1
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Burnett T, König F, Jaki T. Adding experimental treatment arms to multi-arm multi-stage platform trials in progress. Stat Med 2024; 43:3447-3462. [PMID: 38852991 DOI: 10.1002/sim.10090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 01/16/2024] [Accepted: 04/15/2024] [Indexed: 06/11/2024]
Abstract
Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures.
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Affiliation(s)
- Thomas Burnett
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | - Franz König
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Faculty of Computer Science and Data Science, University of Regensburg, Regensburg, Germany
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2
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Dufault SM, Crook AM, Rolfe K, Phillips PPJ. A flexible multi-metric Bayesian framework for decision-making in Phase II multi-arm multi-stage studies. Stat Med 2024; 43:501-513. [PMID: 38038137 DOI: 10.1002/sim.9961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 09/19/2023] [Accepted: 10/30/2023] [Indexed: 12/02/2023]
Abstract
We propose a multi-metric flexible Bayesian framework to support efficient interim decision-making in multi-arm multi-stage phase II clinical trials. Multi-arm multi-stage phase II studies increase the efficiency of drug development, but early decisions regarding the futility or desirability of a given arm carry considerable risk since sample sizes are often low and follow-up periods may be short. Further, since intermediate outcomes based on biomarkers of treatment response are rarely perfect surrogates for the primary outcome and different trial stakeholders may have different levels of risk tolerance, a single hypothesis test is insufficient for comprehensively summarizing the state of the collected evidence. We present a Bayesian framework comprised of multiple metrics based on point estimates, uncertainty, and evidence towards desired thresholds (a Target Product Profile) for (1) ranking of arms and (2) comparison of each arm against an internal control. Using a large public-private partnership targeting novel TB arms as a motivating example, we find via simulation study that our multi-metric framework provides sufficient confidence for decision-making with sample sizes as low as 30 patients per arm, even when intermediate outcomes have only moderate correlation with the primary outcome. Our reframing of trial design and the decision-making procedure has been well-received by research partners and is a practical approach to more efficient assessment of novel therapeutics.
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Affiliation(s)
- Suzanne M Dufault
- Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- UCSF Center for Tuberculosis, University of California, San Francisco, CA, USA
| | - Angela M Crook
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, London, UK
| | | | - Patrick P J Phillips
- UCSF Center for Tuberculosis, University of California, San Francisco, CA, USA
- Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, San Francisco, CA, USA
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3
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Serra A, Mozgunov P, Davies G, Jaki T. Determining the minimum duration of treatment in tuberculosis: An order restricted non-inferiority trial design. Pharm Stat 2023; 22:938-962. [PMID: 37415394 DOI: 10.1002/pst.2320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 04/22/2023] [Accepted: 06/20/2023] [Indexed: 07/08/2023]
Abstract
Tuberculosis (TB) is one of the biggest killers among infectious diseases worldwide. Together with the identification of drugs that can provide benefits to patients, the challenge in TB is also the optimisation of the duration of these treatments. While conventional duration of treatment in TB is 6 months, there is evidence that shorter durations might be as effective but could be associated with fewer side effects and may be associated with better adherence. Based on a recent proposal of an adaptive order-restricted superiority design that employs the ordering assumptions within various duration of the same drug, we propose a non-inferiority (typically used in TB trials) adaptive design that effectively uses the order assumption. Together with the general construction of the hypothesis testing and expression for type I and type II errors, we focus on how the novel design was proposed for a TB trial concept. We consider a number of practical aspects such as choice of the design parameters, randomisation ratios, and timings of the interim analyses, and how these were discussed with the clinical team.
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Affiliation(s)
| | - Pavel Mozgunov
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Geraint Davies
- Department of Clinical Infection, Microbiology and Immunology, Institute of Infection, Veterinary & Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
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4
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Serra A, Mozgunov P, Jaki T. A Bayesian multi-arm multi-stage clinical trial design incorporating information about treatment ordering. Stat Med 2023; 42:2841-2854. [PMID: 37158302 PMCID: PMC10962588 DOI: 10.1002/sim.9752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 01/27/2023] [Accepted: 04/13/2023] [Indexed: 05/10/2023]
Abstract
Multi-Arm Multi-Stage (MAMS) designs can notably improve efficiency in later stages of drug development, but they can be suboptimal when an order in the effects of the arms can be assumed. In this work, we propose a Bayesian multi-arm multi-stage trial design that selects all promising treatments with high probability and can efficiently incorporate information about the order in the treatment effects as well as incorporate prior knowledge on the treatments. A distinguishing feature of the proposed design is that it allows taking into account the uncertainty of the treatment effect order assumption and does not assume any parametric arm-response model. The design can provide control of the family-wise error rate under specific values of the control mean and we illustrate its operating characteristics in a study of symptomatic asthma. Via simulations, we compare the novel Bayesian design with frequentist multi-arm multi-stage designs and a frequentist order restricted design that does not account for the order uncertainty and demonstrate the gains in the sample sizes the proposed design can provide. We also find that the proposed design is robust to violations of the assumptions on the order.
