A Bayesian approach in design and analysis of pediatric cancer clinical trials

N-of-1 medicine

ABSTRACT

The fields of precision and personalised medicine have led to promising advances in tailoring treatment to individual patients. Examples include genome/molecular alteration-guided drug selection, single-patient gene therapy design and synergy-based drug combination development, and these approaches can yield substantially diverse recommendations. Therefore, it is important to define each domain and delineate their commonalities and differences in an effort to develop novel clinical trial designs, streamline workflow development, rethink regulatory considerations, create value in healthcare and economics assessments, and other factors. These and other segments are essential to recognise the diversity within these domains to accelerate their respective workflows towards practice-changing healthcare. To emphasise these points, this article elaborates on the concept of digital health and digital medicine-enabled N-of-1 medicine, which individualises combination regimen and dosing using a patient's own data. We will conclude with recommendations for consideration when developing novel workflows based on emerging digital-based platforms.

Estimating the effect of nintedanib on forced vital capacity in children and adolescents with fibrosing interstitial lung disease using a Bayesian dynamic borrowing approach

BACKGROUND

The rarity of childhood interstitial lung disease (chILD) makes it challenging to conduct powered trials. In the InPedILD trial, among 39 children and adolescents with fibrosing ILD, there was a numerical benefit of nintedanib versus placebo on change in forced vital capacity (FVC) over 24 weeks (difference in mean change in FVC % predicted of 1.21 [95% confidence interval: -3.40, 5.81]). Nintedanib has shown a consistent effect on FVC across populations of adults with different diagnoses of fibrosing ILD.

METHODS

In a Bayesian dynamic borrowing analysis, prespecified before data unblinding, we incorporated data on the effect of nintedanib in adults and the data from the InPedILD trial to estimate the effect of nintedanib on FVC in children and adolescents with fibrosing ILD. The data from adults were represented as a meta-analytic predictive (MAP) prior distribution with mean 1.69 (95% credible interval: 0.49, 3.08). The adult data were weighted according to expert judgment on their relevance to the efficacy of nintedanib in chILD, obtained in a formal elicitation exercise.

RESULTS

Combined data from the MAP prior and InPedILD trial analyzed within the Bayesian framework resulted in a median difference between nintedanib and placebo in change in FVC % predicted at Week 24 of 1.63 (95% credible interval: -0.69, 3.40). The posterior probability for superiority of nintedanib versus placebo was 95.5%, reaching the predefined success criterion of at least 90%.

CONCLUSION

These findings, together with the safety data from the InPedILD trial, support the use of nintedanib in children and adolescents with fibrosing ILDs.

Estimating the Potential Benefits of Confirmatory Trials for Drugs with Accelerated Approval: A Comprehensive Value of Information Framework

BACKGROUND

The US Food and Drug Administration's Accelerated Approval (AA) policy provides a pathway for patients to access potentially life-saving drugs rapidly. However, the use of surrogate endpoints, single-arm designs, and small sample sizes in preliminary trials that support AAs can lead to uncertainty regarding the clinical benefits of such drugs. This study aims to develop a comprehensive value of information (VOI) framework for assessing the potential benefits of future confirmatory trials, accounting for the various uncertainties inherent in preliminary trials.

METHODS

I formulated an expected value of information from confirmatory trial (EVICT) metric, which evaluates the potential benefits of a confirmatory trial that would reduce those uncertainties by using a clinically meaningful endpoint, a randomized control, and increased sample size. The EVICT metric can quantify the expected benefits of a well-designed confirmatory trial or an inadequately designed one that continues to use surrogate endpoints or single-arm design. The framework was illustrated using a hypothetical AA drug for metastatic breast cancer.

RESULTS

The case study demonstrates that a highly uncertain preliminary trial of an AA drug was associated with a substantial EVICT. A confirmatory trial with an increased sample size for this AA drug, utilizing a clinically meaningful endpoint and randomized control, yielded a population-level EVICT of $12.6 million. Persistently using a surrogate endpoint and single-arm trial design would reduce the EVICT by 60%.

CONCLUSIONS

This framework can provide accurate VOI estimates to guide coverage policies, value-based pricing, and the design of confirmatory trials for AA drugs.

Addressing statistical issues when leveraging external control data in pediatric clinical trials using Bayesian dynamic borrowing

Conducting a well-powered and adequately controlled clinical trial in children is often challenging. Bayesian approaches are an attractive option for addressing such challenges as they provide a quantitatively rigorous and integrated framework that makes use of current control data to check and borrow information from historical control data. However various practical concerns and related statistical issues emerge when implementing such Bayesian borrowing approaches. In this manuscript we use a motivating case study to discuss a rigorous stepwise approach on how to address those issues within the Bayesian framework. Specifically, a comprehensive quantitative framework is proposed to assess the extent, synergy, and impact of borrowing. Steps on computing the measures to interpret borrowing are illustrated. Those measures can further help to determine whether additional discounting of external information is necessary.

