Unlocking the Potential: A Systematic Review of Master Protocol in Pediatrics.

The use of master protocols allows for innovative approaches to clinical trial designs, potentially enabling new approaches to operations and analytics and creating value for patients and drug developers. Pediatric research has been conducted for many decades, but the use of novel designs such as master protocols in pediatric research is not well understood. This study aims to provide a systematic review on the utilization of master protocols in pediatric drug development. A search was performed in September 2022 using two data sources (PubMed and ClinicalTrials.gov) and included studies conducted in the past10 years. General study information was extracted such as study type, study status, therapeutic area, and clinical trial phase. Study characteristics that are specific to pediatric studies (such as age of the participants and pediatric drug dosing) and important study design elements (such as number of test drug arms and whether randomization and/or concurrent control was used) were also collected. Our results suggest that master protocol studies are being used in pediatrics, with platform and basket trials more common than umbrella trials. Most of this experience is in oncology and early phase studies. There is a rise in the use starting in 2020, largely in oncology and COVID-19 trials. However, adoption of master protocols in pediatric clinical research is still on a small scale and could be substantially expanded. Work is required to further understand the barriers in implementing pediatric master protocols, from setting up infrastructure to interpreting study findings.

A comprehensive review and shiny application on the matching-adjusted indirect comparison.

Population-adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head-to-head trials are unavailable. Three commonly used PAIC methods include matching-adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta-regression (ML-NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user-friendly R Shiny application Shiny-MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny-MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny-MAIC application, enabling a user-friendly approach conducting MAIC for healthcare decision-making. The Shiny-MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/.

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.

Extent of safety database in pediatric drug development: types of assessment, analytical precision, and pathway for extrapolation through on-target effects.

Pediatric patients should have access to medicines that have been appropriately evaluated for safety and efficacy through revised labelling. Given this goal, the adequacy of the pediatric clinical development plan and resulting safety database are critical factors to inform a favorable benefit-risk assessment for the intended use of the medicinal product. While extrapolation from adults can be used to support efficacy of drugs in children, there may be a reluctance to use the same approach in safety assessments, wiping out potential gains in trial efficiency through a reduction of sample size. To address this issue, we explore safety review in pediatric trials, including specific types of safety assessments and precision on the estimation of event rates for specific adverse events (AEs) that can be achieved. In addition, we discuss the assessments which can provide a benchmark for the use of extrapolation of safety that focuses on on-target effects. Finally, we explore a unified approach for understanding precision using Bayesian approaches as the most appropriate methodology to describe or ascertain risk in probabilistic terms for the estimate of the event rate of specific AEs.

Recent Use of Pediatric Extrapolation in Pediatric Drug Development in US.

The regulatory standards of the United States Food and Drug Administration (FDA) require substantial evidence of effectiveness from adequate and well-controlled trials, for drugs developed in both adults and children. However, when scientifically justified, relying on extrapolation may be acceptable. Historically, the FDA's extrapolation approach was based on draft guidance published in 2014, which introduced the categories of full, partial, and no extrapolation. The European Medicines Agency (EMA) took a different view on pediatric extrapolation. To better understand the use of extrapolation to support pediatric drug development and approval, we reviewed the pediatric labeling changes published by the FDA, focusing on the labeling updates between 1/1/2015 and 7/31/2021, the period where the extrapolation approach is in transition to harmonize with the EMA. Within this time window, among the 265 drugs and biological products with pediatric labeling changes, 169 (63.8%) were identified where extrapolation was used. This includes 64 (24.2%) labeling changes, where full extrapolation was used, and 105 (39.6%) labeling changes, where partial extrapolation was used. The major disease areas that extrapolation was used include neuroscience (40/53, 75.5%) and infectious disease (20/28, 71.4%). The extrapolation approach was identified in terms of source population beyond the use of adult as well as extrapolation from clinical trials conducted in the same drug class. The use of extrapolation increased the rates of new and expanded pediatric indication in the period. This review gives the most recent landscape of pediatric labeling changes using extrapolation. With the released ICH (International Council for Harmonization) E11A guidance in April 2022, the paper also provides insights for future pediatric drug development programs.

A win ratio approach for comparing crossing survival curves in clinical trials.

