Platform trials to evaluate the benefit-risk of COVID-19 therapeutics: Successes, learnings, and recommendations for future pandemics.

BACKGROUND: In response to the COVID-19 global pandemic, multiple platform trials were initiated to accelerate evidence generation of potential therapeutic interventions. Given a rapidly evolving and dynamic pandemic, platform trials have a key advantage over traditional randomized trials: multiple interventions can be investigated under a master protocol sharing a common infrastructure. METHODS: This paper focuses on nine platform trials that were instrumental in advancing care in COVID-19 in the hospital and community setting. A semi-structured qualitative interview was conducted with the principal investigators and lead statisticians of these trials. Information from the interviews and public sources were tabulated and summarized across trials, and recommendations for best practice for the next health crisis are provided. RESULTS: Based on the information gathered takeaways were identified as 1) the existence of some aspect of trial design or conduct (e.g., existing network of investigators or colleagues, infrastructure for data capture and relevant statistical expertise) was a key success factor; 2) the choice of treatments (e.g., repurposed drugs) had major impact on the trials as did the choice of primary endpoint; and 3) the lack of coordination across trials was flagged as an area for improvement. CONCLUSION: These trials deployed during the COVID-19 pandemic demonstrate how to achieve both speed and quality of evidence generation regarding clinical benefit (or not) of existing therapies to treat new pathogens in a pandemic setting. As a group, these trials identified treatments that worked, and many that did not, in a matter of months.

Clinical trial considerations for pediatric cancer drug development.

Oncology has been one of the most active therapeutic areas in medicinal products development. Despite this fact, few drugs have been approved for use in pediatric cancer patients when compared to the number approved for adults with cancer. This disparity could be attributed to the fact that many oncology drugs have had orphan drug designation and were exempt from Pediatric Research Equity Act (PREA) requirements. On August 18, 2017, the RACE for Children Act, i.e. Research to Accelerate Cures and Equity Act, was signed into law as Title V of the 2017 FDA Reauthorization Act (FDARA) to amend the PREA. Pediatric investigation is now required if the drug or biological product is intended for the treatment of an adult cancer and directed at a molecular target that FDA determines to be "substantially relevant to the growth or progression of a pediatric cancer." This paper discusses the specific considerations in clinical trial designs and statistical methodologies to be implemented in oncology pediatric clinical programs.

RESTART trial design: two-stage seamless transition design with operational considerations.

Oncology/hematology is a competitive therapeutic area where the landscape is constantly evolving. With regulatory support, many drug developers have spent a lot of resources on the operationalization of innovative clinical trial designs, for example, adaptive Bayesian designs in confirmatory clinical trial settings. While overall survival is considered the gold standard in these designs, it is often not a viable choice in identifying treatment efficacy at a reasonable pace, especially for early-stage therapies. In recent years, several binary response surrogate endpoints have been used for accelerated or conditional approval of novel cancer therapies. Utilizing surrogate endpoints in the study design to predict objective clinical outcomes, such as overall survival, is particularly fundamental in cancer treatment clinical development. This manuscript will investigate logistic and statistical considerations of our proposed RESTART design, a new two-stage, seamless, single- to double-arm Bayesian design. This design could be used for single-arm dose expansion to a randomized confirmatory study. The operating characteristics of the RESTART design are evaluated based on simulations. Future directions and further modifications of this design will also be elaborated.

The win odds: statistical inference and regression.

Generalized pairwise comparisons and win statistics (i.e., win ratio, win odds and net benefit) are advantageous in analyzing and interpreting a composite of multiple outcomes in clinical trials. An important limitation of these statistics is their inability to adjust for covariates other than by stratified analysis. Because the win ratio does not account for ties, the win odds, a modification that includes ties, has attracted attention. We review and combine information on the win odds to articulate the statistical inferences for the win odds. We also show alternative variance estimators based on the exact permutation and bootstrap as well as statistical inference via the probabilistic index. Finally, we extend multiple-covariate regression probabilistic index models to the win odds with a univariate outcome. As an illustration we apply the regression models to the data in the CHARM trial.

Extrapolation as a Default Strategy in Pediatric Drug Development.

Pediatric drug development lags adult development by about 8 years (Mulugeta et al. in Pediatr Clin 64(6):1185-1196, 2017). In such context, many incentives, regulations, and innovative techniques have been proposed to address the disparity for pediatric patients. One such strategy is extrapolation of efficacy from a reference population. Extrapolation is currently justified by providing evidence in support of the effective use of drugs in children when the course of the disease and the expected treatment response would be sufficiently similar in the pediatric and reference population. This paper's position is that, despite uncertainties, pediatric drug development programs should initially assume some degree of extrapolation. The degree to which extrapolation can be used lies along a continuum representing the uncertainties to be addressed through generation of new pediatric evidence. In addressing these uncertainties, the extrapolation strategy should reflect the level of tolerable uncertainty concerning the decision to expose a child to the risks of a new drug. This judgment about the level of tolerable uncertainty should vary with the context (e.g., disease severity, existing therapeutic options) and can be embedded into pediatric drug development archetypes to ascertain the extent of studies needed and whether simultaneous development for adults and adolescents be considered.