How to select the initial dose for a pediatric study?

In typical clinical development programs, a new drug is first developed for the adult use. Drugs are often approved for adult use or in the process of obtaining approval in adults in the target indication before pediatric development is initiated. In designing the first pediatric clinical trial, one of the challenges is to select the initial dose to be tested. The ICH E11 R1 guidance advises that chronologic age alone may not always be the most appropriate categorical determinant to define developmental subgroups in pediatric studies. In this manuscript, the approaches to utilize available data in adults related to those factors beyond age to inform the starting dose selection in pediatric drug development are discussed. Practical considerations and approaches are provided for informing pediatric starting dose. Additional considerations to use pre-clinical information are provided in the case when adult information is limited or not available.

Utility of propensity score-based Bayesian borrowing of external adult data in pediatric trials: A pragmatic evaluation through a case study in acute lymphoblastic leukemia (ALL)

A fully powered randomized controlled cancer trial can be challenging to conduct in children because of difficulties in enrollment of pediatric patients due to low disease incidence. One way to improve the feasibility of trials in pediatric patients, when clinically appropriate, is through borrowing information from comparable external adult trials in the same disease. Bayesian analysis of a pediatric trial provides a way of seamlessly augmenting pediatric trial efficacy data with data from external adult trials. However, not all external adult trial subjects may be equally clinically relevant with respect to the baseline disease severity, prognostic factors, co-morbidities, and prior therapy observed in the pediatric trial of interest. The propensity score matching method provides a way of matching the external adult subjects to the pediatric trial subjects on a set of clinically determined baseline covariates, such as baseline disease severity, prognostic factors and prior therapy. The matching then allows Bayesian information borrowing from only the most clinically relevant external adult subjects. Through a case study in pediatric acute lymphoblastic leukemia (ALL), we examine the utility of propensity score matched mixture and power priors in bringing appropriate external adult efficacy information into pediatric trial efficacy assessment, and present considerations for scaling fixed borrowing from external adult data.

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.

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.

Confounding adjustment in the analysis of augmented randomized controlled trial with hybrid control arm

The augmented randomized controlled trial (RCT) with hybrid control arm includes a randomized treatment group (RT), a smaller randomized control group (RC), and a large synthetic control (SC) group from real-world data. This kind of trial is useful when there is logistics and ethics hurdle to conduct a fully powered RCT with equal allocation, or when it is necessary to increase the power of the RCT by incorporating real-world data. A difficulty in the analysis of augmented RCT is that the SC and RC may be systematically different in the distribution of observed and unmeasured confounding factors, causing bias when the two control groups are analyzed together as hybrid controls. We propose to use propensity score (PS) analysis to balance the observed confounders between SC and RC. The possible bias caused by unmeasured confounders can be estimated and tested by analyzing propensity score adjusted outcomes from SC and RC. We also propose a partial bias correction (PBC) procedure to reduce bias from unmeasured confounding. Extensive simulation studies show that the proposed PS + PBC procedures can improve the efficiency and statistical power by effectively incorporating the SC into the RCT data analysis, while still control the estimation bias and Type I error inflation that might arise from unmeasured confounding. We illustrate the proposed statistical procedures with data from an augmented RCT in oncology.

Current drug development and trial designs in neuro-oncology: report from the first American Society of Clinical Oncology and Society for Neuro-Oncology Clinical Trials Conference

Successful drug development for people with cancers of the CNS has been challenging. There are multiple barriers to successful drug development including biological factors, rarity of the disease, and ineffective use of clinical trials. Based upon a series of presentations at the First Central Nervous System Clinical Trials Conference hosted by the American Society of Clinical Oncology and the Society for Neuro-Oncology, we provide an overview on drug development and novel trial designs in neuro-oncology. This Review discusses the challenges of therapeutic development in neuro-oncology and proposes strategies to improve the drug discovery process by enriching the pipeline of promising therapies, optimising trial design, incorporating biomarkers, using external data, and maximising efficacy and reproducibility of clinical trials.

Bayesian Design of Superiority Trials: Methods and Applications

In this paper, we lay out the basic elements of Bayesian sample size determination (SSD) for the Bayesian design of a two-arm superiority clinical trial. We develop a flowchart of the Bayesian SSD that highlights the critical components of a Bayesian design and provides a practically useful roadmap for designing a Bayesian clinical trial in real world applications. We empirically examine the amount of borrowing, the choice of noninformative priors, and the impact of model misspecification on the Bayesian type I error and power. A formal and statistically rigorous formulation of conditional borrowing within the decision rule framework is developed. Moreover, by extending the partial borrowing power priors, a new borrowing-by-parts power prior for incorporating historical data is proposed. Computational algorithms are also developed to calculate the Bayesian type I error and power. Extensive simulation studies are carried out to explore the operating characteristics of the proposed Bayesian design of a superiority trial.