Many clinical trials include time-to-event or survival data as an outcome. To compare two survival distributions, the log-rank test is often used to produce a P-value for a statistical test of the null hypothesis that the two survival curves are identical. However, such a P-value does not provide the magnitude of the difference between the curves regarding the treatment effect. As a result, the P-value is often accompanied by an estimate of the hazard ratio from the proportional hazards model or Cox model as a measurement of treatment difference. However, one of the most important assumptions for Cox model is that the hazard functions for the two treatment groups are proportional. When the hazard curves cross, the Cox model could lead to misleading results and the log-rank test could also perform poorly. To address the problem of crossing curves in survival analysis, we propose the use of the win ratio method put forward by Pocock et al. as an estimand for analysing such data. The subjects in the test and control treatment groups are formed into all possible pairs. For each pair, the test treatment subject is labelled a winner or a loser if it is known who had the event of interest such as death. The win ratio is the total number of winners divided by the total number of losers and its standard error can be estimated using Bebu and Lachin method. Using real trial datasets and Monte Carlo simulations, this study investigates the power and type I error and compares the win ratio method with the log-rank test and Cox model under various scenarios of crossing survival curves with different censoring rates and distribution parameters. The results show that the win ratio method has similar power as the log-rank test and Cox model to detect the treatment difference when the assumption of proportional hazards holds true, and that the win ratio method outperforms log-rank test and Cox model in terms of power to detect the treatment difference when the survival curves cross.

Composite Likelihoods with Bounded Weights in Extrapolation of Data.

Among many efforts to facilitate timely access to safe and effective medicines to children, increased attention has been given to extrapolation. Loosely, it is the leveraging of conclusions or available data from adults or older age groups to draw conclusions for the target pediatric population when it can be assumed that the course of the disease and the expected response to a medicinal product would be sufficiently similar in the pediatric and the reference population. Extrapolation then can be characterized as a statistical mapping of information from the reference (adults or older age groups) to the target pediatric population. The translation, or loosely mapping of information, can be through a composite likelihood approach where the likelihood of the reference population is weighted by exponentiation and that this exponent is related to the value of the mapped information in the target population. The weight is bounded above and below recognizing the fact that similarity (of the disease and the expected response) is still valid despite variability of response between the cohorts. Maximum likelihood approaches are then used for estimation of parameters, and asymptotic theory is used to derive distributions of estimates for use in inference. Hence, the estimation of effects in the target population borrows information from the reference population. In addition, this manuscript also talks about how this method is related to the Bayesian statistical paradigm.

Analysis and reporting of pediatric growth and development assessment from clinical trials: overview and challenges.

Pediatric drug development has many unique challenges, one of which is the evaluation of growth and development changes in children that are expected and are not due to the study intervention. Children grow and mature at different pace. The potential impact of the drug could vary with the developmental age of the participants receiving the treatment. For example, sexual maturation is a critical consideration in children of age 10 and above, but not in younger age groups. How the investigational drug impacts children is ultimately a risk-benefit consideration. In this paper, practical considerations and recommendations are provided on how to assess growth and development based on data collected from clinical trials in pediatric patients. The endpoints and measures related to growth, sexual maturation and neurocognitive development are discussed. Basic analysis approaches are recommended.

Strategies to facilitate adolescent access to medicines: Improving regulatory guidance.

BACKGROUND: Historically, pediatric medicines are developed after adult trials are completed, even when identical drug targets and disease similarities exist across the populations. This has resulted in significant delays in the authorization of medicines for adolescent use, limiting access to beneficial drugs. This study sought to understand how adolescent inclusion in adult trials is positioned in regulatory guidance documents as they set critical expectations for trial design and regulatory decision-making. METHODS: This study utilized a qualitative analysis approach. Guidance documents were identified via Food and Drug Administration and European Medicines Agency websites. Utilizing a blinded adjudication process, the documents were classified as permissive, exclusionary, or silent regarding recommendations about adolescent inclusion in adult clinical trials. A post hoc analysis of similarities and differences between the Food and Drug Administration and European Medicines Agency guidance documents was conducted to assess the possible role of regional pediatric research laws on age-inclusive trial methodologies as well as emergent themes by therapeutic area. RESULTS: In total, 96 Food and Drug Administration (1977 to 2019) and 106 European Medicines Agency (1987 to 2019) guidance documents were identified for analysis. The guidance contained explicit or implicit recommendations supporting adolescent inclusion in adult trials in 32% of Food and Drug Administration and 15% of European Medicines Agency documents, while 14% and 21%, respectively, were found to be exclusionary. A large number of guidance documents were silent regarding the applicability of adolescent-inclusive trial designs (53% and 64%, Food and Drug Administration and European Medicines Agency, respectively). Analysis by therapeutic area revealed the most permissive of adolescent inclusion in Food and Drug Administration guidance for infectious diseases and conditions requiring blood products in European Medicines Agency guidance. A more holistic approach to age-inclusive trial design was identified in disease guidance published by the Food and Drug Administration Oncology Center of Excellence. DISCUSSION: There are many influences on the development and/or revision of regulatory guidance documents. Substantial scientific knowledge and regulatory precedence for the inclusion of adolescents within adult trials are available to inform research approaches. Our study has identified important opportunities for the enhancement of guidance. For example, contextualization of developmental factors influencing adolescent disease progression provides insights into the role of adolescent inclusion. If addressed, guidance documents can facilitate broader acceptance of age-inclusive trial methodologies and accelerate adolescent access to medicines.