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Affiliation(s)
| | - Pavel Mozgunov
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
| | - Thomas Jaki
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Faculty for Informatics and Data ScienceUniversity of RegensburgRegensburgGermany
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5
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Robertson DS, Choodari-Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T. Point estimation for adaptive trial designs II: Practical considerations and guidance. Stat Med 2023; 42:2496-2520. [PMID: 37021359 PMCID: PMC7614609 DOI: 10.1002/sim.9734] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 01/20/2023] [Accepted: 03/18/2023] [Indexed: 04/07/2023]
Abstract
In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred.
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Affiliation(s)
| | - Babak Choodari-Oskooei
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Munya Dimairo
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Laura Flight
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | | | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
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6
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Lee KM, Robertson DS, Jaki T, Emsley R. The benefits of covariate adjustment for adaptive multi-arm designs. Stat Methods Med Res 2022; 31:2104-2121. [PMID: 35876412 PMCID: PMC7613816 DOI: 10.1177/09622802221114544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Covariate adjustment via a regression approach is known to increase the precision
of statistical inference when fixed trial designs are employed in randomized
controlled studies. When an adaptive multi-arm design is employed with the
ability to select treatments, it is unclear how covariate adjustment affects
various aspects of the study. Consider the design framework that relies on
pre-specified treatment selection rule(s) and a combination test approach for
hypothesis testing. It is our primary goal to evaluate the impact of covariate
adjustment on adaptive multi-arm designs with treatment selection. Our secondary
goal is to show how the Uniformly Minimum Variance Conditionally Unbiased
Estimator can be extended to account for covariate adjustment analytically. We
find that adjustment with different sets of covariates can lead to different
treatment selection outcomes and hence probabilities of rejecting hypotheses.
Nevertheless, we do not see any negative impact on the control of the familywise
error rate when covariates are included in the analysis model. When adjusting
for covariates that are moderately or highly correlated with the outcome, we see
various benefits to the analysis of the design. Conversely, there is negligible
impact when including covariates that are uncorrelated with the outcome.
Overall, pre-specification of covariate adjustment is recommended for the
analysis of adaptive multi-arm design with treatment selection. Having the
statistical analysis plan in place prior to the interim and final analyses is
crucial, especially when a non-collapsible measure of treatment effect is
considered in the trial.
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Affiliation(s)
- Kim May Lee
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Thomas Jaki
- 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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7
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Serra A, Mozgunov P, Jaki T. An order restricted multi-arm multi-stage clinical trial design. Stat Med 2022; 41:1613-1626. [PMID: 35048391 PMCID: PMC7612618 DOI: 10.1002/sim.9314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/06/2021] [Accepted: 12/20/2021] [Indexed: 11/09/2022]
Abstract
One family of designs that can noticeably improve efficiency in later stages of drug development are multi-arm multi-stage (MAMS) designs. They allow several arms to be studied concurrently and gain efficiency by dropping poorly performing treatment arms during the trial as well as by allowing to stop early for benefit. Conventional MAMS designs were developed for the setting, in which treatment arms are independent and hence can be inefficient when an order in the effects of the arms can be assumed (eg, when considering different treatment durations or different doses). In this work, we extend the MAMS framework to incorporate the order of treatment effects when no parametric dose-response or duration-response model is assumed. The design can identify all promising treatments with high probability. We show that the design provides strong control of the family-wise error rate and illustrate the design in a study of symptomatic asthma. Via simulations we show that the inclusion of the ordering information leads to better decision-making compared to a fixed sample and a MAMS design. Specifically, in the considered settings, reductions in sample size of around 15% were achieved in comparison to a conventional MAMS design.
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Affiliation(s)
| | - Pavel Mozgunov
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.,Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
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8
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Lee KM, Brown LC, Jaki T, Stallard N, Wason J. Statistical consideration when adding new arms to ongoing clinical trials: the potentials and the caveats. Trials 2021; 22:203. [PMID: 33691748 PMCID: PMC7944243 DOI: 10.1186/s13063-021-05150-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 02/24/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Platform trials improve the efficiency of the drug development process through flexible features such as adding and dropping arms as evidence emerges. The benefits and practical challenges of implementing novel trial designs have been discussed widely in the literature, yet less consideration has been given to the statistical implications of adding arms. MAIN: We explain different statistical considerations that arise from allowing new research interventions to be added in for ongoing studies. We present recent methodology development on addressing these issues and illustrate design and analysis approaches that might be enhanced to provide robust inference from platform trials. We also discuss the implication of changing the control arm, how patient eligibility for different arms may complicate the trial design and analysis, and how operational bias may arise when revealing some results of the trials. Lastly, we comment on the appropriateness and the application of platform trials in phase II and phase III settings, as well as publicly versus industry-funded trials. CONCLUSION Platform trials provide great opportunities for improving the efficiency of evaluating interventions. Although several statistical issues are present, there are a range of methods available that allow robust and efficient design and analysis of these trials.
