How to use prior knowledge and still give new data a chance?

Low-intensity swimming with blood flow restriction over 5 weeks increases VO2peak: A randomized controlled trial using Bayesian informative prior distribution

Peak oxygen uptake (VO₂peak) and speed at first (LT1, minimal lactate equivalent) and second lactate threshold (LT2 = LT1 +1.5 mmol·L^-1) are crucial swimming performance surrogates. The present randomized controlled study investigated the effects of blood flow restriction (BFR) during low-intensity swimming (LiT) on VO₂peak, LT1, and LT2. Eighteen male swimmers (22.7 ±3.0 yrs; 69.9 ±8.5 kg; 1.8 ±0.1 m) were either assigned to the BFR or control (noBFR) group. While BFR was applied during LiT, noBFR completed the identical LIT without BFR application. BFR of the upper limb was applied via customized pneumatic cuffs (75% of occlusion pressure: 135 ±10 mmHg; 8 cm cuff width). BFR training took place three times a week over 5 weeks (accumulated weekly net BFR training: 60 min·week^-1; occlusion per session: 2-times 10 min·session^-1) and was used exclusively at low intensities. VO₂peak, LT1, and LT2 diagnostics were employed. Bayesian credible intervals revealed notable VO₂peak improvements by +0.29 L·min^-1 kg^-1 (95% credible interval: -0.26 to +0.85 L·min^-1 kg^-1) when comparing BFR vs. noBFR. Speed at LT1 -0.01 m·s^-1 (-0.04 to +0.02 m·s^-1) and LT2 -0.01 m·s^-1 (-0.03 to +0.02 m·s^-1) did not change meaningfully when BFR was employed. Fifteen sessions of LIT swimming (macrocycle of 5 h over 5 weeks) with a weekly volume of 60 min with BFR application adds additional impact on VO₂peak improvement compared to noBFR LIT swimming. Occasional BFR applications should be considered as a promising means to improve relevant performance surrogates in trained swimmers. HighlightsLow-intensity swimming with blood flow restricted (BFR) induced superior peak oxygen consumption adaptations compared to non-restricted swimming training over a 5-week lasting training periodBFR and non-BFR swimming training-induced similar adaptations regarding swimming speed at first and second lactate thresholdIn conclusion, BFR served as a feasible, promising and beneficial complementary training stimuli to traditional swimming training regarding oxygen consumption adaptations.

Improving efficiency of inference in clinical trials with external control data

Suppose we are interested in the effect of a treatment in a clinical trial. The efficiency of inference may be limited due to small sample size. However, external control data are often available from historical studies. Motivated by an application to Helicobacter pylori infection, we show how to borrow strength from such data to improve efficiency of inference in the clinical trial. Under an exchangeability assumption about the potential outcome mean, we show that the semiparametric efficiency bound for estimating the average treatment effect can be reduced by incorporating both the clinical trial data and external controls. We then derive a doubly robust and locally efficient estimator. The improvement in efficiency is prominent especially when the external control data set has a large sample size and small variability. Our method allows for a relaxed overlap assumption, and we illustrate with the case where the clinical trial only contains a treated group. We also develop doubly robust and locally efficient approaches that extrapolate the causal effect in the clinical trial to the external population and the overall population. Our results also offer a meaningful implication for trial design and data collection. We evaluate the finite-sample performance of the proposed estimators via simulation. In the Helicobacter pylori infection application, our approach shows that the combination treatment has potential efficacy advantages over the triple therapy.

Data Integration in Causal Inference

Integrating data from multiple heterogeneous sources has become increasingly popular to achieve a large sample size and diverse study population. This paper reviews development in causal inference methods that combines multiple datasets collected by potentially different designs from potentially heterogeneous populations. We summarize recent advances on combining randomized clinical trial with external information from observational studies or historical controls, combining samples when no single sample has all relevant variables with application to two-sample Mendelian randomization, distributed data setting under privacy concerns for comparative effectiveness and safety research using real-world data, Bayesian causal inference, and causal discovery methods.

Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network

BACKGROUND

Synthesis of clinical effectiveness from multiple trials is a well-established component of decision-making. Time-to-event outcomes are often synthesised using the Cox proportional hazards model assuming a constant hazard ratio over time. However, with an increasing proportion of trials reporting treatment effects where hazard ratios vary over time and with differing lengths of follow-up across trials, alternative synthesis methods are needed.

OBJECTIVES

To compare and contrast five modelling approaches for synthesis of time-to-event outcomes and provide guidance on key considerations for choosing between the modelling approaches.

METHODS

The Cox proportional hazards model and five other methods of estimating treatment effects from time-to-event outcomes, which relax the proportional hazards assumption, were applied to a network of melanoma trials reporting overall survival: restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models.

RESULTS

All models fitted the melanoma network acceptably well. However, there were important differences in extrapolations of the survival curve and interpretability of the modelling constraints demonstrating the potential for different conclusions from different modelling approaches.

CONCLUSION

The restricted mean survival time, generalised gamma, piecewise exponential, fractional polynomial and Royston-Parmar models can accommodate non-proportional hazards and differing lengths of trial follow-up within a network meta-analysis of time-to-event outcomes. We recommend that model choice is informed using available and relevant prior knowledge, model transparency, graphically comparing survival curves alongside observed data to aid consideration of the reliability of the survival estimates, and consideration of how the treatment effect estimates can be incorporated within a decision model.

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.

Regulatory issues and the potential use of Bayesian approaches for early drug approval systems in Japan

Bayesian methods quantify and interpret the therapeutic effects of investigational drugs based on probability statements of the posterior distribution. However, the basic principle underlying the use of Bayesian methods in registration trials for new drug applications in Japan has not been adequately discussed. Motivated by the two drug approval systems for early approval recently enacted in Japan, we present our perspectives on the application of the Bayesian approach in registration trials in Japan. These are based on discussions among academic, industry, and regulatory experts at invited workshops. Based on the aforementioned early approval systems, we discuss putative common regulatory issues related to the use of the Bayesian approach and introduce instances of clinical trials in which the Bayesian approach is expected to be used. This article provides a well-defined premise for the discussion between industry and regulatory agencies on the use of Bayesian approaches for early drug approval in Japan.

Assessing efficacy in important subgroups in confirmatory trials: An example using Bayesian dynamic borrowing

Assessment of efficacy in important subgroups - such as those defined by sex, age, race and region - in confirmatory trials is typically performed using separate analysis of the specific subgroup. This ignores relevant information from the complementary subgroup. Bayesian dynamic borrowing uses an informative prior based on analysis of the complementary subgroup and a weak prior distribution centred on a mean of zero to construct a robust mixture prior. This combination of priors allows for dynamic borrowing of prior information; the analysis learns how much of the complementary subgroup prior information to borrow based on the consistency between the subgroup of interest and the complementary subgroup. A tipping point analysis can be carried out to identify how much prior weight needs to be placed on the complementary subgroup component of the robust mixture prior to establish efficacy in the subgroup of interest. An attractive feature of the tipping point analysis is that it enables the evidence from the source subgroup, the evidence from the target subgroup, and the combined evidence to be displayed alongside each other. This method is illustrated with an example trial in severe asthma where efficacy in the adolescent subgroup was assessed using a mixture prior combining an informative prior from the adult data in the same trial with a non-informative prior.

Bounds for the weight of external data in shrinkage estimation

Shrinkage estimation in a meta-analysis framework may be used to facilitate dynamical borrowing of information. This framework might be used to analyze a new study in the light of previous data, which might differ in their design (e.g., a randomized controlled trial and a clinical registry). We show how the common study weights arise in effect and shrinkage estimation, and how these may be generalized to the case of Bayesian meta-analysis. Next we develop simple ways to compute bounds on the weights, so that the contribution of the external evidence may be assessed a priori. These considerations are illustrated and discussed using numerical examples, including applications in the treatment of Creutzfeldt-Jakob disease and in fetal monitoring to prevent the occurrence of metabolic acidosis. The target study's contribution to the resulting estimate is shown to be bounded below. Therefore, concerns of evidence being easily overwhelmed by external data are largely unwarranted.

