Extrapolation of efficacy and other data to support the development of new medicines for children: A systematic review of methods

Sharing information across patient subgroups to draw conclusions from sparse treatment networks

Network meta-analysis (NMA) usually provides estimates of the relative effects with the highest possible precision. However, sparse networks with few available studies and limited direct evidence can arise, threatening the robustness and reliability of NMA estimates. In these cases, the limited amount of available information can hamper the formal evaluation of the underlying NMA assumptions of transitivity and consistency. In addition, NMA estimates from sparse networks are expected to be imprecise and possibly biased as they rely on large-sample approximations that are invalid in the absence of sufficient data. We propose a Bayesian framework that allows sharing of information between two networks that pertain to different population subgroups. Specifically, we use the results from a subgroup with a lot of direct evidence (a dense network) to construct informative priors for the relative effects in the target subgroup (a sparse network). This is a two-stage approach where at the first stage, we extrapolate the results of the dense network to those expected from the sparse network. This takes place by using a modified hierarchical NMA model where we add a location parameter that shifts the distribution of the relative effects to make them applicable to the target population. At the second stage, these extrapolated results are used as prior information for the sparse network. We illustrate our approach through a motivating example of psychiatric patients. Our approach results in more precise and robust estimates of the relative effects and can adequately inform clinical practice in presence of sparse networks.

A Bayesian approach based on discounting factor for consistency assessment in multi-regional clinical trial

Multi-regional clinical trial (MRCT) has become an increasing trend for its supporting simultaneous global drug development. After MRCT, consistency assessment needs to be conducted to evaluate regional efficacy. The weighted Z-test approach is a common consistency assessment approach in which the weighting parameter W does not have a good practical significance; the discounting factor approach improved from the weighted Z-test approach by converting the estimation of W in original weighted Z-test approach to the estimation of discounting factor D. However, the discounting factor approach is an approach of frequency statistics, in which D was fixed as a certain value; the variation of D was not considered, which may lead to un-reasonable results. In this paper, we proposed a Bayesian approach based on D to evaluate the treatment effect for the target region in MRCT, in which the variation of D was considered. Specifically, we first took D random instead of fixed as a certain value and specified a beta distribution for it. According to the results of simulation, we further adjusted the Bayesian approach. The application of the proposed approach was illustrated by Markov Chain Monte Carlo simulation.

Framework for the Use of External Controls to Evaluate Treatment Outcomes in Precision Oncology Trials

Advances in genomics have enabled anticancer therapies to be tailored to target specific genomic alterations. Single-arm trials (SATs), including those incorporated within umbrella, basket, and platform trials, are widely adopted when it is not feasible to conduct randomized controlled trials in rare biomarker-defined subpopulations. External controls (ECs), defined as control arm data derived outside the clinical trial, have gained renewed interest as a strategy to supplement evidence generated from SATs to allow comparative analysis. There are increasing examples demonstrating the application of EC in precision oncology trials. The prospective application of EC in conducting comparative studies is associated with distinct methodological challenges, the specific considerations for EC use in biomarker-defined subpopulations have not been adequately discussed, and a formal framework is yet to be established. In this review, we present a framework for conducting a prospective comparative analysis using EC. Key steps are (1) defining the purpose of using EC to address the study question, (2) determining if the external data are fit for purpose, (3) developing a transparent study protocol and a statistical analysis plan, and (iv) interpreting results and drawing conclusions on the basis of a prespecified hypothesis. We specify the considerations required for the biomarker-defined subpopulations, which include (1) specifying the comparator and biomarker status of the comparator group, (2) defining lines of treatment, (3) assessment of the biomarker testing panels used, and (4) assessment of cohort stratification in tumor-agnostic studies. We further discuss novel clinical trial designs and statistical techniques leveraging EC to propose future directions to advance evidence generation and facilitate drug development in precision oncology.

