1
|
Marschner IC, Schou IM. Analysis of Nonconcurrent Controls in Adaptive Platform Trials: Separating Randomized and Nonrandomized Information. Biom J 2024; 66:e202300334. [PMID: 39104093 DOI: 10.1002/bimj.202300334] [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: 11/28/2023] [Revised: 06/09/2024] [Accepted: 07/01/2024] [Indexed: 08/07/2024]
Abstract
Adaptive platform trials allow treatments to be added or dropped during the study, meaning that the control arm may be active for longer than the experimental arms. This leads to nonconcurrent controls, which provide nonrandomized information that may increase efficiency but may introduce bias from temporal confounding and other factors. Various methods have been proposed to control confounding from nonconcurrent controls, based on adjusting for time period. We demonstrate that time adjustment is insufficient to prevent bias in some circumstances where nonconcurrent controls are present in adaptive platform trials, and we propose a more general analytical framework that accounts for nonconcurrent controls in such circumstances. We begin by defining nonconcurrent controls using the concept of a concurrently randomized cohort, which is a subgroup of participants all subject to the same randomized design. We then use cohort adjustment rather than time adjustment. Due to flexibilities in platform trials, more than one randomized design may be in force at any time, meaning that cohort-adjusted and time-adjusted analyses may be quite different. Using simulation studies, we demonstrate that time-adjusted analyses may be biased while cohort-adjusted analyses remove this bias. We also demonstrate that the cohort-adjusted analysis may be interpreted as a synthesis of randomized and indirect comparisons analogous to mixed treatment comparisons in network meta-analysis. This allows the use of network meta-analysis methodology to separate the randomized and nonrandomized components and to assess their consistency. Whenever nonconcurrent controls are used in platform trials, the separate randomized and indirect contributions to the treatment effect should be presented.
Collapse
Affiliation(s)
- Ian C Marschner
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia
| | - I Manjula Schou
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia
| |
Collapse
|
2
|
Greenstreet P, Jaki T, Bedding A, Harbron C, Mozgunov P. A multi-arm multi-stage platform design that allows preplanned addition of arms while still controlling the family-wise error. Stat Med 2024; 43:3613-3632. [PMID: 38880949 DOI: 10.1002/sim.10135] [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: 05/27/2022] [Revised: 05/26/2024] [Accepted: 05/29/2024] [Indexed: 06/18/2024]
Abstract
There is growing interest in platform trials that allow for adding of new treatment arms as the trial progresses as well as being able to stop treatments part way through the trial for either lack of benefit/futility or for superiority. In some situations, platform trials need to guarantee that error rates are controlled. This paper presents a multi-stage design, that allows additional arms to be added in a platform trial in a preplanned fashion, while still controlling the family-wise error rate, under the assumption of known number and timing of treatments to be added, and no time trends. A method is given to compute the sample size required to achieve a desired level of power and we show how the distribution of the sample size and the expected sample size can be found. We focus on power under the least favorable configuration which is the power of finding the treatment with a clinically relevant effect out of a set of treatments while the rest have an uninteresting treatment effect. A motivating trial is presented which focuses on two settings, with the first being a set number of stages per active treatment arm and the second being a set total number of stages, with treatments that are added later getting fewer stages. Compared to Bonferroni, the savings in the total maximum sample size are modest in a trial with three arms, <1% of the total sample size. However, the savings are more substantial in trials with more arms.