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Affiliation(s)
- Kim May Lee
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, UK.
- Pragmatic Clinical Trials Unit, Queen Mary University of London, Yvonne Carter Building, 58 Turner Street, London, E1 2AB, UK.
| | - Louise C Brown
- MRC Clinical Trials Unit, University College London, 90 High Holborn 2nd Floor, London, WC1V 6LJ, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, UK
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, CV4 7AL, UK
| | - James Wason
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, UK
- Population Health Sciences Institute, Baddiley-Clark Building, Newcastle University, Richardson Road, Newcastle upon Tyne, UK
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9
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Alrubaiee GG, Baharom A, Faisal I, Shahar HK, Daud SM, Basaleem HO. Implementation of an educational module on nosocomial infection control measures: a randomised hospital-based trial. BMC Nurs 2021; 20:33. [PMID: 33596894 PMCID: PMC7890621 DOI: 10.1186/s12912-021-00551-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 02/07/2021] [Indexed: 02/05/2023] Open
Abstract
Background Previous cross-sectional studies have reported limited knowledge and practices among nurses regarding controlling nosocomial infections (NIs). Even though health institutions offer many irregular in-service training courses to solve such issues, a three year-nursing educational programme at institutions is not adequate to enable nurses to handle NIs. Therefore, this study aims to evaluate the implementation of an educational module on NIs control measures among Yemeni nurses. Methods A single-blinded randomised hospital-based trial was undertaken involving 540 nurses assigned to two intervention groups and a waitlist group. Intervention group-1 received a face-to-face training course comprising 20 h spread over six weeks and a hard copy of the module, while intervention group-2 only received the hard copy of the module “without training”. In contrast, the waitlist group did not receive anything during the period of collecting data. A self-administered NI control measures-evaluation questionnaire was utilised in collecting the data from the participants; before the intervention, at six weeks and 3 months after the end of the intervention. The period of data collection was between 1st May and 30th October 2016. Results The results from collecting and analysing the data showed a statistically significant difference in the mean knowledge scores between the intervention groups that were detectable immediately post-intervention with a mean difference (MD) of 4.31 (P < 0.001) and 3 months after the end of the intervention (MD = 4.48, P < 0.001) as compared to the waitlist group. Similarly, the results showed a statistically significant difference in the mean practice scores between the intervention groups immediately post-intervention (MD = 2.74, P < 0.001) and 3 months after the intervention (MD = 2.46, P < 0.001) as compared to the waitlist group. Intervention-1 (face-to-face training + module) was more effective than intervention-2 (module only) in improving Yemeni nurses’ knowledge and practices regarding NI control measures compared to the waitlist group. Conclusion The findings of this study found that intervention-1 could be offered to nurses in the form of an in-service training course every six months. The NI course should also be included in nursing curricula, particularly for the three-year-nursing diploma in Yemen. Trial registration Nosocomial infection educational module for nurses ISRCTN19992640, 20/6/2017. The study protocol was retrospectively registered. Supplementary Information The online version contains supplementary material available at 10.1186/s12912-021-00551-0.
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Affiliation(s)
- Gamil Ghaleb Alrubaiee
- Department of Applied Medical Sciences, Faculty of Medical Sciences, Al-Razi University, Sana'a, Yemen.
| | - Anisah Baharom
- Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra, Seri Kembangan, Malaysia
| | - Ibrahim Faisal
- Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra, Seri Kembangan, Malaysia
| | - Hayati Kadir Shahar
- Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra, Seri Kembangan, Malaysia
| | - Shaffe Mohd Daud
- Department of Foundations of Education, Faculty of Educational Studies, Universiti Putra, Seri Kembangan, Malaysia
| | - Huda Omer Basaleem
- Department of Community Medicine and Public Health, Faculty of Medicine and Health Sciences, University of Aden, Aden, Yemen
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10
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Burnett T, Mozgunov P, Pallmann P, Villar SS, Wheeler GM, Jaki T. Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs. BMC Med 2020; 18:352. [PMID: 33208155 PMCID: PMC7677786 DOI: 10.1186/s12916-020-01808-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/07/2020] [Indexed: 12/18/2022] Open
Abstract
Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.