Optimal sample size determination for single-arm trials in pediatric and rare populations with Bayesian borrowing

In many therapeutic areas with unmet medical needs, such as pediatric oncology and rare diseases, one of the deterrent factors for clinical trial interpretability is the limited sample size with less-than-ideal operating characteristics. Single arm is usually the only viable design due to feasibility and ethical concerns. For the trial results to be more interpretable and conclusive, the evaluation of operating characteristics, such as type I error rate and power, and the appropriate utilization of prior information for study design, shall be prespecified and fully investigated during the trial planning phase. So far, very few existing literature addressed optimal sample size determination issues for the planning of pediatric and rare population trials, with majority of research focusing on analysis perspective with focus on Bayesian borrowing. In practice, when a single-arm trial is designed for rare population, it is not uncommon that the only information available is from an earlier trial and/or a few clinical publications based on observational studies, often constituting mixed or uncertain conclusions. In light of this, an optimal Bayesian sample size determination method for single-arm trial with binary or continuous endpoint is proposed, where conflicting prior beliefs can be readily incorporated. Prior effective sample size can be calculated to assess the robustness as well as the prior information borrowed. Moreover, due to the lack of closed-form posterior distributions in general, an alternative approach for calculating Bayesian power is described. Simulation studies are provided to demonstrate the utility of the proposed methods. In addition, a case study in pediatric patients with leukemia is included to illustrate the proposed method with the existing approaches.

Bayesian adaptive design for pediatric clinical trials incorporating a community of prior beliefs

Background

Pediatric population presents several barriers for clinical trial design and analysis, including ethical constraints on the sample size and slow accrual rate. Bayesian adaptive design methods could be considered to address these challenges in pediatric clinical trials.

Methods

We developed an innovative Bayesian adaptive design method and demonstrated the approach as a re-design of a published phase III pediatric trial. The innovative design used early success criteria based on skeptical prior and early futility criteria based on enthusiastic prior extrapolated from a historical adult trial, and the early and late stopping boundaries were calibrated to ensure a one-sided type I error of 2.5%. We also constructed several alternative designs which incorporated only one type of prior belief and the same stopping boundaries. To identify a preferred design, we compared operating characteristics including power, expected trial size and trial duration for all the candidate adaptive designs via simulation when performing an increasing number of equally spaced interim analyses.

Results

When performing an increasing number of equally spaced interim analyses, the innovative Bayesian adaptive trial design incorporating both skeptical and enthusiastic priors at both interim and final analyses outperforms alternative designs which only consider one type of prior belief, because it allows more reduction in sample size and trial duration while still offering good trial design properties including controlled type I error rate and sufficient power.

Conclusions

Designing a Bayesian adaptive pediatric trial with both skeptical and enthusiastic priors can be an efficient and robust approach for early trial stopping, thus potentially saving time and money for trial conduction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01569-x.

The FDA Oncology Center of Excellence Scientific Collaborative: Charting a Course for Applied Regulatory Science Research in Oncology

The FDA Oncology Center of Excellence (OCE) is a leader within the agency in scientific outreach activities and regulatory science research. On the basis of analysis of scientific workshops, internal meetings, and publications, the OCE identified nine scientific priority areas and one cross-cutting area of high interest for collaboration with external researchers. This article describes the process for identifying these scientific interest areas and highlights funded and unfunded opportunities for external researchers to work with FDA staff on critical regulatory science challenges.

A review of the experience with pediatric written requests issued for oncology drug products

BACKGROUND

Pediatric anticancer drug development has numerous challenges. The Pediatric Research Equity Act (PREA) and the Best Pharmaceuticals for Children Act (BPCA) were passed to address pediatric drug development deficiencies in general. Until recently, the requirements for pediatric evaluation of most oncology products developed for adult cancers have been waived. Because children typically do not have the same type of cancers, which occur commonly in adults, or the indication or drug had been granted an orphan designation, PREA therefore has had no impact. Pediatric studies for labeling updates are largely done through BPCA by a written request (WR) issued by the Food and Drug Administration (FDA). Because the cancers that occur in pediatric and adult populations do not share the same etiology or natural history, there are limited opportunities to extrapolate adult efficacy and safety to the pediatric population. The characteristics of individual pediatric studies included in WRs have varied greatly over time.

PROCEDURE

In this study, we searched WRs that were issued by the FDA since 2001. We found 40 such requests issued for oncology drugs and biologics, which had been accepted by sponsors.

RESULTS

Clinical trials included in 23 of the WRs have been concluded, 19 have resulted in exclusivity, and three drugs that were studied have been approved for use in pediatric populations. Herein, we present the spectrum of WRs from a regulatory, study design, dosing, formulation, analysis plan, evidentiary standard of efficacy, and safety perspective.

CONCLUSIONS

This provides information on requests issued in the past nearly 20 years and studies that are completed. As WRs have provided the only regulatory mechanism to assure pediatric cancer drug development, this can potentially provide insight on how pediatric cancer drug development may change in the future.