Implementing Historical Controls in Oncology Trials

Drug development in oncology has broadened from mainly considering randomized clinical trials to also including single-arm trials tailored for very specific subtypes of cancer. They often use historical controls, and this article discusses benefits and risks of this paradigm and provide various regulatory and statistical considerations. While leveraging the information brought by historical controls could potentially shorten development time and reduce the number of patients enrolled, a careful selection of the past studies, a prespecified statistical analysis accounting for the heterogeneity between studies, and early engagement with regulators will be key to success. Although both the European Medicines Agency and the U.S. Food and Drug Administration have already approved medicines based on nonrandomized experiments, the evidentiary package can be perceived as less comprehensive than randomized experiments. Use of historical controls, therefore, is better suited for cases of high unmet clinical need, where the disease course is well characterized and the primary endpoint is objective. IMPLICATIONS FOR PRACTICE: Incorporating historical data in single-arm oncology trials has the potential to accelerate drug development and to reduce the number of patients enrolled, compared with standard randomized controlled clinical trials. Given the lack of blinding and randomization, such an approach is better suited for cases of high unmet clinical need and/or difficult experimental situations, in which the trajectory of the disease is well characterized and the endpoint can be measured objectively. Careful pre-specification and selection of the historical data, matching of the patient characteristics with the concurrent trial data, and innovative statistical methodologies accounting for between-study variation will be needed. Early engagement with regulators (e.g., via Scientific Advice) is highly recommended.

Unruptured Arteriovenous Malformations: Do We Have an Answer After the Final Follow-Up of ARUBA? A Bayesian Viewpoint

Brain arteriovenous malformations (bAVMs) are vascular lesions that carry significant morbidity and mortality risk upon rupture. bAVM rupture causes either intracerebral or intraventricular hemorrhage, or both. In 2014, the first results of the ARUBA trial (A Randomized Trial of Unruptured Brain Arteriovenous Malformations) were published in The Lancet, causing a paradigm shift in clinical practice and suggesting the superiority of medical treatment in terms of mortality or stroke compared with any intervention designed to obliterate the AVM. In 2020, the final results of the ARUBA trial were published. In this Viewpoint, we critically review the clinical equipoise behind the trial, highlight issues regarding external validity, and place the results of the trial in the context of other results in scientific literature of bAVMs using Bayesian inference. ARUBA is a trial of decision-making, and only proper knowledge of the nuances of its interpretation within the broader context of bAVM research can lead to proper decision-making when confronted with patients with unruptured bAVMs.

Feasibility, Safety, and Outcome of Endovascular Recanalization in Childhood Stroke: The Save ChildS Study

Importance

Randomized clinical trials have shown the efficacy of thrombectomy of large intracranial vessel occlusions in adults; however, any association of therapy with clinical outcomes in children is unknown.

Objective

To evaluate the use of endovascular recanalization in pediatric patients with arterial ischemic stroke.

Design, Setting, and Participants

This retrospective, multicenter cohort study, conducted from January 1, 2000, to December 31, 2018, analyzed the databases from 27 stroke centers in Europe and the United States. Included were all pediatric patients (<18 years) with ischemic stroke who underwent endovascular recanalization. Median follow-up time was 16 months.

Exposures

Endovascular recanalization.

Main Outcomes and Measures

The decrease of the Pediatric National Institutes of Health Stroke Scale (PedNIHSS) score from admission to day 7 was the primary outcome (score range: 0 [no deficit] to 34 [maximum deficit]). Secondary clinical outcomes included the modified Rankin scale (mRS) (score range: 0 [no deficit] to 6 [death]) at 6 and 24 months and rate of complications.

Results

Seventy-three children from 27 participating stroke centers were included. Median age was 11.3 years (interquartile range [IQR], 7.0-15.0); 37 patients (51%) were boys, and 36 patients (49%) were girls. Sixty-three children (86%) received treatment for anterior circulation occlusion and 10 patients (14%) received treatment for posterior circulation occlusion; 16 patients (22%) received concomitant intravenous thrombolysis. Neurologic outcome improved from a median PedNIHSS score of 14.0 (IQR, 9.2-20.0) at admission to 4.0 (IQR, 2.0-7.3) at day 7. Median mRS score was 1.0 (IQR, 0-1.6) at 6 months and 1.0 (IQR, 0-1.0) at 24 months. One patient (1%) developed a postinterventional bleeding complication and 4 patients (5%) developed transient peri-interventional vasospasm. The proportion of symptomatic intracerebral hemorrhage events in the HERMES meta-analysis of trials with adults was 2.79 (95% CI, 0.42-6.66) and in Save ChildS was 1.37 (95% CI, 0.03-7.40).

