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  • 1
    In: British Journal of Cancer, Springer Science and Business Media LLC, Vol. 118, No. 6 ( 2018-3), p. 793-801
    Type of Medium: Online Resource
    ISSN: 0007-0920 , 1532-1827
    RVK:
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    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
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  • 2
    Online Resource
    Online Resource
    SAGE Publications ; 2017
    In:  Clinical Trials Vol. 14, No. 1 ( 2017-02), p. 78-87
    In: Clinical Trials, SAGE Publications, Vol. 14, No. 1 ( 2017-02), p. 78-87
    Abstract: Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.
    Type of Medium: Online Resource
    ISSN: 1740-7745 , 1740-7753
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2017
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  • 3
    Online Resource
    Online Resource
    SAGE Publications ; 2020
    In:  Statistical Methods in Medical Research Vol. 29, No. 9 ( 2020-09), p. 2583-2602
    In: Statistical Methods in Medical Research, SAGE Publications, Vol. 29, No. 9 ( 2020-09), p. 2583-2602
    Abstract: Within paediatric populations, there may be distinct age groups characterised by different exposure–response relationships. Several regulatory guidance documents have suggested general age groupings. However, it is not clear whether these categorisations will be suitable for all new medicines and in all disease areas. We consider two model-based approaches to quantify how exposure–response model parameters vary over a continuum of ages: Bayesian penalised B-splines and model-based recursive partitioning. We propose an approach for deriving an optimal dosing rule given an estimate of how exposure–response model parameters vary with age. Methods are initially developed for a linear exposure–response model. We perform a simulation study to systematically evaluate how well the various approaches estimate linear exposure–response model parameters and the accuracy of recommended dosing rules. Simulation scenarios are motivated by an application to epilepsy drug development. Results suggest that both bootstrapped model-based recursive partitioning and Bayesian penalised B-splines can estimate underlying changes in linear exposure–response model parameters as well as (and in many scenarios, better than) a comparator linear model adjusting for a categorical age covariate with levels following International Conference on Harmonisation E11 groupings. Furthermore, the Bayesian penalised B-splines approach consistently estimates the intercept and slope more accurately than the bootstrapped model-based recursive partitioning. Finally, approaches are extended to estimate Emax exposure–response models and are illustrated with an example motivated by an in vitro study of cyclosporine.
    Type of Medium: Online Resource
    ISSN: 0962-2802 , 1477-0334
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
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    detail.hit.zdb_id: 1136948-6
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  • 4
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    Online Resource
    SAGE Publications ; 2020
    In:  Statistical Methods in Medical Research Vol. 29, No. 1 ( 2020-01), p. 94-110
    In: Statistical Methods in Medical Research, SAGE Publications, Vol. 29, No. 1 ( 2020-01), p. 94-110
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0962-2802 , 1477-0334
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
    detail.hit.zdb_id: 2001539-2
    detail.hit.zdb_id: 1136948-6
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  • 5
    Online Resource
    Online Resource
    SAGE Publications ; 2021
    In:  Statistical Methods in Medical Research Vol. 30, No. 4 ( 2021-04), p. 1057-1071
    In: Statistical Methods in Medical Research, SAGE Publications, Vol. 30, No. 4 ( 2021-04), p. 1057-1071
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 0962-2802 , 1477-0334
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
    detail.hit.zdb_id: 2001539-2
    detail.hit.zdb_id: 1136948-6
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  • 6
    Online Resource
    Online Resource
    SAGE Publications ; 2018
    In:  Statistical Methods in Medical Research Vol. 27, No. 2 ( 2018-02), p. 398-413
    In: Statistical Methods in Medical Research, SAGE Publications, Vol. 27, No. 2 ( 2018-02), p. 398-413
    Abstract: When developing new medicines for children, the potential to extrapolate from adult data to reduce the experimental burden in children is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. We reviewed the literature to identify statistical methods that could be used to optimise extrapolations in paediatric drug development programmes. Methods: Web of Science was used to identify papers proposing methods relevant for using data from a ‘source population’ to support inferences for a ‘target population’. Four key areas of methods development were targeted: paediatric clinical trials, trials extrapolating efficacy across ethnic groups or geographic regions, the use of historical data in contemporary clinical trials and using short-term endpoints to support inferences about long-term outcomes. Results: Searches identified 626 papers of which 52 met our inclusion criteria. From these we identified 102 methods comprising 58 Bayesian and 44 frequentist approaches. Most Bayesian methods (n = 54) sought to use existing data in the source population to create an informative prior distribution for a future clinical trial. Of these, 46 allowed the source data to be down-weighted to account for potential differences between populations. Bayesian and frequentist versions of methods were found for assessing whether key parameters of source and target populations are commensurate (n = 34). Fourteen frequentist methods synthesised data from different populations using a joint model or a weighted test statistic. Conclusions: Several methods were identified as potentially applicable to paediatric drug development. Methods which can accommodate a heterogeneous target population and which allow data from a source population to be down-weighted are preferred. Methods assessing the commensurability of parameters may be used to determine whether it is appropriate to pool data across age groups to estimate treatment effects.
