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Cellamare, Matteo, Steffen Ventz, Elisabeth Baudin, Carole D. Mitnick, and Lorenzo Trippa. "A Bayesian response-adaptive trial in tuberculosis: The endTB trial." Clinical Trials 14, no. 1 (September 23, 2016): 17–28. http://dx.doi.org/10.1177/1740774516665090.
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Purpose: To evaluate the use of Bayesian adaptive randomization for clinical trials of new treatments for multidrug-resistant tuberculosis. Methods: We built a response-adaptive randomization procedure, adapting on two preliminary outcomes for tuberculosis patients in a trial with five experimental regimens and a control arm. The primary study outcome is treatment success after 73 weeks from randomization; preliminary responses are culture conversion at 8 weeks and treatment success at 39 weeks. We compared the adaptive randomization design with balanced randomization using hypothetical scenarios. Results: When we compare the statistical power under adaptive randomization and non-adaptive designs, under several hypothetical scenarios we observe that adaptive randomization requires fewer patients than non-adaptive designs. Moreover, adaptive randomization consistently allocates more participants to effective arm(s). We also show that these advantages are limited to scenarios consistent with the assumptions used to develop the adaptive randomization algorithm. Conclusion: Given the objective of evaluating several new therapeutic regimens in a timely fashion, Bayesian response-adaptive designs are attractive for tuberculosis trials. This approach tends to increase allocation to the effective regimens.
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Lai, Tze Leung, Philip William Lavori, and Mei-Chiung Shih. "Adaptive Trial Designs." Annual Review of Pharmacology and Toxicology 52, no. 1 (February 10, 2012): 101–10. http://dx.doi.org/10.1146/annurev-pharmtox-010611-134504.
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Chen, Zhengjia, Yichuan Zhao, Ye Cui, and Jeanne Kowalski. "Methodology and Application of Adaptive and Sequential Approaches in Contemporary Clinical Trials." Journal of Probability and Statistics 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/527351.
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The clinical trial, a prospective study to evaluate the effect of interventions in humans under prespecified conditions, is a standard and integral part of modern medicine. Many adaptive and sequential approaches have been proposed for use in clinical trials to allow adaptations or modifications to aspects of a trial after its initiation without undermining the validity and integrity of the trial. The application of adaptive and sequential methods in clinical trials has significantly improved the flexibility, efficiency, therapeutic effect, and validity of trials. To further advance the performance of clinical trials and convey the progress of research on adaptive and sequential methods in clinical trial design, we review significant research that has explored novel adaptive and sequential approaches and their applications in Phase I, II, and III clinical trials and discuss future directions in this field of research.
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Chow, Shein-Chung. "Adaptive Clinical Trial Design." Annual Review of Medicine 65, no. 1 (January 14, 2014): 405–15. http://dx.doi.org/10.1146/annurev-med-092012-112310.
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May Lee, Kim, and J. Jack Lee. "Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials." Statistical Methods in Medical Research 30, no. 5 (March 10, 2021): 1273–87. http://dx.doi.org/10.1177/0962280221995961.
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Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.
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Mawocha, Samkeliso C., Michael D. Fetters, Laurie J. Legocki, Timothy C. Guetterman, Shirley Frederiksen, William G. Barsan, Roger J. Lewis, Donald A. Berry, and William J. Meurer. "A conceptual model for the development process of confirmatory adaptive clinical trials within an emergency research network." Clinical Trials 14, no. 3 (January 31, 2017): 246–54. http://dx.doi.org/10.1177/1740774516688900.