Dynamic enrichment of Bayesian small-sample, sequential, multiple assignment randomized trial design using natural history data: a case study from Duchenne muscular dystrophy

In Duchenne muscular dystrophy (DMD) and other rare diseases, recruiting patients into clinical trials is challenging. Additionally, assigning patients to long-term, multi-year placebo arms raises ethical and trial retention concerns. This poses a significant challenge to the traditional sequential drug development paradigm. In this paper, we propose a small-sample, sequential, multiple assignment, randomized trial (snSMART) design that combines dose selection and confirmatory assessment into a single trial. This multi-stage design evaluates the effects of multiple doses of a promising drug and re-randomizes patients to appropriate dose levels based on their Stage 1 dose and response. Our proposed approach increases the efficiency of treatment effect estimates by (i) enriching the placebo arm with external control data, and (ii) using data from all stages. Data from external control and different stages are combined using a robust meta-analytic combined (MAC) approach to consider the various sources of heterogeneity and potential selection bias. We reanalyze data from a DMD trial using the proposed method and external control data from the Duchenne Natural History Study (DNHS). Our method's estimators show improved efficiency compared to the original trial. Also, the robust MAC-snSMART method most often provides more accurate estimators than the traditional analytic method. Overall, the proposed methodology provides a promising candidate for efficient drug development in DMD and other rare diseases.

A toolbox for mimicking gastrointestinal conditions in children: Design and evaluation of biorelevant dissolution media for mimicking paediatric gastric- and small intestinal conditions

The goal of the present work was to develop an in vitro toolbox to evaluate the oral administration of dosage forms to children of different age groups and under different administration conditions (fasted/fed). Based on current data on the gastrointestinal physiology of children, a set of new biorelevant media was designed to mimic the composition and physicochemical properties of resting gastric and resting small intestinal fluid in children of different age groups. In addition, guidelines were developed on how to generate fasted and fed state gastric and small intestinal fluids by combining these media with age-specific drinking volumes or portions of already established simulated paediatric breakfast meals, respectively. These fluids can simulate the conditions in the paediatric stomach and small intestine after administration of a dosage form in the fasting state or after a breakfast. The in vitro toolbox was evaluated using the example of pre-school children with a total of five paediatric medicines. Results from the corresponding set of in vitro studies highlight the importance of addressing patient-specific characteristics rather than downscaling existing adult in vitro models.

Perspectives on informative Bayesian methods in pediatrics

Bayesian methods have been proposed as a natural fit for pediatric extrapolation, as they allow the incorporation of relevant external data to reduce the required sample size and hence trial burden for the pediatric patient population. In this paper we will discuss our experience and perspectives with these methods in pediatric trials. We will present some of the background and thinking underlying pediatric extrapolation and discuss the use of Bayesian methods within this context. We will present two recent case examples illustrating the value of a Bayesian approach in this setting and present perspectives on some of the issues that we have encountered in these and other cases.

Review article: Externally derived control arms-An opportunity for clinical trials in inflammatory bowel disease?

BACKGROUND

One of the greatest challenges in the current IBD clinical trial landscape is, perhaps, the recruitment and retention of eligible participants. Seamless testing of promising investigational compounds is paramount to address unmet needs, but this is hindered by a number of barriers, particularly patient concerns of placebo assignment.

AIMS

To review the use of novel trial designs leveraging externally derived data to synthetically create control groups or augment existing ones, and to summarise the regulatory position on the use of external controls for market authorisation.

METHODS

We conducted a PubMed literature search without restriction using search terms such as 'external controls' and 'historical controls' to identify relevant articles.

RESULTS

External controls are increasingly being used outside the context of cancer and rare diseases, including IBD, and increasingly recognised by regulatory bodies. Such designs, particularly in earlier phase trials, can inform key nodes in drug development and permit evaluating efficacy of interventions without combating the ethical and numerical enrolment challenges described. However, the lack of randomisation and blinding subjects them to significant bias. Groups require robust statistical and computational approaches to ensure patient-level data across groups are adequately balanced.

CONCLUSIONS

While this approach has several pitfalls, and is not robust enough to replace traditional randomised, placebo-controlled trials, it may offer a compromise to address key research questions at a more rapid pace, with fewer patients, and lower cost.