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Affiliation(s)
- Thomas Burnett
- Department of Mathematics and Statistics, Lancaster University, Fylde College, Lancaster, LA1 4YF UK
| | - Pavel Mozgunov
- Department of Mathematics and Statistics, Lancaster University, Fylde College, Lancaster, LA1 4YF UK
| | - Philip Pallmann
- Centre for Trials Research, College of Biomedical & Life Sciences, Cardiff University, Cardiff, UK
| | - Sofia S. Villar
- MRC Biostatistics Unit, University of Cambridge School of Clinical Medicine, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR UK
| | - Graham M. Wheeler
- Cancer Research UK & UCL Cancer Trials Centre, University College London, 90 Tottenham Court Road, London, W1T 4TJ UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Fylde College, Lancaster, LA1 4YF UK
- MRC Biostatistics Unit, University of Cambridge School of Clinical Medicine, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR UK
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11
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Dimairo M, Pallmann P, Wason J, Todd S, Jaki T, Julious SA, Mander AP, Weir CJ, Koenig F, Walton MK, Nicholl JP, Coates E, Biggs K, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. Trials 2020; 21:528. [PMID: 32546273 PMCID: PMC7298968 DOI: 10.1186/s13063-020-04334-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites."To maximise the benefit to society, you need to not just do research but do it well" Douglas G Altman.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK.
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Institute of Health and Society, Newcastle University, Newcastle, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Reading, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Adrian P Mander
- Centre for Trials Research, Cardiff University, Cardiff, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Franz Koenig
- Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Marc K Walton
- Janssen Pharmaceuticals, Titusville, New Jersey, USA
| | - Jon P Nicholl
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | | | - Michael A Proschan
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | - John A Scott
- Division of Biostatistics in the Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Tokyo, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Douglas G Altman
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
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12
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Dimairo M, Pallmann P, Wason J, Todd S, Jaki T, Julious SA, Mander AP, Weir CJ, Koenig F, Walton MK, Nicholl JP, Coates E, Biggs K, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. BMJ 2020; 369:m115. [PMID: 32554564 PMCID: PMC7298567 DOI: 10.1136/bmj.m115] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/19/2019] [Indexed: 12/11/2022]
Abstract
Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, UK
- Institute of Health and Society, Newcastle University, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Adrian P Mander
- Centre for Trials Research, Cardiff University, UK
- MRC Biostatistics Unit, University of Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, UK
| | - Franz Koenig
- Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Austria
| | | | - Jon P Nicholl
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | | | - Michael A Proschan
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA
| | - John A Scott
- Division of Biostatistics in the Center for Biologics Evaluation and Research, Food and Drug Administration, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
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13
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Bostrøm K, Mæhlum S, Cvancarova Småstuen M, Storheim K. Clinical comparative effectiveness of acupuncture versus manual therapy treatment of lateral epicondylitis: feasibility randomized clinical trial. Pilot Feasibility Stud 2019; 5:110. [PMID: 31516727 PMCID: PMC6731611 DOI: 10.1186/s40814-019-0490-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 08/14/2019] [Indexed: 02/02/2023] Open
Abstract
Background Lateral epicondylitis (LE) is a challenging condition for clinicians, and research has yet not proven the superiority of one specific treatment approach. However, manual therapy (elbow mobilization) in addition to eccentric exercise has been found to be superior to exercise alone. As well, acupuncture is effective in short-term pain relief when compared with sham treatment, but there is little knowledge on the comparative effectiveness of manual therapy and acupuncture treatment of LE in terms of pain relief. The primary objective of this pilot trial was to assess the feasibility (retention and adherence rates) of performing a randomized controlled trial (RCT) to explore the clinical effectiveness of acupuncture and manual therapy treatment of LE. Methods This pilot trial took place in an outpatient interdisciplinary institute of sports medicine and rehabilitation in Oslo, Norway. Thirty-six adults with clinically diagnosed LE were randomly allocated into one of three groups: eccentric exercise alone, eccentric exercise plus acupuncture, or eccentric exercise plus manual therapy for a 12-week treatment period. Primary outcomes were patient retention and adherence rates. Secondary outcomes included patient-reported pain (NRS), level of disability (Quick-DASH), and participant’s satisfaction with treatment and global perceived effect. Results Nine (69%) patients in the acupuncture group completed the 1-year follow-up, compared to eight (67%) in the manual therapy group and five (45%) in exercise alone. Our goal was to demonstrate a retention rate above 80% to avoid serious threats to validity, but the result was lower than expected. The majority of participants (64%) in both treatment groups received only three-treatment sessions; the reasons included non-attendance or recovery from pain. Secondary outcomes support the rationale for conduction of an RCT. There were no adverse advents related to study participation. Conclusions Based on differences in pain relief between groups, patient retention, and adherence rates, an RCT seems to be feasible to assess treatment effectiveness more precisely. In a future definitive trial, greater dropout may be reduced by maintaining contact with the participants in the exercise alone group throughout the intervention, and objective assessments might be considered. Trial registration ClinicalTrials.gov, NCT02321696