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Background: Adaptive clinical trials use accumulating data from enrolled subjects to alter trial conduct in pre-specified ways based on quantitative decision rules. In this research, we sought to characterize the perspectives of key stakeholders during the development process of confirmatory-phase adaptive clinical trials within an emergency clinical trials network and to build a model to guide future development of adaptive clinical trials. Methods: We used an ethnographic, qualitative approach to evaluate key stakeholders’ views about the adaptive clinical trial development process. Stakeholders participated in a series of multidisciplinary meetings during the development of five adaptive clinical trials and completed a Strengths–Weaknesses–Opportunities–Threats questionnaire. In the analysis, we elucidated overarching themes across the stakeholders’ responses to develop a conceptual model. Results: Four major overarching themes emerged during the analysis of stakeholders’ responses to questioning: the perceived statistical complexity of adaptive clinical trials and the roles of collaboration, communication, and time during the development process. Frequent and open communication and collaboration were viewed by stakeholders as critical during the development process, as were the careful management of time and logistical issues related to the complexity of planning adaptive clinical trials. Conclusion: The Adaptive Design Development Model illustrates how statistical complexity, time, communication, and collaboration are moderating factors in the adaptive design development process. The intensity and iterative nature of this process underscores the need for funding mechanisms for the development of novel trial proposals in academic settings.
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Trippa, Lorenzo, Eudocia Q. Lee, Patrick Y. Wen, Tracy T. Batchelor, Timothy Cloughesy, Giovanni Parmigiani, and Brian M. Alexander. "Bayesian Adaptive Randomized Trial Design for Patients With Recurrent Glioblastoma." Journal of Clinical Oncology 30, no. 26 (September 10, 2012): 3258–63. http://dx.doi.org/10.1200/jco.2011.39.8420.
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Purpose To evaluate whether the use of Bayesian adaptive randomized (AR) designs in clinical trials for glioblastoma is feasible and would allow for more efficient trials. Patients and Methods We generated an adaptive randomization procedure that was retrospectively applied to primary patient data from four separate phase II clinical trials in patients with recurrent glioblastoma. We then compared AR designs with more conventional trial designs by using realistic hypothetical scenarios consistent with survival data reported in the literature. Our primary end point was the number of patients needed to achieve a desired statistical power. Results If our phase II trials had been a single, multiarm trial using AR design, 30 fewer patients would have been needed compared with a multiarm balanced randomized (BR) design to attain the same power level. More generally, Bayesian AR trial design for patients with glioblastoma would result in trials with fewer overall patients with no loss in statistical power and in more patients being randomly assigned to effective treatment arms. For a 140-patient trial with a control arm, two ineffective arms, and one effective arm with a hazard ratio of 0.6, a median of 47 patients would be randomly assigned to the effective arm compared with 35 in a BR trial design. Conclusion Given the desire for control arms in phase II trials, an increasing number of experimental therapeutics, and a relatively short time for events, Bayesian AR designs are attractive for clinical trials in glioblastoma.
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Lauffenburger, Julie C., Niteesh K. Choudhry, Massimiliano Russo, Robert J. Glynn, Steffen Ventz, and Lorenzo Trippa. "Designing and conducting adaptive trials to evaluate interventions in health services and implementation research: practical considerations." BMJ Medicine 1, no. 1 (July 2022): e000158. http://dx.doi.org/10.1136/bmjmed-2022-000158.
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Randomised controlled clinical trials are widely considered the preferred method for evaluating the efficacy or effectiveness of interventions in healthcare. Adaptive trials incorporate changes as the study proceeds, such as modifying allocation probabilities or eliminating treatment arms that are likely to be ineffective. These designs have been widely used in drug discovery studies but can also be useful in health services and implementation research and have been minimally used. In this article, we use an ongoing adaptive trial and two completed parallel group studies as motivating examples to highlight the potential advantages, disadvantages, and important considerations when using adaptive trial designs in health services and implementation research. We also investigate the impact on power and the study duration if the two completed parallel group trials had instead been conducted using adaptive principles. Compared with traditional trial designs, adaptive designs can often allow the evaluation of more interventions, adjust participant allocation probabilities (eg, to achieve covariate balance), and identify participants who are likely to agree to enrol. These features could reduce resources needed to conduct a trial. However, adaptive trials have potential disadvantages and practical aspects that need to be considered, most notably: outcomes that can be rapidly measured and extracted (eg, long term outcomes that take considerable time to measure from data sources can be challenging), minimal missing data, and time trends. In conclusion, adaptive designs are a promising approach to help identify how best to implement evidence based interventions into real world practice in health services and implementation research.