Estimands, Handling of Missing Data and Impact on Assumed Effect Size and Power in Pivotal COVID-19 Treatment Trials.

Estimands play an important role for aligning study objectives, study design and analyses through a precise definition of the quantity of interest. For COVID-19 studies, apart from intercurrent events, high volume of missing data has been observed. We explore their impact on several estimands through a synthetic COVID-19 data generated from a discrete-time multi-state model. We compare estimators of these estimands based on their ability to closely match the true response rates and retain assumed power. The final choice of the estimand then needs to be aligned with clinically meaningful quantities of interest to patients, clinicians, regulators and payers.

Matching within a hybrid RCT/RWD: framework on associated causal estimands.

As the regulatory environment becomes progressively receptive toward utilizing real-world evidence, a spectrum of real-world data incorporation techniques in trial conduct and analysis has seen increasing interest and adoption in different stages of drug development. Of particular interest is leveraging external control data to augment the control arm in a concurrent randomized controlled trial, where patients are enrolled in both investigational treatment arm and the control arm. Yet despite the emerging literature in external data borrowing in a hybrid trial setting, very little discussion focuses on delineating what should be matched and what is actually being estimated, especially when a variety of matching schemes can be considered. In general, external control can be matched in four different ways: (1) matching with the intersection between investigational treatment and concurrent control, (2) matching with the union of concurrent investigational treatment and concurrent control, (3) matching with concurrent control alone, and (4) matching with investigational treatment alone. In this article, the formulation of estimands for different matching schemes are detailed to describe what these matching methods facilitate to answer. Simulation studies are also conducted to evaluate the performance characteristics under different matching schemes, estimation methods, effect size assumptions, and missingness of confounders.

Real-World Data as External Controls: Practical Experience from Notable Marketing Applications of New Therapies.

INTRODUCTION: Real-world data (RWD) can contextualize findings from single-arm trials when randomized comparative trials are unethical or unfeasible. Findings from single-arm trials alone are difficult to interpret and a comparison, when feasible and meaningful, to patient-level information from RWD facilitates the evaluation. As such, there have been several recent regulatory applications including RWD or other external data to support the product's efficacy and safety. This paper summarizes some lessons learned from such contextualization from 20 notable new drug or biologic licensing applications in oncology and rare diseases. METHODS: This review focuses on 20 notable new drug or biologic licensing applications that included patient-level RWD or other external data for contextualization of trial results. Publicly available regulatory documents including clinical and statistical reviews, advisory committee briefing materials and minutes, and approved product labeling were retrieved for each application. The authors conducted independent assessments of these documents focusing on the regulatory evaluation, in each case. Three examples are presented in detail to illustrate the salient issues and themes identified across applications. RESULTS: Regulatory decisions were strongly influenced by the quality and usability of the RWD. Comparability of cohort attributes such as endpoints, populations, follow-up, index and censoring criteria, as well as data completeness and accuracy of key variables appeared to be essential to ensure the quality and relevance of the RWD. Given adequate sample size of the clinical trials or external control, the use of appropriate analytic methods to properly account for confounding, such as regression or matching, and pre-specification of these methods while blinded to patient outcomes seemed good strategies to address baseline differences. DISCUSSION: Contextualizing single-arm trials with patient-level RWD appears to be an advance in regulatory science; however, challenges remain. Statisticians and epidemiologists have long focused on analytical methods for comparative effectiveness but hurdles in use of RWD have often occurred upstream of the analyses. More specifically, we noted hurdles in evaluating data quality, justifying cohort selection or initiation of follow-up, and demonstrating comparability of cohorts and endpoints.

