a strong need for a systematic approach to safety planning, evaluation, and reporting at the program level throughout the drug development life cycle. An efficient process can help us identify safety signals early and afford us the opportunity to develop effective risk minimization plan early in the development cycle. This awareness has led many pharmaceutical sponsors to set up internal systems and structures to effectively conduct safety assessment at all levels (patient, study, and program). In addition to process, tools have emerged that are designed to enhance data review and pattern recognition. In this paper, we describe advancements in the practice of safety assessment during the premarketing phase of drug development. In particular, we share examples of safety assessment practice at our respective companies, some of which are based on recommendations from industry-initiated working groups on best practice in recent years.

Bayesian meta-experimental design: evaluating cardiovascular risk in new antidiabetic therapies to treat type 2 diabetes.

Recent guidance from the Food and Drug Administration for the evaluation of new therapies in the treatment of type 2 diabetes (T2DM) calls for a program-wide meta-analysis of cardiovascular (CV) outcomes. In this context, we develop a new Bayesian meta-analysis approach using survival regression models to assess whether the size of a clinical development program is adequate to evaluate a particular safety endpoint. We propose a Bayesian sample size determination methodology for meta-analysis clinical trial design with a focus on controlling the type I error and power. We also propose the partial borrowing power prior to incorporate the historical survival meta data into the statistical design. Various properties of the proposed methodology are examined and an efficient Markov chain Monte Carlo sampling algorithm is developed to sample from the posterior distributions. In addition, we develop a simulation-based algorithm for computing various quantities, such as the power and the type I error in the Bayesian meta-analysis trial design. The proposed methodology is applied to the design of a phase 2/3 development program including a noninferiority clinical trial for CV risk assessment in T2DM studies.

Landmark mediation survival analysis using longitudinal surrogate.

Clinical cancer trials are designed to collect radiographic measurements of each patient's baseline and residual tumor burden at regular intervals over the course of study. For solid tumors, the extent of reduction in tumor size following treatment is used as a measure of a drug's antitumor activity. Statistical estimation of treatment efficacy routinely reduce the longitudinal assessment of tumor burden to a binary outcome describing the presence versus absence of an objective tumor response as defined by RECIST criteria. The objective response rate (ORR) is the predominate method for evaluating an experimental therapy in a single-arm trial. Additionally, ORR is routinely compared against a control therapy in phase III randomized controlled trials. The longitudinal assessments of tumor burden are seldom integrated into a formal statistical model, nor integrated into mediation analysis to characterize the relationships among treatment, residual tumor burden, and survival. This article presents a frameworkfor landmark mediation survival analyses devised to incorporate longitudinal assessment of tumor burden. R 2 effect-size measures are developed to quantify the survival treatment mediation effects using longitudinal predictors. Analyses are demonstrated with applications to two colorectal cancer trials. Survival prediction is compared in the presence versus absence of longitudinal analysis. Simulation studies elucidate settings wherein patterns of tumor burden dynamics require longitudinal analysis.

Planning and core analyses for periodic aggregate safety data reviews.

BACKGROUND: In 2009, the Safety Planning, Evaluation and Reporting Team gave detailed recommendations for a well-planned and systematic approach for safety data collection and analysis. Important aspects of this approach included regular reviews of aggregate data by a multidisciplinary team focusing on safety. PURPOSE: This article provides information to facilitate the planning and implementation of aggregate data reviews. METHODS: Our recommendations are based on experience of the authors and review of relevant literature. RESULTS: We present information regarding the planning of aggregate data reviews as well as examples of data displays that are useful for many different compounds. A subset of these data displays could form a set of 'core' analyses to be generated for aggregate data reviews.

Bayesian hierarchical modeling for detecting safety signals in clinical trials.

Detection of safety signals from clinical trial adverse event data is critical in drug development, but carries a challenging statistical multiplicity problem. Bayesian hierarchical mixture modeling is appealing for its ability to borrow strength across subgroups in the data, as well as moderate extreme findings most likely due merely to chance. We implement such a model for subject incidence (Berry and Berry, 2004 ) using a binomial likelihood, and extend it to subject-year adjusted incidence rate estimation under a Poisson likelihood. We use simulation to choose a signal detection threshold, and illustrate some effective graphics for displaying the flagged signals.

Recommendations for safety planning, data collection, evaluation and reporting during drug, biologic and vaccine development: a report of the safety planning, evaluation, and reporting team.