Application of Bayesian approaches in drug development: starting a virtuous cycle

The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and p values, for evaluation and approval of new treatments. The clinical drug development process, however, with its accumulation of data over time, can be well suited for the use of Bayesian statistical approaches that explicitly incorporate existing data into clinical trial design, analysis and decision-making. Such approaches, if used appropriately, have the potential to substantially reduce the time and cost of bringing innovative medicines to patients, as well as to reduce the exposure of patients in clinical trials to ineffective or unsafe treatment regimens. Nevertheless, despite advances in Bayesian methodology, the availability of the necessary computational power and growing amounts of relevant existing data that could be used, Bayesian methods remain underused in the clinical development and regulatory review of new therapies. Here, we highlight the value of Bayesian methods in drug development, discuss barriers to their application and recommend approaches to address them. Our aim is to engage stakeholders in the process of considering when the use of existing data is appropriate and how Bayesian methods can be implemented more routinely as an effective tool for doing so.

Updating the probability of study success for combination therapies using related combination study data

Combination therapies are becoming increasingly used in a range of therapeutic areas such as oncology and infectious diseases, providing potential benefits such as minimising drug resistance and toxicity. Sets of combination studies may be related, for example, if they have at least one treatment in common and are used in the same indication. In this setting, value can be gained by sharing information between related combination studies. We present a framework that allows the study success probabilities of a set of related combination therapies to be updated based on the outcome of a single combination study. This allows us to incorporate both direct and indirect data on a combination therapy in the decision-making process for future studies. We also provide a robustification that accounts for the fact that the prior assumptions on the correlation structure of the set of combination therapies may be incorrect. We show how this framework can be used in practice and highlight the use of the study success probabilities in the planning of clinical studies.

Challenges of using external data in clinical trials- an illustration in patients with COVID-19

BACKGROUND

To improve the efficiency of clinical trials, leveraging external data on control and/or treatment effects, which is almost always available, appears to be a promising approach.

METHODS

We used data from the experimental arm of the Covidicus trial evaluating high-dose dexamethasone in severely ill and mechanically ventilated COVID-19 patients, using published data from the Recovery trial as external data, to estimate the 28-day mortality rate. Primary approaches to deal with external data were applied.

RESULTS

Estimates ranged from 0.241 ignoring the external data up to 0.294 using hierarchical Bayesian models. Some evidence of differences in mortality rates between the Covidicus and Recovery trials were observed, with an matched adjusted odds ratio of death in the Covidicus arm of 0.41 compared to the Recovery arm.

CONCLUSIONS

These indirect comparisons appear sensitive to the method used. None of those approaches appear robust enough to overcome randomized clinical trial data.

TRIAL REGISTRATION

Covidicus Trial: NCT04344730, First Posted: 14/04/2020; Recovery trial: NCT04381936.

Informed Bayesian survival analysis

Background

We provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of evidence, and incorporating uncertainty about the true data generating process.

Methods

We illustrate the application of the outlined Bayesian approaches on an example data set, retrospective re-analyzing a colon cancer trial. We assess the performance of Bayesian parametric survival analysis and maximum likelihood survival models with AIC/BIC model selection in fixed-n and sequential designs with a simulation study.

Results

In the retrospective re-analysis of the example data set, the Bayesian framework provided evidence for the absence of a positive treatment effect of adding Cetuximab to FOLFOX6 regimen on disease-free survival in patients with resected stage III colon cancer. Furthermore, the Bayesian sequential analysis would have terminated the trial 10.3 months earlier than the standard frequentist analysis. In a simulation study with sequential designs, the Bayesian framework on average reached a decision in almost half the time required by the frequentist counterparts, while maintaining the same power, and an appropriate false-positive rate. Under model misspecification, the Bayesian framework resulted in higher false-negative rate compared to the frequentist counterparts, which resulted in a higher proportion of undecided trials. In fixed-n designs, the Bayesian framework showed slightly higher power, slightly elevated error rates, and lower bias and RMSE when estimating treatment effects in small samples. We found no noticeable differences for survival predictions. We have made the analytic approach readily available to other researchers in the RoBSA R package.

Conclusions

The outlined Bayesian framework provides several benefits when applied to parametric survival analyses. It uses data more efficiently, is capable of considerably shortening the length of clinical trials, and provides a richer set of inferences.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01676-9.