    Type of Medium: Online Resource
    ISSN: 0962-2802 , 1477-0334
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2018
    detail.hit.zdb_id: 2001539-2
    detail.hit.zdb_id: 1136948-6
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  • 7
    In: Health Technology Assessment, National Institute for Health and Care Research, Vol. 23, No. 60 ( 2019-10), p. 1-88
    Abstract: The randomised controlled trial is widely considered to be the gold standard study for comparing the effectiveness of health interventions. Central to its design is a calculation of the number of participants needed (the sample size) for the trial. The sample size is typically calculated by specifying the magnitude of the difference in the primary outcome between the intervention effects for the population of interest. This difference is called the ‘target difference’ and should be appropriate for the principal estimand of interest and determined by the primary aim of the study. The target difference between treatments should be considered realistic and/or important by one or more key stakeholder groups. Objective The objective of the report is to provide practical help on the choice of target difference used in the sample size calculation for a randomised controlled trial for researchers and funder representatives. Methods The Difference ELicitation in TriAls 2 (DELTA 2 ) recommendations and advice were developed through a five-stage process, which included two literature reviews of existing funder guidance and recent methodological literature; a Delphi process to engage with a wider group of stakeholders; a 2-day workshop; and finalising the core document. Results Advice is provided for definitive trials (Phase III/IV studies). Methods for choosing the target difference are reviewed. To aid those new to the topic, and to encourage better practice, 10 recommendations are made regarding choosing the target difference and undertaking a sample size calculation. Recommended reporting items for trial proposal, protocols and results papers under the conventional approach are also provided. Case studies reflecting different trial designs and covering different conditions are provided. Alternative trial designs and methods for choosing the sample size are also briefly considered. Conclusions Choosing an appropriate sample size is crucial if a study is to inform clinical practice. The number of patients recruited into the trial needs to be sufficient to answer the objectives; however, the number should not be higher than necessary to avoid unnecessary burden on patients and wasting precious resources. The choice of the target difference is a key part of this process under the conventional approach to sample size calculations. This document provides advice and recommendations to improve practice and reporting regarding this aspect of trial design. Future work could extend the work to address other less common approaches to the sample size calculations, particularly in terms of appropriate reporting items. Funding Funded by the Medical Research Council (MRC) UK and the National Institute for Health Research as part of the MRC–National Institute for Health Research Methodology Research programme.