BACKGROUND: The Safety Planning, Evaluation and Reporting Team (SPERT) was formed in 2006 by the Pharmaceutical Research and Manufacturers of America. PURPOSE: SPERT's goal was to propose a pharmaceutical industry standard for safety planning, data collection, evaluation, and reporting, beginning with planning first-in-human studies and continuing through the planning of the post-product-approval period. METHODS: SPERT's recommendations are based on our review of relevant literature and on consensus reached in our discussions. RESULTS: An important recommendation is that sponsors create a Program Safety Analysis Plan early in development. We also give recommendations for the planning of repeated, cumulative meta-analyses of the safety data obtained from the studies conducted within the development program. These include clear definitions of adverse events of special interest and standardization of many aspects of data collection and study design. We describe a 3-tier system for signal detection and analysis of adverse events and highlight proposals for reducing "false positive" safety findings. We recommend that sponsors review the aggregated safety data on a regular and ongoing basis throughout the development program, rather than waiting until the time of submission. LIMITATIONS: We recognize that there may be other valid approaches. CONCLUSIONS: The proactive approach we advocate has the potential to benefit patients and health care providers by providing more comprehensive safety information at the time of new product marketing and beyond.

Patient-reported outcomes and health-care resource utilization in patients with psoriasis treated with etanercept: continuous versus interrupted treatment.

OBJECTIVE: The 24-week Etanercept Assessment of Safety and Effectiveness (EASE) study evaluated the effectiveness and tolerability of continuous versus interrupted etanercept treatment in patients with moderate to severe plaque psoriasis. The objective of this analysis was to assess patient-reported outcomes (PROs) and health-care resource utilization (HRU) data from the EASE study. METHODS: Patients received open-label etanercept 50 mg twice weekly for 12 weeks and then received either continued or interrupted (single round of discontinuation and re-treatment with etanercept) etanercept 50 mg once weekly for the second 12 weeks. PROs included the following: 1) the patient global assessments of psoriasis, joint pain, and itching scores; 2) the Dermatology Life Quality Index; 3) the Medical Outcomes Study Short Form 36 vitality domain; 4) the Beck Depression Inventory; 5) the European Quality-of-Life Group Feeling Thermometer; and 6) a patient satisfaction survey. HRU was evaluated using the Economic Implications of Psoriasis patient questionnaire. RESULTS: Continuous treatment with etanercept 50 mg twice weekly for 12 weeks followed by 50 mg once weekly for 12 weeks produced sustained and clinically important improvements in PROs and reductions in HRU. Reductions in some outcome measures after treatment discontinuation at week 12 were observed in the interrupted group; however, most changes did not revert to baseline levels, consistent with some residual clinical effect, and re-treatment produced improvements similar to week 12 levels. CONCLUSIONS: Continuous etanercept treatment provided greater sustained improvements in PROs than interrupted therapy; however, interrupting etanercept therapy, if needed, has predictable and manageable effects.

A randomized, open-label trial of continuous versus interrupted etanercept therapy in the treatment of psoriasis.

BACKGROUND: Although etanercept is used as a continuous therapy for moderate to severe plaque psoriasis, intermittent use may be necessary in some instances. OBJECTIVE: In this randomized, open-label study, we evaluated the effectiveness and safety of continuous versus interrupted etanercept therapy. METHODS: All patients received uninterrupted etanercept 50 mg twice weekly during the first 12 weeks, followed by either continuous (n = 1272) or interrupted (n = 1274) etanercept 50 mg once weekly in the next 12 weeks. The primary effectiveness end point was the proportion of responders (those who achieved a Physician's Global Assessment [PGA] score

Reductions in healthcare resource utilization in psoriatic arthritis patients receiving etanercept therapy: results from the educate trial.

The Experience Diagnosing, Understanding Care, and Treatment with Enbrel (EDUCATE) trial is a phase IV, 24-week, multicenter, open-label study of etanercept 50 mg weekly in the treatment of psoriatic arthritis (PsA) in community dermatology clinics. In this study, patients with active PsA and moderate to severe plaque psoriasis have measurable uses of healthcare resources at baseline, reflecting a burden of illness. Etanercept significantly reduced healthcare resource utilization, absenteeism, and caregiver assistance in PsA patients after 24 weeks of treatment. These results could translate into savings on both direct and indirect costs and improvements in health-related quality of life for patients with PsA.

Real-world effectiveness of select biologic and DMARD monotherapy and combination therapy in the treatment of rheumatoid arthritis: results from the RADIUS observational registry.