Once-daily dolutegravir-based antiretroviral therapy in infants and children living with HIV from age 4 weeks: results from the below 14 kg cohort in the randomised ODYSSEY trial

BACKGROUND

Young children living with HIV have few treatment options. We aimed to assess the efficacy and safety of dolutegravir-based antiretroviral therapy (ART) in children weighing between 3 kg and less than 14 kg.

METHODS

ODYSSEY is an open-label, randomised, non-inferiority trial (10% margin) comparing dolutegravir-based ART with standard of care and comprises two cohorts (children weighing ≥14 kg and <14 kg). Children weighing less than 14 kg starting first-line or second-line ART were enrolled in seven HIV treatment centres in South Africa, Uganda, and Zimbabwe. Randomisation, which was computer generated by the trial statistician, was stratified by first-line or second-line ART and three weight bands. Dispersible 5 mg dolutegravir was dosed according to WHO weight bands. The primary outcome was the Kaplan-Meier estimated proportion of children with virological or clinical failure by 96 weeks, defined as: confirmed viral load of at least 400 copies per mL after week 36; absence of virological suppression by 24 weeks followed by a switch to second-line or third-line ART; all-cause death; or a new or recurrent WHO stage 4 or severe WHO stage 3 event. The primary outcome was assessed by intention to treat in all randomly assigned participants. A primary Bayesian analysis of the difference in the proportion of children meeting the primary outcome between treatment groups incorporated evidence from the higher weight cohort (≥14 kg) in a prior distribution. A frequentist analysis was also done of the lower weight cohort (<14 kg) alone. Safety analyses are presented for all randomly assigned children in this study (<14 kg cohort). ODYSSEY is registered with ClinicalTrials.gov, NCT02259127.

FINDINGS

Between July 5, 2018, and Aug 26, 2019, 85 children weighing less than 14 kg were randomly assigned to receive dolutegravir (n=42) or standard of care (n=43; 32 [74%] receiving protease inhibitor-based ART). Median age was 1·4 years (IQR 0·6-2·0) and median weight 8·1 kg (5·4-10·0). 72 (85%) children started first-line ART and 13 (15%) started second-line ART. Median follow-up was 124 weeks (112-137). By 96 weeks, treatment failure occurred in 12 children in the dolutegravir group (Kaplan-Meier estimated proportion 31%) versus 21 (48%) in the standard-of-care group. The Bayesian estimated difference in treatment failure (dolutegravir minus standard of care) was -10% (95% CI -19% to -2%; p=0·020), demonstrating superiority of dolutegravir. The frequentist estimated difference was -18% (-36% to 2%; p=0·057). 15 serious adverse events were reported in 11 (26%) children in the dolutegravir group, including two deaths, and 19 were reported in 11 (26%) children in the standard-of-care group, including four deaths (hazard ratio [HR] 1·08 [95% CI 0·47-2·49]; p=0·86). 36 adverse events of grade 3 or higher were reported in 19 (45%) children in the dolutegravir group, versus 34 events in 21 (49%) children in the standard-of-care group (HR 0·93 [0·50-1·74]; p=0·83). No events were considered related to dolutegravir.

INTERPRETATION

Dolutegravir-based ART was superior to standard of care (mainly protease inhibitor-based) with a lower risk of treatment failure in infants and young children, providing support for global dispersible dolutegravir roll-out for younger children and allowing alignment of adult and paediatric treatment.

FUNDING

Paediatric European Network for Treatment of AIDS Foundation, ViiV Healthcare, UK Medical Research Council.

From innovative thinking to pharmaceutical industry implementation: Some success stories

In industry, successful innovation involves not only developing new statistical methodology, but also ensuring that this methodology is implemented successfully. This includes enabling applied statisticians to understand the method, its benefits and limitations and empowering them to implement the new method. This will include advocacy, influencing in-house and external stakeholders, such that these stakeholders are receptive to the new methodology. In this paper, we describe some industry successes and focus on our colleague, Andy Grieve's role in these.

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.

Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial

Background

Clinical trial investigators may need to evaluate treatment effects in a specific subgroup (or subgroups) of participants in addition to reporting results of the entire study population. Such subgroups lack power to detect a treatment effect, but there may be strong justification for borrowing information from a larger patient group within the same trial, while allowing for differences between populations. Our aim was to develop methods for eliciting expert opinions about differences in treatment effect between patient populations, and to incorporate these opinions into a Bayesian analysis.