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Berry, Donald A., Scott Berry, Peter Hale, Leah Isakov, Andrew W. Lo, Kien Wei Siah, and Chi Heem Wong. "A cost/benefit analysis of clinical trial designs for COVID-19 vaccine candidates." PLOS ONE 15, no. 12 (December 23, 2020): e0244418. http://dx.doi.org/10.1371/journal.pone.0244418.
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We compare and contrast the expected duration and number of infections and deaths averted among several designs for clinical trials of COVID-19 vaccine candidates, including traditional and adaptive randomized clinical trials and human challenge trials. Using epidemiological models calibrated to the current pandemic, we simulate the time course of each clinical trial design for 756 unique combinations of parameters, allowing us to determine which trial design is most effective for a given scenario. A human challenge trial provides maximal net benefits—averting an additional 1.1M infections and 8,000 deaths in the U.S. compared to the next best clinical trial design—if its set-up time is short or the pandemic spreads slowly. In most of the other cases, an adaptive trial provides greater net benefits.
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Freidlin, Boris, and Edward L. Korn. "Biomarker-adaptive clinical trial designs." Pharmacogenomics 11, no. 12 (December 2010): 1679–82. http://dx.doi.org/10.2217/pgs.10.153.
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Papadimitrakopoulou, Vassiliki. "SC28.04 Adaptive Clinical Trial Designs." Journal of Thoracic Oncology 12, no. 1 (January 2017): S140—S142. http://dx.doi.org/10.1016/j.jtho.2016.11.126.
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Houlding, B., and F. P. A. Coolen. "Adaptive utility and trial aversion." Journal of Statistical Planning and Inference 141, no. 2 (February 2011): 734–47. http://dx.doi.org/10.1016/j.jspi.2010.07.023.
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Alexander, Brian Michael, Patrick Y. Wen, Eudocia Quant Lee, Tracy Batchelor, Timothy Francis Cloughesy, Giovanni Parmigiani, and Lorenzo Trippa. "Bayesian adaptive randomized trial design for patients with recurrent glioblastoma." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 2005. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.2005.
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2005 Background: Bayesian-based trial design has the ability to utilize accumulating data in real time to alter the course of the trial, thereby enabling dynamic allocation to experimental arms and earlier dropping of ineffective arms. This flexibility results in a potentially more efficient trial framework by increasing the probability of enrollment to arms that show evidence of efficacy. In this study we considered a hypothetical scenario in which patients who have been previously treated on several separate experimental protocols at the Dana-Farber/Harvard Cancer Center and the University of California, Los Angeles were instead enrolled in a single multi-arm protocol utilizing a Bayesian adaptively randomized trial design. The purpose was to determine whether similar scientific results could have been accomplished more efficiently. Methods: We generated an adaptive randomization procedure that was retrospectively applied to primary patient data from four separate phase II clinical trials in patients with recurrent glioblastoma. We then compared AR to more conventional trial designs using realistic hypothetical scenarios consistent with survival data reported in the literature. Our primary endpoint is the number of patients needed to achieve a desired statistical power. Results: If our phase II trials had been a single multi-arm AR trial, bevacizumab would have been identified as an efficacious therapy, and 30 fewer patients would have been needed compared to a multi-arm balanced randomized (BR) design. More generally, Bayesian AR trial design for patients with glioblastoma would result in trials with fewer overall patients with no loss in statistical power, and in more patients randomized to effective treatment arms. For a trial with a control arm, two ineffective arms and one effective arm with hazard ratio 0.6, a median of 47 patients would be randomized to the effective arm compared with 35 in a BR design. Conclusions: Given the desire for control arms in phase II trials, an increasing number of experimental therapeutics for patients with glioblastoma, and a relatively short time for events, Bayesian adaptive designs are attractive for clinical trials in glioblastoma.
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Flight, Laura, Steven Julious, Alan Brennan, and Susan Todd. "Expected Value of Sample Information to Guide the Design of Group Sequential Clinical Trials." Medical Decision Making 42, no. 4 (December 3, 2021): 461–73. http://dx.doi.org/10.1177/0272989x211045036.