Collapse
Affiliation(s)
- Peter Greenstreet
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
- Exeter Clinical Trials Unit, University of Exeter, Exeter, UK
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- University of Regensburg, Regensburg, Germany
| | | | | | - Pavel Mozgunov
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| |
Collapse
|
3
|
Lee KM, Emsley R. The impact of heterogeneity on the analysis of platform trials with normally distributed outcomes. BMC Med Res Methodol 2024; 24:163. [PMID: 39080538 PMCID: PMC11290279 DOI: 10.1186/s12874-024-02293-4] [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: 11/15/2023] [Accepted: 07/19/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND A platform trial approach allows adding arms to on-going trials to speed up intervention discovery programs. A control arm remains open for recruitment in a platform trial while intervention arms may be added after the onset of the study and could be terminated early for efficacy and/or futility when early stopping is allowed. The topic of utilising non-concurrent control data in the analysis of platform trials has been explored and discussed extensively. A less familiar issue is the presence of heterogeneity, which may exist for example due to modification of enrolment criteria and recruitment strategy. METHOD We conduct a simulation study to explore the impact of heterogeneity on the analysis of a two-stage platform trial design. We consider heterogeneity in treatment effects and heteroscedasticity in outcome data across stages for a normally distributed endpoint. We examine the performance of some hypothesis testing procedures and modelling strategies. The use of non-concurrent control data is also considered accordingly. Alongside standard regression analysis, we examine the performance of a novel method that was known as the pairwise trials analysis. It is similar to a network meta-analysis approach but adjusts for treatment comparisons instead of individual studies using fixed effects. RESULTS Several testing strategies with concurrent control data seem to control the type I error rate at the required level when there is heteroscedasticity in outcome data across stages and/or a random cohort effect. The main parameter of treatment effects in some analysis models correspond to overall treatment effects weighted by stage wise sample sizes; while others correspond to the effect observed within a single stage. The characteristics of the estimates are not affected significantly by the presence of a random cohort effect and/ or heteroscedasticity. CONCLUSION In view of heterogeneity in treatment effect across stages, the specification of null hypotheses in platform trials may need to be more subtle. We suggest employing testing procedure of adaptive design as opposed to testing the statistics from regression models; comparing the estimates from the pairwise trials analysis method and the regression model with interaction terms may indicate if heterogeneity is negligible.
Collapse
Affiliation(s)
- Kim May Lee
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK.
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Richard Emsley
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| |
Collapse
|
4
|
Sasaki M, Sato H, Uemura Y, Mikami A, Ichihara N, Fujitani S, Kondo M, Doi Y, Morino E, Tokita D, Ohmagari N, Sugiura W, Hirakawa A. How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand-Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic. Clin Pharmacol Ther 2024; 115:1372-1382. [PMID: 38441177 DOI: 10.1002/cpt.3224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/12/2024] [Indexed: 05/14/2024]
Abstract
With the coronavirus disease 2019 (COVID-19) pandemic, there is growing interest in utilizing adaptive platform clinical trials (APTs), in which multiple drugs are compared with a single common control group, such as a placebo or standard-of-care group. APTs evaluate several drugs for one disease and accept additions or exclusions of drugs as the trials progress; however, little is known about the efficiency of APTs over multiple stand-alone trials. In this study, we simulated the total development period, total sample size, and statistical operating characteristics of APTs and multiple stand-alone trials in drug development settings for hospitalized patients with COVID-19. Simulation studies using selected scenarios reconfirmed several findings regarding the efficiency of APTs. The APTs without staggered addition of drugs showed a shorter total development period than stand-alone trials, but the difference rapidly diminished if patient's enrollment was accelerated during the trials owing to the spread of infection. APTs with staggered addition of drugs still have the possibility of reducing the total development period compared with multiple stand-alone trials in some cases. Our study demonstrated that APTs could improve efficiency relative to multiple stand-alone trials regarding the total development period and total sample size without undermining statistical validity; however, this improvement varies depending on the speed of patient enrollment, sample size, presence/absence of family-wise error rate adjustment, allocation ratio between drug and placebo groups, and interval of staggered addition of drugs. Given the complexity of planning and implementing APT, the decision to implement APT during a pandemic must be made carefully.
