Bayesian bandit models for the design of clinical trials

Development of treatments for rare diseases is challenging due to the limited number of patients available. Since a substantial proportion of all patients may be included in the trial, the goal is to treat those patients within the trial as effectively as possible. This motivates the use of response-adaptive designs which skew allocation towards the better performing treatment(s) but often reduce the statistical power. Consequently, this raises the question of how to allocate patients in order to attain a compromise between these conflicting objectives. This can be formalised as a multi-armed bandit problem with the dynamic programming and Gittins index solutions considered here. Dynamic programming is utilised to propose a randomised design for a two-arm sequential trial with binary outcomes. This design maximises the total number of patient successes and penalises if a minimum number of patients are not allocated to each treatment so that sufficient power is achieved. Moreover, the treatment effect estimator exhibits a very small bias and mean squared error. This design is shown to be fairly robust to delays, with only a slight reduction in patient benefit. Solutions to ameliorate this loss are therefore proposed, for both the fixed and random delay settings. A design based upon the Gittins index — which is randomised and orientated towards a patient benefit objective — is proposed for normal outcomes, illustrated in the multi-armed setting where patients are allocated in blocks. Patient benefit gains are observed when using this design with a continuous outcome instead of dichotomising it. These gains persist even when missing data is imputed. Throughout, we compare the proposed designs to alternative designs via extensive simulations in a range of scenarios. This thesis helps bridge the gap between theory and practice by addressing key issues that have prevented bandit models from being implemented in practice

A Bayesian adaptive design for clinical trials in rare diseases

Development of treatments for rare diseases is challenging due to the limited number of patients available for participation. Learning about treatment effectiveness with a view to treat patients in the larger outside population, as in the traditional fixed randomised design, may not be a plausible goal. An alternative goal is to treat the patients within the trial as effectively as possible. Using the framework of finite-horizon Markov decision processes and dynamic programming (DP), a novel randomised response-adaptive design is proposed which maximises the total number of patient successes in the trial and penalises if a minimum number of patients are not recruited to each treatment arm. Several performance measures of the proposed design are evaluated and compared to alternative designs through extensive simulation studies using a recently published trial as motivation. For simplicity, a two-armed trial with binary endpoints and immediate responses is considered. Simulation results for the proposed design show that: (i) the percentage of patients allocated to the superior arm is much higher than in the traditional fixed randomised design; (ii) relative to the optimal DP design, the power is largely improved upon and (iii) it exhibits only a very small bias and mean squared error of the treatment effect estimator. Furthermore, this design is fully randomised which is an advantage from a practical point of view because it protects the trial against various sources of bias. As such, the proposed design addresses some of the key issues that have been suggested as preventing so-called bandit models from being implemented in clinical practice.We gratefully acknowledge the support of the EPSRC funded STOR-i Centre for Doctoral Training, Grant No. EP/H023151/1. Sofía S. Villar is grateful to the Biometrika Trust for its research funding. This report is independent research arising in part from Professor Jaki’s Senior Research Fellowship (NIHR-SRF-2015-08-001) supported by the National Institute for Health Research

Hybrid sample size calculations for cluster randomised trials using assurance

Sample size determination for cluster randomised trials (CRTs) is challenging as it requires robust estimation of the intra-cluster correlation coefficient (ICC). Typically, the sample size is chosen to provide a certain level of power to reject the null hypothesis in a hypothesis test. This relies on the minimal clinically important difference (MCID) and estimates for the standard deviation, ICC and possibly the coefficient of variation of the cluster size. Varying these parameters can have a strong effect on the sample size. In particular, it is sensitive to small differences in the ICC. A relevant ICC estimate is often not available, or the available estimate is imprecise. If the ICC used is far from the unknown true value, this can lead to trials which are substantially over- or under-powered. We propose a hybrid approach using Bayesian assurance to find the sample size for a CRT with a frequentist analysis. Assurance is an alternative to power which incorporates uncertainty on parameters through a prior distribution. We suggest specifying prior distributions for the standard deviation, ICC and coefficient of variation of the cluster size, while still utilising the MCID. We illustrate the approach through the design of a CRT in post-stroke incontinence. We show assurance can be used to find a sample size based on an elicited prior distribution for the ICC, when a power calculation discards all information in the prior except a single point estimate. Results show that this approach can avoid misspecifying sample sizes when prior medians for the ICC are very similar but prior distributions exhibit quite different behaviour. Assurance provides an understanding of the probability of success of a trial given an MCID and can be used to produce sample sizes which are robust to parameter uncertainty. This is especially useful when there is difficulty obtaining reliable parameter estimates.Comment: 21 pages, 4 figures and 2 table

Medicines and Healthcare products Regulatory Agency’s “Consultation on proposals for legislative changes for clinical trials”: a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing

In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals “to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines”. The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing — making anonymised individual-level clinical trial data available to other investigators for further use — is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council’s Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic

Efficient Adaptive Designs for Clinical Trials of Interventions for COVID-19.