Methods

We used an interaction parameter to model the relationship between underlying treatment effects in two subgroups. Elicitation was used to obtain clinical opinions on the likely values of the interaction parameter, since this parameter is poorly informed by the data. Feedback was provided to experts to communicate how uncertainty about the interaction parameter corresponds with relative weights allocated to subgroups in the Bayesian analysis. The impact on the planned analysis was then determined.

Results

The methods were applied to an ongoing non-inferiority trial designed to compare antiretroviral therapy regimens in 707 children living with HIV and weighing ≥ 14 kg, with an additional group of 85 younger children weighing < 14 kg in whom the treatment effect will be estimated separately. Expert clinical opinion was elicited and demonstrated that substantial borrowing is supported. Clinical experts chose on average to allocate a relative weight of 78% (reduced from 90% based on sample size) to data from children weighing ≥ 14 kg in a Bayesian analysis of the children weighing < 14 kg. The total effective sample size in the Bayesian analysis was 386 children, providing 84% predictive power to exclude a difference of more than 10% between arms, whereas the 85 younger children weighing < 14 kg provided only 20% power in a standalone frequentist analysis.

Conclusions

Borrowing information from a larger subgroup or subgroups can facilitate estimation of treatment effects in small subgroups within a clinical trial, leading to improved power and precision. Informative prior distributions for interaction parameters are required to inform the degree of borrowing and can be informed by expert opinion. We demonstrated accessible methods for obtaining opinions.

Pharmacometrics meets statistics-A synergy for modern drug development

Modern drug development problems are very complex and require integration of various scientific fields. Traditionally, statistical methods have been the primary tool for design and analysis of clinical trials. Increasingly, pharmacometric approaches using physiology-based drug and disease models are applied in this context. In this paper, we show that statistics and pharmacometrics have more in common than what keeps them apart, and collectively, the synergy from these two quantitative disciplines can provide greater advances in clinical research and development, resulting in novel and more effective medicines to patients with medical need.

Applying the Noninferiority Paradigm to Assess Exposure-Response Similarity and Dose Between Pediatric and Adult Patients

The use of extrapolation of efficacy in pediatric drug development programs is possible when disease progression and treatment response are similar in adult and pediatric populations. Historically, the exposure-response (E-R) similarity was assessed by visual inspection of 2 E-R curves to support pediatric extrapolation. The aim of this study was to develop a quantitative framework to describe the E-R relationship and the difference in E-R between pediatric and adult patients based on accumulated experience in pediatric drug development programs. Using clinical data for 8 drugs with either a linear or nonlinear E-R relationship, we adapted the methodology used in noninferiority testing to assess the E-R similarity between adult and pediatric patients at the targeted drug exposure. We implemented bootstrap-based and Bayesian-based methodologies to estimate the probability of concluding noninferiority of the E-R relationship. This approach provides objective criteria that can be applied to an assessment of E-R noninferiority in 2 populations to support extrapolation of efficacy in drug development programs from adults to pediatric populations.

Bridging across patient subgroups in phase I oncology trials that incorporate animal data

In this paper, we develop a general Bayesian hierarchical model for bridging across patient subgroups in phase I oncology trials, for which preliminary information about the dose-toxicity relationship can be drawn from animal studies. Parameters that re-scale the doses to adjust for intrinsic differences in toxicity, either between animals and humans or between human subgroups, are introduced to each dose-toxicity model. Appropriate priors are specified for these scaling parameters, which capture the magnitude of uncertainty surrounding the animal-to-human translation and bridging assumption. After mapping data onto a common, 'average' human dosing scale, human dose-toxicity parameters are assumed to be exchangeable either with the standardised, animal study-specific parameters, or between themselves across human subgroups. Random-effects distributions are distinguished by different covariance matrices that reflect the between-study heterogeneity in animals and humans. Possibility of non-exchangeability is allowed to avoid inferences for extreme subgroups being overly influenced by their complementary data. We illustrate the proposed approach with hypothetical examples, and use simulation to compare the operating characteristics of trials analysed using our Bayesian model with several alternatives. Numerical results show that the proposed approach yields robust inferences, even when data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.