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Affiliation(s)
- Katrine Bostrøm
- Norwegian Institute of Sports Medicine (NIMI), Sognsveien 75D, O805 Oslo, Norway.,2Faculty of Medicine, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Sverre Mæhlum
- Norwegian Institute of Sports Medicine (NIMI), Sognsveien 75D, O805 Oslo, Norway
| | - Milada Cvancarova Småstuen
- Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.,4Research and Communication Unit for Musculoskeletal Health (FORMI), Oslo University Hospital, Oslo, Norway
| | - Kjersti Storheim
- Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.,4Research and Communication Unit for Musculoskeletal Health (FORMI), Oslo University Hospital, Oslo, Norway
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14
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Bayar MA, Le Teuff G, Koenig F, Le Deley MC, Michiels S. Group sequential adaptive designs in series of time-to-event randomised trials in rare diseases: A simulation study. Stat Methods Med Res 2019; 29:1483-1498. [PMID: 31354106 DOI: 10.1177/0962280219862313] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In rare diseases, fully powered large trials may not be doable in a reasonable time frame even with international collaborations. In a previous work, we proposed an approach based on a series of smaller parallel group two-arm randomised controlled trials (RCT) performed over a long research horizon. Within the series of trials, the treatment selected after each trial becomes the control treatment of the next one. We concluded that running more trials with smaller sample sizes and relaxed α-levels leads in the long term and under reasonable assumptions to larger survival benefits with a moderate increase of risk as compared to traditional designs based on larger but fewer trials designed to meet stringent evidence criteria. We now extend this quantitative framework with more 'flexible' designs including interim analyses for futility and/or efficacy, and three-arm adaptive designs with treatment selection at interim. In the simulation study, we considered different disease severities, accrual rates, and hypotheses of how treatments improve over time. For each design, we estimated the long-term survival benefit as the relative difference in hazard rates between the end and the start of the research horizon, and the risk defined as the probability of selecting at the end of the research horizon a treatment inferior to the initial control. We assessed the impact of the α-level and the choice of the stopping rule on the operating characteristics. We also compared the performance of series based on two- vs. three-arm trials. We show that relaxing α-levels within the limit of 0.1 is associated with larger survival gains and moderate increase of risk which remains within acceptable ranges. Including an interim analysis with a futility rule is associated with an additional survival gain and a better risk control as compared to series with no interim analysis, when the α-level is below or equal to 0.1, whereas the benefit of including an interim analysis is rather small for higher α-levels. Including an interim analysis for efficacy yields almost no additional gain. Series based on three-arm trials are associated with a systematic improvement in terms of survival gain and risk control as compared to series of two-arm trials.
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Affiliation(s)
- Mohamed Amine Bayar
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Villejuif, France.,CESP, Faculté de médecine - Université Paris-Sud, Faculté de médecine - INSERM, Université Paris-Saclay, Villejuif, France
| | - Gwénaël Le Teuff
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Villejuif, France.,CESP, Faculté de médecine - Université Paris-Sud, Faculté de médecine - INSERM, Université Paris-Saclay, Villejuif, France
| | - Franz Koenig
- Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Marie-Cécile Le Deley
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Villejuif, France.,CESP, Faculté de médecine - Université Paris-Sud, Faculté de médecine - INSERM, Université Paris-Saclay, Villejuif, France.,Unité de Méthodologie et Biostatistique, Centre Oscar Lambret, Lille, France
| | - Stefan Michiels
- Service de Biostatistique et d'Épidémiologie, Gustave Roussy, Villejuif, France.,CESP, Faculté de médecine - Université Paris-Sud, Faculté de médecine - INSERM, Université Paris-Saclay, Villejuif, France
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15
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Scotina AD, Beaudoin FL, Gutman R. Matching estimators for causal effects of multiple treatments. Stat Methods Med Res 2019; 29:1051-1066. [PMID: 31138025 DOI: 10.1177/0962280219850858] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Matching estimators for average treatment effects are widely used in the binary treatment setting, in which missing potential outcomes are imputed as the average of observed outcomes of all matches for each unit. With more than two treatment groups, however, estimation using matching requires additional techniques. In this paper, we propose a nearest-neighbors matching estimator for use with multiple, nominal treatments, and use simulations to show that this method is precise and has coverage levels that are close to nominal. In addition, we implement the proposed inference methods to examine the effects of different medication regimens on long-term pain for patients experiencing motor vehicle collision.