    Type of Medium: Online Resource
    ISSN: 1366-5278 , 2046-4924
    Language: English
    Publisher: National Institute for Health and Care Research
    Publication Date: 2019
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    detail.hit.zdb_id: 2006765-3
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  • 8
    In: BMJ Open, BMJ, Vol. 9, No. 5 ( 2019-05), p. e025877-
    Abstract: The controversial results on the mifamurtide efficacy associated with chemotherapy, issued from the American INT-0133-study, in localised osteosarcomas, and the underpowered analysis performed separately in metastatic patients, should be clarified to homogenise international use of this promising drug. The European Commission has granted a marketing authorisation to mifamurtide combined with postoperative chemotherapy in localised osteosarcomas but not in metastatic patients, while the Food and Drug Administration (FDA) has denied this authorisation. Methods and analysis Sarcome-13/OS2016 trial is a multicentre randomised open-label phase II trial evaluating the survival benefit of mifamurtide administered during 36 weeks in combination with postoperative chemotherapy versus chemotherapy alone, in patients 〉 2 and ≤50 years with newly diagnosed high-risk localised or metastatic osteosarcoma. The main objective is to evaluate the impact on event-free survival (EFS) of mifamurtide on intention-to-treat population. The secondary objectives are to evaluate the impact of mifamurtide on overall survival, to evaluate the feasibility and toxicity of the planned treatment, to correlate biology/immunology with the mifamurtide efficacy/toxicity. With a total of 126 enrolled patients and 51 events, the power is 80% if mifamurtide is associated with an 18% improvement of the 3-year EFS (52%vs70%, equivalent to an HR=0.55), with a one-sided logrank test alpha=10%. As relevant historical data are available (aggregate treatment effect from the INT-0133 trial and individual data from the control group of the Sarcome-09/OS2006 trial), a Bayesian analysis is also planned. Ethics and dissemination This study was approved by the ‘Comité de Protection des Personnes Ile de France I’ (12/06/2018), complies with the Declaration of Helsinki and French laws and regulations, and follows the International Conference on Harmonisation E6 Guideline for Good Clinical Practice. The trial results, even if they are inconclusive, as well as biological ancillary studies will be presented at appropriate international congresses and published in international peer-review journals. Trial registration number EudraCT 2017-001165-24, NCT03643133
    Type of Medium: Online Resource
    ISSN: 2044-6055 , 2044-6055
    Language: English
    Publisher: BMJ
    Publication Date: 2019
    detail.hit.zdb_id: 2599832-8
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  • 9
    In: BMJ, BMJ
    Type of Medium: Online Resource
    ISSN: 0959-8138 , 1756-1833
    Language: English
    Publisher: BMJ
    Publication Date: 2018
    detail.hit.zdb_id: 1362901-3
    detail.hit.zdb_id: 1479799-9
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  • 10
    Online Resource
    Online Resource
    SAGE Publications ; 2018
    In:  Statistical Methods in Medical Research Vol. 27, No. 12 ( 2018-12), p. 3612-3627
    In: Statistical Methods in Medical Research, SAGE Publications, Vol. 27, No. 12 ( 2018-12), p. 3612-3627
    Abstract: Just over half of publicly funded trials recruit their target sample size within the planned study duration. When recruitment targets are missed, the funder of a trial is faced with the decision of either committing further resources to the study or risk that a worthwhile treatment effect may be missed by an underpowered final analysis. To avoid this challenging situation, when there is insufficient prior evidence to support predicted recruitment rates, funders now require feasibility assessments to be performed in the early stages of trials. Progression criteria are usually specified and agreed with the funder ahead of time. To date, however, the progression rules used are typically ad hoc. In addition, rules routinely permit adaptations to recruitment strategies but do not stipulate criteria for evaluating their effectiveness. In this paper, we develop a framework for planning and designing internal pilot studies which permit a trial to be stopped early if recruitment is disappointing or to continue to full recruitment if enrolment during the feasibility phase is adequate. This framework enables a progression rule to be pre-specified and agreed upon prior to starting a trial. The novel two-stage designs stipulate that if neither of these situations arises, adaptations to recruitment should be made and subsequently evaluated to establish whether they have been successful. We derive optimal progression rules for internal pilot studies which minimise the expected trial overrun and maintain a high probability of completing the study when the recruitment rate is adequate. The advantages of this procedure are illustrated using a real trial example.
    Type of Medium: Online Resource
    ISSN: 0962-2802 , 1477-0334
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2018
    detail.hit.zdb_id: 2001539-2
    detail.hit.zdb_id: 1136948-6
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