Summarising salient information on historical controls: A structured assessment of validity and comparability across studies

BACKGROUND

While placebo-controlled randomised controlled trials remain the standard way to evaluate drugs for efficacy, historical data are used extensively across the development cycle. This ranges from supplementing contemporary data to increase the power of trials to cross-trial comparisons in estimating comparative efficacy. In many cases, these approaches are performed without in-depth review of the context of data, which may lead to bias and incorrect conclusions.

METHODS

We discuss the original 'Pocock' criteria for the use of historical data and how the use of historical data has evolved over time. Based on these factors and personal experience, we created a series of questions that may be asked of historical data, prior to their use. Based on the answers to these questions, various statistical approaches are recommended. The strategy is illustrated with a case study in colorectal cancer.

RESULTS

A number of areas need to be considered with historical data, which we split into three categories: outcome measurement, study/patient characteristics (including setting and inclusion/exclusion criteria), and disease process/intervention effects. Each of these areas may introduce issues if not appropriately handled, while some may preclude the use of historical data entirely. We present a tool (in the form of a table) for highlighting any such issues. Application of the tool to a colorectal cancer data set demonstrates under what conditions historical data could be used and what the limitations of such an analysis would be.

CONCLUSION

Historical data can be a powerful tool to augment or compare with contemporary trial data, though caution is required. We present some of the issues that may be considered when involving historical data and what (if any) statistical approaches may account for differences between studies. We recommend that, where historical data are to be used in analyses, potential differences between studies are addressed explicitly.

Empirical profile Bayesian estimation for extrapolation of historical adult data to pediatric drug development

For pediatric drug development, the clinical effectiveness of the study medication for the adult population has already been demonstrated. Given the fact that it is usually not feasible to enroll a large number of pediatric patients, appropriately leveraging historical adult data into pediatric evaluation may be critical to success of pediatric drug development. In this manuscript, we propose a new empirical Bayesian approach, profile Bayesian estimation, to dynamically borrow adult information to the evaluation of treatment effect in pediatric patients. The new approach demonstrates an attractive balance between type I error control and power gain under the transfer-ability assumption that the pediatric treatment effect size may differ from the adult treatment effect size. The decision making boundary mimics the real-world practice in pediatric drug development. In addition, the posterior mean of the proposed empirical profile Bayesian is an unbiased estimator of the true pediatric treatment effect. We compare our approach to robust mixture prior with prior weight for informative borrowing set to 0.5 or 0.9, regular Bayesian approach, and frequentist for both type I error and power.

A Bayesian decision-theoretic approach to incorporate preclinical information into phase I oncology trials

Leveraging preclinical animal data for a phase I oncology trial is appealing yet challenging. In this paper, we use animal data to improve decision-making in a model-based dose-escalation procedure. We make a proposal for how to measure and address a prior-data conflict in a sequential study with a small sample size. Animal data are incorporated via a robust two-component mixture prior for the parameters of the human dose-toxicity relationship. The weights placed on each component of the prior are chosen empirically and updated dynamically as the trial progresses and more data accrue. After completion of each cohort, we use a Bayesian decision-theoretic approach to evaluate the predictive utility of the animal data for the observed human toxicity outcomes, reflecting the degree of agreement between dose-toxicity relationships in animals and humans. The proposed methodology is illustrated through several data examples and an extensive simulation study.

Power gains by using external information in clinical trials are typically not possible when requiring strict type I error control

In the era of precision medicine, novel designs are developed to deal with flexible clinical trials that incorporate many treatment strategies for multiple diseases in one trial setting. This situation often leads to small sample sizes in disease-treatment combinations and has fostered the discussion about the benefits of borrowing of external or historical information for decision-making in these trials. Several methods have been proposed that dynamically discount the amount of information borrowed from historical data based on the conformity between historical and current data. Specifically, Bayesian methods have been recommended and numerous investigations have been performed to characterize the properties of the various borrowing mechanisms with respect to the gain to be expected in the trials. However, there is common understanding that the risk of type I error inflation exists when information is borrowed and many simulation studies are carried out to quantify this effect. To add transparency to the debate, we show that if prior information is conditioned upon and a uniformly most powerful test exists, strict control of type I error implies that no power gain is possible under any mechanism of incorporation of prior information, including dynamic borrowing. The basis of the argument is to consider the test decision function as a function of the current data even when external information is included. We exemplify this finding in the case of a pediatric arm appended to an adult trial and dichotomous outcome for various methods of dynamic borrowing from adult information to the pediatric arm. In conclusion, if use of relevant external data is desired, the requirement of strict type I error control has to be replaced by more appropriate metrics.