Real-World Evidence Utilization in Clinical Development Reflected by US Product Labeling: Statistical Review.

The US Food and Drug Administration (FDA) has shown scientific discretion in interpreting the substantial evidence requirement for the approval of new drugs with its considerations on the use of single controlled or uncontrolled trials (Federal Food, Drug, and Cosmetic Act § 505(d), 21 USC 355(d), 1962). With the passage of the 21st Centuries Cures Act (21st Century Cures-patients. House, Energy and Commerce Committee, Washington, DC, 2019 available at: https://energycommerce.house.gov/sites/republicans.energycommerce.house.gov/files/analysis/21stCenturyCures/20140516PatientsWhitePaper.pdf ), the FDA is mandated to expand the role of real-world evidence (RWE) in support of drug approval. This mandate further broadens the scope of scientific discretion to include data collected outside clinical trials. We summarize the agency's past acceptance of real-world data (RWD) sources for supporting drug approval in new indications which have been reflected in US labels. In our summary, we focus on the type of RWD and statistical methodologies presented in these labels. Furthermore, two labels were selected for in-depth assessment of the RWE presented in these labels. Through these examples, we demonstrate the issues that can be raised in data collection that could affect interpretation. In addition, a brief discussion of statistical methods that can be used to incorporate RWE to clinical development is presented.

Bone Mineral Density to Assess Pediatric Bone Health in Drug Development.

BACKGROUND: Pediatric bone health is an important part of the safety assessment of inhaled corticosteroids and certain other drugs. Current regulatory guidance for assessment of bone health for intranasal and inhaled corticosteroid drugs is a single 1-year study of linear growth. OBJECTIVE: The objective of this study was to assess whether a significant change in bone mineral density (BMD) could be observed during a 12-month period in pediatric patients being treated for asthma with an inhaled corticosteroid using a previously conducted study. METHODS: The publicly available information from the Childhood Asthma Management Program (CAMP) study was used to assess whether a statistically significant difference in BMD could be detected over a 1-year period. Patients who were at Tanner stage ≥2 were excluded from analysis as is stated in the present FDA Guidance on growth studies with inhaled corticosteroids, and children with any use of oral corticosteroids were also excluded. A comparison in BMD change over time (bone mineral accretion [BMA]) between baseline and 12 months of follow-up was made for the placebo and inhaled budesonide groups using multiple regression analysis to account for age, race, and gender as covariates. RESULTS: From the original 1041 patients in the CAMP study, 74 patients met the criteria for evaluation, with 42 patients receiving budesonide and 32 placebo patients. Children randomized to budesonide had a lower mean BMA than those receiving placebo (0.021 ± 0.023 [SD] g/cm2/y vs 0.036 ± 0.025 [SD] g/cm2/y). CONCLUSION: In a select pediatric patient population, a significant change in BMA can be observed over 12 months on an inhaled corticosteroid. Based on this post hoc analysis, measurement of BMA as an assessment of pediatric bone health may be considered for certain drugs, especially when there is a potential signal of bone toxicity from animal or human data. The clinical relevance of this finding is presently unknown, and more data on the relationship between changes in BMA and clinical pediatric bone health outcomes are needed.

Flexible Cure Rate Modeling Under Latent Activation Schemes.

With rapid improvements in medical treatment and health care, many datasets dealing with time to relapse or death now reveal a substantial portion of patients who are cured (i.e., who never experience the event). Extended survival models called cure rate models account for the probability of a subject being cured and can be broadly classified into the classical mixture models of Berkson and Gage (BG type) or the stochastic tumor models pioneered by Yakovlev and extended to a hierarchical framework by Chen, Ibrahim, and Sinha (YCIS type). Recent developments in Bayesian hierarchical cure models have evoked significant interest regarding relationships and preferences between these two classes of models. Our present work proposes a unifying class of cure rate models that facilitates flexible hierarchical model-building while including both existing cure model classes as special cases. This unifying class enables robust modeling by accounting for uncertainty in underlying mechanisms leading to cure. Issues such as regressing on the cure fraction and propriety of the associated posterior distributions under different modeling assumptions are also discussed. Finally, we offer a simulation study and also illustrate with two datasets (on melanoma and breast cancer) that reveal our framework's ability to distinguish among underlying mechanisms that lead to relapse and cure.

Modelling geographically referenced survival data with a cure fraction.

The emergence of geographical information systems and related softwares nowadays enables medical databases to incorporate the geographical information on patients, allowing studies in spatial associations. Public health administrators and researchers are often interested in detecting variation in survival patterns by region or county in order to understand the possible factors that contribute towards such spatial discrepancies. These issues have led statisticians to develop survival models that account for spatial clustering and variation. Additionally, with rapid developments in medical and health sciences, researchers increasingly encounter data sets where a substantial portion of patients are cured. Models accounting for cure in the population assist in the prognosis of potentially terminal diseases. This article proposes a Bayesian modelling framework that models spatial associations for areally referenced survival data using a general class of cure models proposed by Cooner et al. The special models we outline are alternatives to the traditional proportional hazards models and can be fitted using standard Bayesian software such as WinBUGS.