Collapse
Affiliation(s)
- Masanao Sasaki
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Hiroyuki Sato
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| | - Yukari Uemura
- Biostatistics Section, Department of Data Science, Center of Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Ayako Mikami
- Center for Clinical Research, National Center for Child Health and Development, Tokyo, Japan
| | - Nao Ichihara
- Department of Healthcare Quality Assessment, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shigeki Fujitani
- Department of Emergency and Critical Care Medicine, St. Marianna University School of Medicine, Kanagawa, Japan
| | - Masashi Kondo
- Center for Clinical Trial and Research Support, Fujita Health University School of Medicine, Aichi, Japan
- Department of Respiratory Medicine, Fujita Health University School of Medicine, Aichi, Japan
| | - Yohei Doi
- Departments of Microbiology and Infectious Diseases, Fujita Health University School of Medicine, Aichi, Japan
| | - Eriko Morino
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Daisuke Tokita
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Norio Ohmagari
- Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo, Japan
| | - Wataru Sugiura
- Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
| | - Akihiro Hirakawa
- Department of Clinical Biostatistics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University (TMDU), Tokyo, Japan
| |
Collapse
|
5
|
Blackwell SE, Schönbrodt FD, Woud ML, Wannemüller A, Bektas B, Braun Rodrigues M, Hirdes J, Stumpp M, Margraf J. Demonstration of a 'leapfrog' randomized controlled trial as a method to accelerate the development and optimization of psychological interventions. Psychol Med 2023; 53:6113-6123. [PMID: 36330836 PMCID: PMC10520605 DOI: 10.1017/s0033291722003294] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/14/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND The scale of the global mental health burden indicates the inadequacy not only of current treatment options, but also the pace of the standard treatment development process. The 'leapfrog' trial design is a newly-developed simple Bayesian adaptive trial design with potential to accelerate treatment development. A first leapfrog trial was conducted to provide a demonstration and test feasibility, applying the method to a low-intensity internet-delivered intervention targeting anhedonia. METHODS At the start of this online, single-blind leapfrog trial, participants self-reporting depression were randomized to an initial control arm comprising four weeks of weekly questionnaires, or one of two versions of a four-week cognitive training intervention, imagery cognitive bias modification (imagery CBM). Intervention arms were compared to control on an ongoing basis via sequential Bayesian analyses, based on a primary outcome of anhedonia at post-intervention. Results were used to eliminate and replace arms, or to promote them to become the control condition based on pre-specified Bayes factor and sample size thresholds. Two further intervention arms (variants of imagery CBM) were added into the trial as it progressed. RESULTS N = 188 participants were randomized across the five trial arms. The leapfrog methodology was successfully implemented to identify a 'winning' version of the imagery CBM, i.e. the version most successful in reducing anhedonia, following sequential elimination of the other arms. CONCLUSIONS The study demonstrates feasibility of the leapfrog design and provides a foundation for its adoption as a method to accelerate treatment development in mental health. Registration: clinicaltrials.gov, NCT04791137.
Collapse
Affiliation(s)
- Simon E. Blackwell
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
| | - Felix D. Schönbrodt
- Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Marcella L. Woud
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
| | - Andre Wannemüller
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
| | - Büsra Bektas
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
| | - Max Braun Rodrigues
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
| | - Josefine Hirdes
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
| | - Michael Stumpp
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
| | - Jürgen Margraf
- Mental Health Research and Treatment Center, Faculty of Psychology, Ruhr-Universität Bochum, Bochum, Germany
| |
Collapse
|
6
|
Marschner IC, Jones M, Totterdell JA, Mahar RK, Snelling TL, Tong SYC. Transparent reporting of adaptive clinical trials using concurrently randomised cohorts. BMJ MEDICINE 2023; 2:e000497. [PMID: 37736079 PMCID: PMC10510920 DOI: 10.1136/bmjmed-2023-000497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 09/01/2023] [Indexed: 09/23/2023]
Abstract
Adaptive clinical trials have designs that evolve over time because of changes to treatments or changes to the chance that participants will receive these treatments. These changes might introduce confounding that biases crude comparisons of the treatment arms and makes the results from standard reporting methods difficult to interpret for adaptive trials. To deal with this shortcoming, a reporting framework for adaptive trials was developed based on concurrently randomised cohort reporting. A concurrently randomised cohort is a subgroup of participants who all had the same treatments available and the same chance of receiving these treatments. The reporting of pre-randomisation characteristics and post-randomisation outcomes for each concurrently randomised cohort in the study is recommended. This approach provides a transparent and unbiased display of the degree of baseline balance and the randomised treatment comparisons for adaptive trials. The key concepts, terminology, and recommendations underlying concurrently randomised cohort reporting are presented, and its routine use in adaptive trial reporting is advocated.
Collapse
Affiliation(s)
- Ian C Marschner
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia
| | - Mark Jones
- School of Public Health, University of Sydney, Sydney, NSW, Australia
| | | | - Robert K Mahar
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Murdoch Children's Research Institute, Parkville, VIC, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, QLD, Australia
| | - Thomas L Snelling
- School of Public Health, University of Sydney, Sydney, NSW, Australia
- Telethon Kids Institute, University of Western Australia, Perth, WA, Australia
| | - Steven Y C Tong
- Department of Infectious Diseases, University of Melbourne, Melbourne, VIC, Australia
- Victorian Infectious Diseases Service, University of Melbourne, Melbourne, VIC, Australia
- Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | | |
Collapse
|