Why do you need a biostatistician?

The quality of medical research importantly depends, among other aspects, on a valid statistical planning of the study, analysis of the data, and reporting of the results, which is usually guaranteed by a biostatistician. However, there are several related professions next to the biostatistician, for example epidemiologists, medical informaticians and bioinformaticians. For medical experts, it is often not clear what the differences between these professions are and how the specific role of a biostatistician can be described. For physicians involved in medical research, this is problematic because false expectations often lead to frustration on both sides. Therefore, the aim of this article is to outline the tasks and responsibilities of biostatisticians in clinical trials as well as in other fields of application in medical research.

A robust Bayesian meta-analytic approach to incorporate animal data into phase I oncology trials

Before a first-in-man trial is conducted, preclinical studies are performed in animals to help characterise the safety profile of the new medicine. We propose a robust Bayesian hierarchical model to synthesise animal and human toxicity data, using scaling factors to translate doses administered to different animal species onto an equivalent human scale. After scaling doses, the parameters of dose-toxicity models intrinsic to different animal species can be interpreted on a common scale. A prior distribution is specified for each translation factor to capture uncertainty about differences between toxicity of the drug in animals and humans. Information from animals can then be leveraged to learn about the relationship between dose and risk of toxicity in a new phase I trial in humans. The model allows human dose-toxicity parameters to be exchangeable with the study-specific parameters of animal species studied so far or non-exchangeable with any of them. This leads to robust inferences, enabling the model to give greatest weight to the animal data with parameters most consistent with human parameters or discount all animal data in the case of non-exchangeability. The proposed model is illustrated using a case study and simulations. Numerical results suggest that our proposal improves the precision of estimates of the toxicity rates when animal and human data are consistent, while it discounts animal data in cases of inconsistency.

Incorporating individual historical controls and aggregate treatment effect estimates into a Bayesian survival trial: a simulation study

Background

Performing well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible. However, with the increasing availability of historical data, incorporating existing information into trials with small sample sizes is appealing in order to increase the power. Bayesian approaches enable one to incorporate historical data into a trial’s analysis through a prior distribution.

Methods

Motivated by a RCT intended to evaluate the impact on event-free survival of mifamurtide in patients with osteosarcoma, we performed a simulation study to evaluate the impact on trial operating characteristics of incorporating historical individual control data and aggregate treatment effect estimates. We used power priors derived from historical individual control data for baseline parameters of Weibull and piecewise exponential models, while we used a mixture prior to summarise aggregate information obtained on the relative treatment effect. The impact of prior-data conflicts, both with respect to the parameters and survival models, was evaluated for a set of pre-specified weights assigned to the historical information in the prior distributions.

Results

The operating characteristics varied according to the weights assigned to each source of historical information, the variance of the informative and vague component of the mixture prior and the level of commensurability between the historical and new data. When historical and new controls follow different survival distributions, we did not observe any advantage of choosing a piecewise exponential model compared to a Weibull model for the new trial analysis. However, we think that it remains appealing given the uncertainty that will often surround the shape of the survival distribution of the new data.

Conclusion

In the setting of Sarcome-13 trial, and other similar studies in rare diseases, the gains in power and accuracy made possible by incorporating different types of historical information commensurate with the new trial data have to be balanced against the risk of biased estimates and a possible loss in power if data are not commensurate. The weights allocated to the historical data have to be carefully chosen based on this trade-off. Further simulation studies investigating methods for incorporating historical data are required to generalise the findings.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0714-z) contains supplementary material, which is available to authorized users.

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK-PD) framework. While, at first sight, a Bayesian PK-PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often-overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed.