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Affiliation(s)
- Anthony D Scotina
- Department of Mathematics and Statistics, Simmons University, Boston, MA, USA
| | - Francesca L Beaudoin
- Department of Health Services, Policy, and Practice, Brown University, Providence, RI, USA.,Department of Emergency Medicine, Brown University, Providence, RI, USA
| | - Roee Gutman
- Department of Biostatistics, Brown University, Providence, RI, USA
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16
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Scotina AD, Gutman R. Matching algorithms for causal inference with multiple treatments. Stat Med 2019; 38:3139-3167. [DOI: 10.1002/sim.8147] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 02/11/2019] [Accepted: 02/25/2019] [Indexed: 01/02/2023]
Affiliation(s)
- Anthony D. Scotina
- Department of Mathematics and StatisticsSimmons University Boston Massachusetts
| | - Roee Gutman
- Department of BiostatisticsBrown University Providence Rhode Island
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17
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Alrubaiee G, Baharom A, Faisal I, Hayati KS, Mohd Daud S, Basaleem HO. Randomized community trial on nosocomial infection control educational module for nurses in public hospitals in Yemen: a study protocol. BMC Nurs 2019; 18:10. [PMID: 30936778 PMCID: PMC6425650 DOI: 10.1186/s12912-019-0333-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 02/26/2019] [Indexed: 11/05/2022] Open
Abstract
Background Nosocomial infections remain a global health problem and they are considered as one of the leading causes of increased morbidity and mortality. In-service training courses related to infection control measures can help nurses to make informed and therapeutic decisions which could prevent or reduce the incidence of nosocomial infections. This study protocol is of a hospital-based trial to develop, implement and evaluate an educational module on nosocomial infection control among nurses in public hospitals in Yemen. This study is currently ongoing and at the analysis stage. Methods A three-arm single-blinded randomized community hospital-based trial was conducted to evaluate the effectiveness of a newly developed nosocomial infection control educational module among nurses in public hospitals in Yemen. To ensure effective delivery and acquisition of knowledge, the Situated Learning Theory was applied during the course of the intervention. A total of 540 Yemeni in-ward nurses, who had three years nursing diploma and at least a year of working experience in the selected public hospitals were recruited in this study. The hospitals were the unit of randomization whereby eight hospitals were assigned randomly to intervention and waitlist groups. Intervention group-1 (n = 180) received an educational module supported by audio-video CD and a training course for eight weeks. Intervention group-2 (n = 180) was given only the educational module with audio-video CD (without the training course). The waitlist group received no intervention during the period of data collection but they will be given the same training and learning materials after the completion of the study. Discussion This study contributes to the lack of a nosocomial infection control educational module for nurses in Yemen. It is hoped that the educational module will serve as an effective approach to increase the nurses’ knowledge and improve their practices regarding nosocomial infection control measures and hence decrease the prevalence of nosocomial infections in the future. Trial registration ID: ISRCTN19992640, Date of registration: 20/6/2017. This study protocol was retrospectively registered. Electronic supplementary material The online version of this article (10.1186/s12912-019-0333-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gamil Alrubaiee
- Department of Applied Medical Sciences, Faculty of Medical Sciences, Al-Razi University, Sana'a, Yemen
| | - Anisah Baharom
- 2Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - Ibrahim Faisal
- 2Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - Kadir Shahar Hayati
- 2Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - Shaffe Mohd Daud
- 3Department of Foundations of Education, Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan, Malaysia
| | - Huda Omer Basaleem
- 4Department of Community Medicine and Public Health, Faculty of Medicine and Health Sciences, University of Aden, Aden, Yemen
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18
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Yu Z, Ramakrishnan V, Meinzer C. Simulation optimization for Bayesian multi-arm multi-stage clinical trial with binary endpoints. J Biopharm Stat 2019; 29:306-317. [PMID: 30763151 DOI: 10.1080/10543406.2019.1577682] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Multi-arm multi-stage designs, in which multiple active treatments are compared to a control and accumulated information from interim data are used to add or remove arms from the trial, may reduce development costs and shorten the drug development timeline. As such, this adaptive update is a natural complement to Bayesian methodology in which the prior clinical belief is sequentially updated using the observed probability of success. Simulation is often required for planning clinical trials to accommodate the complexity of the design and to optimize key design characteristics. This paper addresses two key limiting factors in simulations, namely the computational burden and the time needed to obtain results. We first introduce a generic process for simulating Bayesian multi-arm multi-stage designs with binary endpoints. Then, to address the computational burden and time, we optimize the method for calculating the posterior probability and posterior predictive probability of success.