OBJECTIVE: To evaluate the effectiveness of select biologics, methotrexate (MTX), and other disease-modifying anti-rheumatic drugs (DMARDs) in the management of adult rheumatoid arthritis (RA) in routine clinical practice. RESEARCH DESIGN AND METHODS: RADIUS (Rheumatoid Arthritis DMARD Intervention and Utilization Study) comprises two prospective, 5-year, observational registries of over 10 000 patients. Over 4600 patients who initiated MTX or a biologic regimen (etanercept [ETN], infliximab [INF], ETN + MTX, and INF + MTX) and who had at least one on-regimen, follow-up evaluation, were included in this analysis. Adalimumab was not included because it had not yet received FDA approval at RADIUS initiation. Other common DMARD regimens (N = 762) were also compared with MTX. Patients who initiated less commonly used regimens, such as anakinra or cyclosporine, and those who did not have at least one on-regimen, follow-up evaluation, were not eligible for this analysis. Because ESR/CRP measurements were often not available, a modified ACR20 response (mACR20), defined as three out of four response criteria excluding ESR/CRP, was used to assess response at 12 months. Logistic regression analysis was performed to control for baseline covariates that may affect outcomes. MAIN OUTCOME MEASURES: The primary endpoint was the proportion of patients who achieved a mACR20 response at 12 months post-RADIUS entry. RESULTS: After adjusting for baseline covariates, patients receiving either ETN + MTX or ETN monotherapy were more likely to achieve a mACR20 response at 12 months than patients receiving MTX alone (odds ratio [OR] 1.29, 95% confidence interval [CI] 1.09-1.52; p < 0.01 and OR 1.23, 95% CI 1.02-1.47; p < 0.05, respectively). Conversely, patients treated with MTX + leflunomide (LEF) were less likely to achieve a mACR20 response than those receiving MTX alone (OR 0.68, 95% CI 0.48-0.96; p < 0.05). Significant differences were not observed between patients receiving MTX alone and either INF + MTX, MTX + hydroxychloroquine, MTX + hydroxychloroquine + sulfasalazine, INF monotherapy, or LEF monotherapy. CONCLUSION: These data from routine rheumatology clinical practice settings highlight the effectiveness of common biologic and DMARD therapies, and provide additional data beyond those of randomized, controlled trials.

The FDA's Final Rule on Expedited Safety Reporting: Statistical Considerations.

In March 2011, a Final Rule for expedited reporting of serious adverse events took effect in the United States for studies conducted under an Investigational New Drug (IND) application. In December 2012, the U.S. Food and Drug Administration (FDA) promulgated a final Guidance describing the operationalization of this Final Rule. The Rule and Guidance clarified that a clinical trial sponsor should have evidence suggesting causality before defining an unexpected serious adverse event as a suspected adverse reaction that would require expedited reporting to the FDA. The Rule's emphasis on the need for evidence suggestive of a causal relation should lead to fewer events being reported but, among those reported, a higher percentage actually being caused by the product being tested. This article reviews the practices that were common before the Final Rule was issued and the approach the New Rule specifies. It then discusses methods for operationalizing the Final Rule with particular focus on relevant statistical considerations. It concludes with a set of recommendations addressed to Sponsors and to the FDA in implementing the Final Rule.

Statistical Evaluation of Drug Safety Data.

There has been growing awareness of the importance of the statistical evaluation of drug safety data both in the premarketing and postmarketing settings. Careful and comprehensive approaches are warranted in safety evaluation. This paper offers a high-level review of some key issues and emerging statistical methodological developments. Specifically, the following topics are discussed: prospective program-level safety planning, evaluation, and reporting; the impact of adverse event grouping on statistical analysis; the applications of Bayesian methods in safety signal detection; meta-analysis for analyzing safety data; and safety graphics. Aspects related to benefit-risk assessments are also covered.

Safety monitoring in clinical trials.

Monitoring patient safety during clinical trials is a critical component throughout the drug development life-cycle. Pharmaceutical sponsors must work proactively and collaboratively with all stakeholders to ensure a systematic approach to safety monitoring. The regulatory landscape has evolved with increased requirements for risk management plans, risk evaluation and minimization strategies. As the industry transitions from passive to active safety surveillance activities, there will be greater demand for more comprehensive and innovative approaches that apply quantitative methods to accumulating data from all sources, ranging from the discovery and preclinical through clinical and post-approval stages. Statistical methods, especially those based on the Bayesian framework, are important tools to help provide objectivity and rigor to the safety monitoring process.