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Introduction Adaptive designs allow changes to an ongoing trial based on prespecified early examinations of accrued data. Opportunities are potentially being missed to incorporate health economic considerations into the design of these studies. Methods We describe how to estimate the expected value of sample information for group sequential design adaptive trials. We operationalize this approach in a hypothetical case study using data from a pilot trial. We report the expected value of sample information and expected net benefit of sampling results for 5 design options for the future full-scale trial including the fixed-sample-size design and the group sequential design using either the Pocock stopping rule or the O’Brien-Fleming stopping rule with 2 or 5 analyses. We considered 2 scenarios relating to 1) using the cost-effectiveness model with a traditional approach to the health economic analysis and 2) adjusting the cost-effectiveness analysis to incorporate the bias-adjusted maximum likelihood estimates of trial outcomes to account for the bias that can be generated in adaptive trials. Results The case study demonstrated that the methods developed could be successfully applied in practice. The results showed that the O’Brien-Fleming stopping rule with 2 analyses was the most efficient design with the highest expected net benefit of sampling in the case study. Conclusions Cost-effectiveness considerations are unavoidable in budget-constrained, publicly funded health care systems, and adaptive designs can provide an alternative to costly fixed-sample-size designs. We recommend that when planning a clinical trial, expected value of sample information methods be used to compare possible adaptive and nonadaptive trial designs, with appropriate adjustment, to help justify the choice of design characteristics and ensure the cost-effective use of research funding. Highlights Opportunities are potentially being missed to incorporate health economic considerations into the design of adaptive clinical trials. Existing expected value of sample information analysis methods can be extended to compare possible group sequential and nonadaptive trial designs when planning a clinical trial. We recommend that adjusted analyses be presented to control for the potential impact of the adaptive designs and to maintain the accuracy of the calculations. This approach can help to justify the choice of design characteristics and ensure the cost-effective use of limited research funding.
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Zhong, Xiaobo, Bin Cheng, Xinru Wang, and Ying Kuen Cheung. "SMARTAR: an R package for designing and analyzing Sequential Multiple Assignment Randomized Trials." PeerJ 9 (January 11, 2021): e10559. http://dx.doi.org/10.7717/peerj.10559.
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This article introduces an R package, SMARTAR (Sequential Multiple Assignment Randomized Trial with Adaptive Randomization), by which clinical investigators can design and analyze a sequential multiple assignment randomized trial (SMART) for comparing adaptive treatment strategies. Adaptive treatment strategies are commonly used in clinical practice to personalize healthcare in chronic disorder management. SMART is an efficient clinical design for selecting the best adaptive treatment strategy from a family of candidates. Although some R packages can help in adaptive treatment strategies research, they mainly focus on secondary data analysis for observational studies, instead of clinical trials. SMARTAR is the first R package provides functions that can support clinical investigators and data analysts at every step of the statistical work pipeline in clinical trial practice. In this article, we demonstrate how to use this package, using a real data example.
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Zhong, Xiaobo, Bin Cheng, Xinru Wang, and Ying Kuen Cheung. "SMARTAR: an R package for designing and analyzing Sequential Multiple Assignment Randomized Trials." PeerJ 9 (January 11, 2021): e10559. http://dx.doi.org/10.7717/peerj.10559.
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This article introduces an R package, SMARTAR (Sequential Multiple Assignment Randomized Trial with Adaptive Randomization), by which clinical investigators can design and analyze a sequential multiple assignment randomized trial (SMART) for comparing adaptive treatment strategies. Adaptive treatment strategies are commonly used in clinical practice to personalize healthcare in chronic disorder management. SMART is an efficient clinical design for selecting the best adaptive treatment strategy from a family of candidates. Although some R packages can help in adaptive treatment strategies research, they mainly focus on secondary data analysis for observational studies, instead of clinical trials. SMARTAR is the first R package provides functions that can support clinical investigators and data analysts at every step of the statistical work pipeline in clinical trial practice. In this article, we demonstrate how to use this package, using a real data example.