Win ratio approach for analyzing composite time-to-event endpoint with opposite treatment effects in its components.

There is an increasing interest in the use of win ratio with composite time-to-event due to its flexibility in combining component endpoints. Exploring this flexibility further, one interesting question is in assessing the impact when there is a difference in treatment effect in the component endpoints. For example, the active treatment may prolong the time to occurrence of the negative event such as death or ventilation; meanwhile, the treatment effect may also shorten the time to achieving positive events, such as recovery or improvement. Notably, this portrays a situation where the treatment effect on time to recovery is in a different direction of benefit compared to the time to ventilation or death. Under such circumstances, if a single endpoint is used, the benefit gained for other individual outcomes is not counted and is diminished. As consequence, the study may need a larger sample size to detect a significant effect of treatment. Such a scenario can be handled by win ratio in a novel way by ranking component events, which is different from the usual composite endpoint approach such as time-to-first event. To evaluate how the different directions of treatment effect on component endpoints will impact the win ratio analysis, we use a Clayton copula-based bivariate survival simulation to investigate the correlation of component time-to-event. Through simulation, we found that compared to the marginal model using single endpoints, the win ratio analysis on composite endpoint performs better, especially when the correlation between two events is weak. Then, we applied the methodology to an infectious disease progression simulated study motivated by COVID-19. The application demonstrates that the win ratio approach offers advantages in empirical power compared to the traditional Cox proportional hazard approach when there is a difference in treatment effect in the marginal events.

Power priors with entropy balancing weights in data augmentation of partially controlled randomized trials.

In pediatric or orphan diseases, there are many instances where it is unfeasible to conduct randomized and controlled clinical trials. This is due in part to the difficulty of enrolling a sufficient number of patients over a reasonable time period to meet adequate statistical power to demonstrate the treatment efficacy. One solution to reduce the sample size or expedite the trial timeline is to complement the current trial with real-world data. To this end, several propensity score-based methods have been developed to create defined groups of patients that are controlled for confounding based on a set of measured covariates at baseline. However, balance checking on the measured covariates and tweaks to the propensity score models is usually inevitable to achieve the joint balance across all covariates. To mitigate this iterative procedure, we utilize the entropy balancing weighting technique which focuses on balancing the covariates of subjects between the experimental and control groups directly and augments the current trial with the external control data via a power prior. The finite-sample properties of the proposed method are assessed via simulations in the context of asymmetrically randomized controlled trials where only a small portion of patients are randomized to the control group. Other methods such as covariate-balancing propensity score (CBPS) and propensity score matching (PSM) and weighting (PSW) are also compared to provide context on the operating characteristics of the proposed method.

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.

Efficacy and safety of the oral Janus kinase inhibitor baricitinib in the treatment of adults with alopecia areata: Phase 2 results from a randomized controlled study.

BACKGROUND: There are no treatments approved by the Food and Drug Administration for alopecia areata. OBJECTIVE: To evaluate the efficacy and safety of baricitinib in patients with ≥50% scalp hair loss in a phase 2 study of adults with alopecia areata (BRAVE-AA1). METHODS: Patients were randomized 1:1:1:1 to receive placebo or baricitinib 1 mg, 2 mg, or 4 mg once daily. Two consecutive interim analyses were performed after all patients completed weeks 12 and 36 or had discontinued treatment prior to these time points. The primary endpoint was the proportion of patients achieving a Severity of Alopecia Tool (SALT) score ≤20 at week 36. Logistic regression was used with nonresponder imputation for missing data. RESULTS: A total of 110 patients were randomized (placebo, 28; baricitinib 1-mg, 28; 2-mg, 27; 4-mg, 27). The baricitinib 1-mg dose was dropped after the first interim analysis based on lower SALT30 response rate. At week 36, the proportion of patients achieving a SALT score of ≤20 was significantly greater in baricitinib 2-mg (33.3%, P = .016) and 4-mg (51.9%, P = .001) groups versus placebo (3.6%). Baricitinib was well tolerated with no new safety findings. LIMITATIONS: Small sample size limits generalizability of results. CONCLUSION: These results support the efficacy and safety of baricitinib in patients with ≥50% scalp hair loss.