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Affiliation(s)
- Zhenning Yu
- a Graduate Research Assistant, Data Coordination Unit, Department of Public Health Sciences , Medical University of South Carolina , Charleston , SC , USA
| | - Viswanathan Ramakrishnan
- b Department of Public Health Sciences , Medical University of South Carolina , Charleston , SC , USA
| | - Caitlyn Meinzer
- c Data Coordination Unit, Department of Public Health Sciences , Medical University of South Carolina , Charleston , SC , USA
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19
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Ryeznik Y, Sverdlov O, Hooker AC. Implementing Optimal Designs for Dose-Response Studies Through Adaptive Randomization for a Small Population Group. AAPS JOURNAL 2018; 20:85. [PMID: 30027336 DOI: 10.1208/s12248-018-0242-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 06/18/2018] [Indexed: 11/30/2022]
Abstract
In dose-response studies with censored time-to-event outcomes, D-optimal designs depend on the true model and the amount of censored data. In practice, such designs can be implemented adaptively, by performing dose assignments according to updated knowledge of the dose-response curve at interim analysis. It is also essential that treatment allocation involves randomization-to mitigate various experimental biases and enable valid statistical inference at the end of the trial. In this work, we perform a comparison of several adaptive randomization procedures that can be used for implementing D-optimal designs for dose-response studies with time-to-event outcomes with small to moderate sample sizes. We consider single-stage, two-stage, and multi-stage adaptive designs. We also explore robustness of the designs to experimental (chronological and selection) biases. Simulation studies provide evidence that both the choice of an allocation design and a randomization procedure to implement the target allocation impact the quality of dose-response estimation, especially for small samples. For best performance, a multi-stage adaptive design with small cohort sizes should be implemented using a randomization procedure that closely attains the targeted D-optimal design at each stage. The results of the current work should help clinical investigators select an appropriate randomization procedure for their dose-response study.
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Affiliation(s)
- Yevgen Ryeznik
- Department of Mathematics, Uppsala University, Room Å14133 Lägerhyddsvägen 1, Hus 1, 6 och 7, 751 06, Uppsala, Sweden. .,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
| | - Oleksandr Sverdlov
- Early Development Biostatistics, Novartis Institutes for Biomedical Research, East Hannover, New Jersey, USA
| | - Andrew C Hooker
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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20
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Pallmann P, Bedding AW, Choodari-Oskooei B, Dimairo M, Flight L, Hampson LV, Holmes J, Mander AP, Odondi L, Sydes MR, Villar SS, Wason JMS, Weir CJ, Wheeler GM, Yap C, Jaki T. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med 2018; 16:29. [PMID: 29490655 PMCID: PMC5830330 DOI: 10.1186/s12916-018-1017-7] [Citation(s) in RCA: 349] [Impact Index Per Article: 58.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 01/30/2018] [Indexed: 12/11/2022] Open
Abstract
Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.
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Affiliation(s)
- Philip Pallmann
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
| | | | - Babak Choodari-Oskooei
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | | | - Laura Flight
- Medical Statistics Group, University of Sheffield, Sheffield, UK
| | - Lisa V. Hampson
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
- Statistical Innovation Group, Advanced Analytics Centre, AstraZeneca, Cambridge, UK
| | - Jane Holmes
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | | | - Lang’o Odondi
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Matthew R. Sydes
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Sofía S. Villar
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - James M. S. Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Institute of Health and Society, Newcastle University, Newcastle, UK
| | - Christopher J. Weir
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Graham M. Wheeler
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cancer Research UK & UCL Cancer Trials Centre, University College London, London, UK
| | - Christina Yap
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Thomas Jaki
- Department of Mathematics & Statistics, Lancaster University, Lancaster, LA1 4YF UK
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21
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Brückner M, Titman A, Jaki T. Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint. Stat Med 2017; 36:3137-3153. [PMID: 28612371 PMCID: PMC5575545 DOI: 10.1002/sim.7367] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 05/08/2017] [Accepted: 05/12/2017] [Indexed: 12/29/2022]
Abstract
We consider estimation of treatment effects in two‐stage adaptive multi‐arm trials with a common control. The best treatment is selected at interim, and the primary endpoint is modeled via a Cox proportional hazards model. The maximum partial‐likelihood estimator of the log hazard ratio of the selected treatment will overestimate the true treatment effect in this case. Several methods for reducing the selection bias have been proposed for normal endpoints, including an iterative method based on the estimated conditional selection biases and a shrinkage approach based on empirical Bayes theory. We adapt these methods to time‐to‐event data and compare the bias and mean squared error of all methods in an extensive simulation study and apply the proposed methods to reconstructed data from the FOCUS trial. We find that all methods tend to overcorrect the bias, and only the shrinkage methods can reduce the mean squared error. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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Affiliation(s)
- Matthias Brückner
- Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, U.K
| | - Andrew Titman
- Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, U.K
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YF, U.K
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22
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Ghosh P, Liu L, Senchaudhuri P, Gao P, Mehta C. Design and monitoring of multi-arm multi-stage clinical trials. Biometrics 2017; 73:1289-1299. [PMID: 28346823 DOI: 10.1111/biom.12687] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 01/01/2017] [Accepted: 02/01/2017] [Indexed: 11/28/2022]
Abstract
Two-arm group sequential designs have been widely used for over 40 years, especially for studies with mortality endpoints. The natural generalization of such designs to trials with multiple treatment arms and a common control (MAMS designs) has, however, been implemented rarely. While the statistical methodology for this extension is clear, the main limitation has been an efficient way to perform the computations. Past efforts were hampered by algorithms that were computationally explosive. With the increasing interest in adaptive designs, platform designs, and other innovative designs that involve multiple comparisons over multiple stages, the importance of MAMS designs is growing rapidly. This article provides break-through algorithms that can compute MAMS boundaries rapidly thereby making such designs practical. For designs with efficacy-only boundaries the computational effort increases linearly with number of arms and number of stages. For designs with both efficacy and futility boundaries the computational effort doubles with successive increases in number of stages.