The COVID-19 pandemic has led to an unprecedented response in terms of clinical research activity. An important part of this research has been focused on randomized controlled clinical trials to evaluate potential therapies for COVID-19. The results from this research need to be obtained as rapidly as possible. This presents a number of challenges associated with considerable uncertainty over the natural history of the disease and the number and characteristics of patients affected, and the emergence of new potential therapies. These challenges make adaptive designs for clinical trials a particularly attractive option. Such designs allow a trial to be modified on the basis of interim analysis data or stopped as soon as sufficiently strong evidence has been observed to answer the research question, without compromising the trial's scientific validity or integrity. In this article, we describe some of the adaptive design approaches that are available and discuss particular issues and challenges associated with their use in the pandemic setting. Our discussion is illustrated by details of four ongoing COVID-19 trials that have used adaptive designs

Medicines and Healthcare products Regulatory Agency's "Consultation on proposals for legislative changes for clinical trials": a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing

In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals “to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines”. The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing — making anonymised individual-level clinical trial data available to other investigators for further use — is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council’s Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic

The evolving SARS-CoV-2 epidemic in Africa: insights from rapidly expanding genomic surveillance

Investment in SARS-CoV-2 sequencing in Africa over the past year has led to a major increase in the number of sequences generated, now exceeding 100,000 genomes, used to track the pandemic on the continent. Our results show an increase in the number of African countries able to sequence domestically, and highlight that local sequencing enables faster turnaround time and more regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and shed light on the distinct dispersal dynamics of Variants of Concern, particularly Alpha, Beta, Delta, and Omicron, on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve, while the continent faces many emerging and re-emerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa

[Figure: see text]

Comprehensive Cancer-Predisposition Gene Testing in an Adult Multiple Primary Tumor Series Shows a Broad Range of Deleterious Variants and Atypical Tumor Phenotypes.

Multiple primary tumors (MPTs) affect a substantial proportion of cancer survivors and can result from various causes, including inherited predisposition. Currently, germline genetic testing of MPT-affected individuals for variants in cancer-predisposition genes (CPGs) is mostly targeted by tumor type. We ascertained pre-assessed MPT individuals (with at least two primary tumors by age 60 years or at least three by 70 years) from genetics centers and performed whole-genome sequencing (WGS) on 460 individuals from 440 families. Despite previous negative genetic assessment and molecular investigations, pathogenic variants in moderate- and high-risk CPGs were detected in 67/440 (15.2%) probands. WGS detected variants that would not be (or were not) detected by targeted resequencing strategies, including low-frequency structural variants (6/440 [1.4%] probands). In most individuals with a germline variant assessed as pathogenic or likely pathogenic (P/LP), at least one of their tumor types was characteristic of variants in the relevant CPG. However, in 29 probands (42.2% of those with a P/LP variant), the tumor phenotype appeared discordant. The frequency of individuals with truncating or splice-site CPG variants and at least one discordant tumor type was significantly higher than in a control population (χ2 = 43.642; p ≤ 0.0001). 2/67 (3%) probands with P/LP variants had evidence of multiple inherited neoplasia allele syndrome (MINAS) with deleterious variants in two CPGs. Together with variant detection rates from a previous series of similarly ascertained MPT-affected individuals, the present results suggest that first-line comprehensive CPG analysis in an MPT cohort referred to clinical genetics services would detect a deleterious variant in about a third of individuals.JW is supported by a Cancer Research UK Cambridge Cancer Centre Clinical Research Training Fellowship. Funding for the NIHR BioResource – Rare diseases project was provided by the National Institute for Health Research (NIHR, grant number RG65966). ERM acknowledges support from the European Research Council (Advanced Researcher Award), NIHR (Senior Investigator Award and Cambridge NIHR Biomedical Research Centre), Cancer Research UK Cambridge Cancer Centre and Medical Research Council Infrastructure Award. The University of Cambridge has received salary support in respect of EM from the NHS in the East of England through the Clinical Academic Reserve. The views expressed are those of the authors and not necessarily those of the NHS or Department of Health. DGE is an NIHR Senior Investigator and is supported by the all Manchester NIHR Biomedical Research Centre

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance.

Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern-particularly Alpha, Beta, Delta, and Omicron-on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century