Conclusions and Relevance

The results of this study suggest that the safety profile of thrombectomy in childhood stroke does not differ from the safety profile in randomized clinical trials for adults; most of the treated children had favorable neurologic outcomes. This study may support clinicians' practice of off-label thrombectomy in childhood stroke in the absence of high-level evidence.

Clinical Diffusion Mismatch to Select Pediatric Patients for Embolectomy 6 to 24 Hours After Stroke: An Analysis of the Save ChildS Study

OBJECTIVE

To determine whether thrombectomy is safe in children up to 24 hours after onset of symptoms when selected by mismatch between clinical deficit and infarct.

METHODS

A secondary analysis of the Save ChildS Study (January 2000-December 2018) was performed, including all pediatric patients (<18 years) diagnosed with arterial ischemic stroke who underwent endovascular recanalization at 27 European and United States stroke centers. Patients were included if they had a relevant mismatch between clinical deficit and infarct.

RESULTS

Twenty children with a median age of 10.5 (interquartile range [IQR] 7-14.6) years were included. Of those, 7 were male (35%), and median time from onset to thrombectomy was 9.8 (IQR 7.8-16.2) hours. Neurologic outcome improved from a median Pediatric NIH Stroke Scale score of 12.0 (IQR 8.8-20.3) at admission to 2.0 (IQR 1.2-6.8) at day 7. Median modified Rankin Scale (mRS) score was 1.0 (IQR 0-1.6) at 3 months and 0.0 (IQR 0-1.0) at 24 months. One patient developed transient peri-interventional vasospasm; no other complications were observed. A comparison of the mRS score to the mRS score in the DAWN and DEFUSE 3 trials revealed a higher proportion of good outcomes in the pediatric compared to the adult study population.

CONCLUSIONS

Thrombectomy in pediatric ischemic stroke in an extended time window of up to 24 hours after onset of symptoms seems safe and neurologic outcomes are generally good if patients are selected by a mismatch between clinical deficit and infarct.

CLASSIFICATION OF EVIDENCE

This study provides Class IV evidence that for children with acute ischemic stroke with a mismatch between clinical deficit and infarct size, thrombectomy is safe.

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.

Reducing Patient Burden in Clinical Trials Through the Use of Historical Controls: Appropriate Selection of Historical Data to Minimize Risk of Bias

Historical data have been used to augment or replace control arms in some rare disease and pediatric clinical trials. With greater availability of historical data and new methodology such as dynamic borrowing, the inclusion of historical data in clinical trials is an increasingly appealing approach for larger disease areas as well, as this can result in increased power and precision and can minimize the burden on patients in clinical trials. However, sponsors must assess whether the potential biases incurred with this approach outweigh the benefits and discuss this trade-off with the regulatory agencies. This paper discusses important points for the appropriate selection of historical controls for inclusion in the analysis of primary and/or key secondary endpoint(s) in clinical trials. The general steps are as follows: (1) Assess whether a trial is a suitable candidate for this approach. (2) If it is, then carefully identify appropriate historical trials to minimize selection bias. (3) Refine the historical control set if appropriate, for example, by selecting subsets of studies or patients. Identification of trial settings that are amenable to historical borrowing and selection of appropriate historical data using the principles discussed in this paper has the potential to lead to more efficient estimation and decision making. Ultimately, this efficiency gain results in lower patient burden and gets effective drugs to patients more quickly.

Commentary on the EMA Reflection Paper on the use of extrapolation in the development of medicines for paediatrics

Adopted guidelines reflect a harmonised European approach to a specific scientific issue and should reflect the most recent scientific knowledge. However, whilst EU regulations are mandatory for all member states and EU directives must be followed by national laws in line with the directive, EMA guidelines do not have legal force and alternative approaches may be taken, but these obviously require more justification. This new series of the BJCP, developed in collaboration with the EMA, aims to address this issue by providing an annotated version of some relevant EMA guidelines and regulatory documents by experts. Hopefully, this will help in promoting their diffusion and in opening a forum for discussion with our readers.