Considerations for adaptive design in pediatric clinical trials: study protocol for a systematic review, mixed-methods study, and integrated knowledge translation plan

Background

Although children have historically been excluded from clinical trials (CTs), many require medicines tested and approved in CTs, forcing health care providers to treat their pediatric patients based on extrapolated data. Unfortunately, traditional randomized CTs can be slow and resource-intensive, and they often require multi-center collaboration. However, an adaptive design (AD) framework for CTs could be used to increase the efficiency of pediatric CTs by incorporating prospectively planned modifications to CT methods without undermining the integrity or validity of the study. There are many possible adaptations, but each will have ethical, logistical, and statistical implications. It remains unclear which adaptations (or combinations thereof) will lead to real-world improvements in pediatric CT efficiency. This study will identify, evaluate, and synthesize the various regulatory, ethical, logistical, and statistical considerations and emerging issues of AD in CTs that could be used to evaluate the use of drugs in children.

Methods/design

Following the development of a peer-reviewed search strategy, a systematic review on AD in CTs will be conducted. Data on regulatory, ethical, logistic, and statistical considerations as well as population and trial design characteristics will be synthesized. A mixed-methods study including surveys and focus groups with regulators, research ethics board members, biostatisticians, clinicians, and scientists, as well as representatives from patient groups and the public will evaluate the opportunities and challenges in applying AD in trials enrolling children and propose recommendations on best practices.

Discussion

This study will deliver practical recommendations on the use of AD in pediatric CTs. Collaboration and consultation with national and global partners will ensure that our results meet the needs of researchers, regulators, and patients, both locally and globally, and that they remain current and relevant by engaging a wide variety of stakeholders. Overall, this research will enrich the knowledge base regarding if, how, and when AD can be used to answer research questions with fewer resources while still meeting the highest ethical standards and regulatory requirements for CTs. In turn, this will result in increased high-quality clinical research needed by health care providers so they have access to appropriate, population-specific evidence regarding the safe and effective use of medicines in children.

Model averaging for robust extrapolation in evidence synthesis

Extrapolation from a source to a target, eg, from adults to children, is a promising approach to utilize external information when data are sparse. In the context of meta-analyses, one is commonly faced with a small number of studies, whereas potentially relevant additional information may also be available. Here, we describe a simple extrapolation strategy using heavy-tailed mixture priors for effect estimation in meta-analysis, which effectively results in a model-averaging technique. The described method is robust in the sense that a potential prior-data conflict, ie, a discrepancy between source and target data, is explicitly anticipated. The aim of this paper is to develop a solution for this particular application to showcase the ease of implementation by providing R code, and to demonstrate the robustness of the general approach in simulations.

A Review of the New Antiepileptic Drugs for Focal-Onset Seizures in Pediatrics: Role of Extrapolation

Most antiepileptic drugs (AEDs) receive regulatory approval for children years after the drug is available in adults, encouraging off-label use of the drug in children and hindering attempts to obtain quality pediatric data in controlled trials. Extrapolating adult efficacy data to pediatrics can reduce the time between approval in adults and that in children. To extrapolate efficacy from adults to children, several assumptions must be supported, such as (1) a similar disease progression and response to interventions in adults and children, and (2) similar exposure response in adults and children. The Pediatric Epilepsy Academic Consortium for Extrapolation (PEACE) addressed these assumptions in focal-onset seizures (FOS), the most common seizure type in both adults and children. PEACE reviewed the biological and clinical evidence that supported the assumptions that children with FOS have a similar disease progression and response to intervention as adults with FOS. After age 2 years, the pathophysiological underpinnings of FOS and the biological milieu in which seizures are initiated and propagated in children, seizure semiology, electroencephalographic features, etiology and AED response to FOS in children are similar to those in adults with FOS. PEACE concluded that extrapolation of efficacy data in adults to pediatrics in FOS is supported by strong scientific and clinical evidence. However, safety and pharmacokinetic (PK) data cannot be extrapolated from adults to children. Based on extrapolation, eslicarbazepine is now approved for children with FOS, down to age 4 years. Perampanel, lacosamide and brivaracetam are now undergoing PK and safety studies for the purposes of extrapolation down to age 2 or 4 years. When done in conjunction with PK and safety investigations in children, extrapolation of adult data from adults to children can reduce the time delay between approval of effective and safe AEDs in adults and approval in children.