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Affiliation(s)
- Pranab Ghosh
- Cytel Inc., Cambridge, Massachusetts, U.S.A.,Boston University, Boston, Massachusetts, U.S.A
| | | | | | - Ping Gao
- The Medicines Company, Parsippany, New Jersey, U.S.A
| | - Cyrus Mehta
- Cytel Inc., Cambridge, Massachusetts, U.S.A.,Harvard T.H. Chan School of Public Health, Boston, Massachusetts, U.S.A
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23
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Comparison of multi-arm multi-stage design and adaptive randomization in platform clinical trials. Contemp Clin Trials 2017; 54:48-59. [DOI: 10.1016/j.cct.2017.01.003] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Revised: 12/22/2016] [Accepted: 01/11/2017] [Indexed: 11/19/2022]
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24
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Urach S, Posch M. Multi-arm group sequential designs with a simultaneous stopping rule. Stat Med 2016; 35:5536-5550. [PMID: 27550822 PMCID: PMC5157767 DOI: 10.1002/sim.7077] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 07/01/2016] [Accepted: 07/28/2016] [Indexed: 11/08/2022]
Abstract
Multi‐arm group sequential clinical trials are efficient designs to compare multiple treatments to a control. They allow one to test for treatment effects already in interim analyses and can have a lower average sample number than fixed sample designs. Their operating characteristics depend on the stopping rule: We consider simultaneous stopping, where the whole trial is stopped as soon as for any of the arms the null hypothesis of no treatment effect can be rejected, and separate stopping, where only recruitment to arms for which a significant treatment effect could be demonstrated is stopped, but the other arms are continued. For both stopping rules, the family‐wise error rate can be controlled by the closed testing procedure applied to group sequential tests of intersection and elementary hypotheses. The group sequential boundaries for the separate stopping rule also control the family‐wise error rate if the simultaneous stopping rule is applied. However, we show that for the simultaneous stopping rule, one can apply improved, less conservative stopping boundaries for local tests of elementary hypotheses. We derive corresponding improved Pocock and O'Brien type boundaries as well as optimized boundaries to maximize the power or average sample number and investigate the operating characteristics and small sample properties of the resulting designs. To control the power to reject at least one null hypothesis, the simultaneous stopping rule requires a lower average sample number than the separate stopping rule. This comes at the cost of a lower power to reject all null hypotheses. Some of this loss in power can be regained by applying the improved stopping boundaries for the simultaneous stopping rule. The procedures are illustrated with clinical trials in systemic sclerosis and narcolepsy. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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Affiliation(s)
- S Urach
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems (CEMSIIS), Medical University of Vienna, Spitalgasse 23, A-1090, Wien, Austria
| | - M Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems (CEMSIIS), Medical University of Vienna, Spitalgasse 23, A-1090, Wien, Austria
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25
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Lu X, He Y, Wu SS. Interval estimation in multi-stage drop-the-losers designs. Stat Methods Med Res 2016; 27:221-233. [PMID: 26980742 DOI: 10.1177/0962280215626748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Drop-the-losers designs have been discussed extensively in the past decades, mostly focusing on two-stage models. The designs with more than two stages have recently received increasing attention due to their improved efficiency over the corresponding two-stage designs. In this paper, we consider the problem of estimating and testing the effect of selected treatment under the setting of three-stage drop-the-losers designs. A conservative interval estimator is proposed, which is proved to have at least the specified coverage probability using a stochastic ordering approach. The proposed interval estimator is also demonstrated numerically to have narrower interval width but higher coverage rate than the bootstrap method proposed by Bowden and Glimm (Biometrical Journal, vol. 56, pp. 332-349) in most cases. It is also a straightforward derivation from the stochastic ordering result that the family-wise error rate is strongly controlled with the maximum achieved at the global null hypothesis.
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Affiliation(s)
- Xiaomin Lu
- 1 Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Ying He
- 2 Department of Mathematics, Clarkson University, Potsdam, NY, USA
| | - Samuel S Wu
- 1 Department of Biostatistics, University of Florida, Gainesville, FL, USA
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