Best Practices to Maximize the Use and Reuse of Quantitative and Systems Pharmacology Models: Recommendations From the United Kingdom Quantitative and Systems Pharmacology Network

The lack of standardization in the way that quantitative and systems pharmacology (QSP) models are developed, tested, and documented hinders their reproducibility, reusability, and expansion or reduction to alternative contexts. This in turn undermines the potential impact of QSP in academic, industrial, and regulatory frameworks. This article presents a minimum set of recommendations from the UK Quantitative and Systems Pharmacology Network (UK QSP Network) to guide QSP practitioners seeking to maximize their impact, and stakeholders considering the use of QSP models in their environment.

Regulatory strategies for rare diseases under current global regulatory statutes: a discussion with stakeholders

Rare or orphan diseases often are inherited and overwhelmingly affect children. Many of these diseases have no treatments, are incurable, and have a devastating impact on patients and their families. Regulatory standards for drug approval for rare diseases must ensure that patients receive safe and efficacious treatments. However, regulatory bodies have shown flexibility in applying these standards to drug development in rare diseases, given the unique challenges that hinder efficient and effective traditional clinical trials, including low patient numbers, limited understanding of disease pathology and progression, variability in disease presentation, and a lack of established endpoints.

To take steps toward improving rare disease clinical development strategies under current global regulatory statutes, Amicus Therapeutics, Inc. and BioNJ convened a 1-day meeting that included representatives from the Food and Drug Administration (FDA), biopharmaceutical industry, and not-for-profit agencies. The meeting focused on orphan diseases in pediatric and adult patients and was intended to identify potential strategies to overcome regulatory hurdles through open collaboration.

During this meeting, several strategies were identified to minimize the limitations associated with low patient numbers in rare diseases, including the use of natural history to generate historical control data in comparisons, simulations, and identifying inclusion/exclusion criteria and appropriate endpoints. Novel approaches to clinical trial design were discussed to minimize patient exposure to placebo and to reduce the numbers of patients and clinical trials needed for providing substantial evidence. Novel statistical analysis approaches were also discussed to address the inherent challenges of small patient numbers. Areas of urgent unmet need were identified, including the need to develop registries that protect patient identities, to establish close collaboration and communication between the sponsor and regulatory bodies to address methodological and statistical challenges, to collaborate in pre-competitive opportunities within multiple sponsors and in conjunction with academia and disease-specific patient advocacy groups for optimal data sharing, and to develop harmonized guidelines for data extrapolation from source to target pediatric populations. Ultimately, these innovations will help in solving many regulatory challenges in rare disease drug development and encourage the availability of new treatments for patients with rare diseases.

How to use prior knowledge and still give new data a chance?

A common challenge for the development of drugs in rare diseases and special populations, eg, paediatrics, is the small numbers of patients that can be recruited into clinical trials. Extrapolation can be used to support development and licensing in paediatrics through the structured integration of available data in adults and prospectively generated data in paediatrics to derive conclusions that support licensing decisions in the target paediatric population. In this context, Bayesian analyses have been proposed to obtain formal proof of efficacy of a new drug or therapeutic principle by using additional information (data, opinion, or expectation), expressed through a prior distribution. However, little is said about the impact of the prior assumptions on the evaluation of outcome and prespecified strategies for decision‐making as required in the regulatory context.

On the basis of examples, we explore the use of data‐based Bayesian meta‐analytic–predictive methods and compare these approaches with common frequentist and Bayesian meta‐analysis models. Noninformative efficacy prior distributions usually do not change the conclusions irrespective of the chosen analysis method. However, if heterogeneity is considered, conclusions are highly dependent on the heterogeneity prior. When using informative efficacy priors based on previous study data in combination with heterogeneity priors, these may completely determine conclusions irrespective of the data generated in the target population. Thus, it is important to understand the impact of the prior assumptions and ensure that prospective trial data in the target population have an appropriate chance, to change prior belief to avoid trivial and potentially erroneous conclusions.