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Pickles, Tim, Rieke Alten, Maarten Boers, Vivian Bykerk, Jared Christensen, Robin Christensen, Hubert van Hoogstraten, Lee S. Simon, Lai-Shan Tam, and Ernest H. Choy. "Adaptive Trial Designs in Rheumatology: Report from the OMERACT Special Interest Group." Journal of Rheumatology 46, no. 10 (February 15, 2019): 1406–8. http://dx.doi.org/10.3899/jrheum.181054.
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Objective.Adaptive trial design was developed initially for oncology to improve trial efficiency. If optimized for rheumatology, it may improve trial efficiency by reducing sample size and time.Methods.A systematic review assessed design of phase II clinical trials in rheumatoid arthritis.Results.Fifty-six trials were reviewed. Most trials had 4 groups (1 control and 3 intervention), with an average group size of 34 patients. American College of Rheumatology 20 measured at 16 weeks was the most commonly used primary endpoint.Conclusion.The next step is to undertake a systematic review of adaptive designs used in early-phase trials in nonrheumatic conditions.
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Fang, Fang, Yong Lin, Weichung Joe Shih, Shou-En Lu, and Guangrui Zhu. "Evaluation of Performance of Adaptive Designs Based on Treatment Effect Intervals." International Journal of Statistics and Probability 7, no. 6 (September 17, 2018): 81. http://dx.doi.org/10.5539/ijsp.v7n6p81.
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The accuracy of the treatment effect estimation is crucial to the success of Phase 3 studies. The calculation of sample size relies on the treatment effect estimation and cannot be changed during the trial in a fixed sample size design. Oftentimes, with limited efficacy data available from early phase studies and relevant historical studies, the sample size calculation may not accurately reflect the true treatment effect. Several adaptive designs have been proposed to address this uncertainty in the sample size calculation. These adaptive designs provide flexibility of sample size adjustment during the trial by allowing early trial stopping or sample size adjustment at interim look(s). The use of adaptive designs can optimize the trial performance when the treatment effect is an assumed constant value. However in practice, it may be more reasonable to consider the treatment effect within an interval rather than as a point estimate. Because proper selection of adaptive designs may decrease the failure rate of Phase 3 clinical trials and increase the chance for new drug approval, this paper proposes measures and evaluates the performance of different adaptive designs based on treatment effect intervals, and identifies factors that may affect the performance of adaptive designs.
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Thorlund, Kristian, Shirin Golchi, Jonas Haggstrom, and Edward Mills. "Highly Efficient Clinical Trials Simulator (HECT): Software application for planning and simulating platform adaptive trials." Gates Open Research 3 (March 8, 2019): 780. http://dx.doi.org/10.12688/gatesopenres.12912.1.
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Background: Adaptive designs and platform designs are among two common clinical trial innovations that are increasingly being used to manage medical intervention portfolios and attain faster regulatory approvals. Planning of adaptive and platform trials necessitate simulations to understand how a set of adaptation rules will likely affect the properties of the trial. Clinical trial simulations, however, remain a black box to many clinical trials researchers who are not statisticians. Results: In this article we introduce a simple intuitive open-source browser-based clinical trial simulator for planning adaptive and platform trials. The software application is implemented in RShiny and features a graphical user interface that allows the user to set key clinical trial parameters and explore multiple scenarios such as varying treatment effects, control response and adherence, as well as number of interim looks and adaptation rules. The software provides simulation options for a number of designs such as dropping treatment arms for futility, adding a new treatment arm (i.e., platform design), and stopping a trial early based on superiority. All available adaptations are based on underlying Bayesian probabilities. The software comes with a number of graphical outputs to examine properties of individual simulated trials. The main output is a comparison of trial design performance across several simulations, graphically summarizing type I error (false positive risk), power, and expected cost/time to completion of the considered designs. Conclusion: We have developed and validated an intuitive highly efficient clinical trial simulator for planning of clinical trials. The software is open-source and caters to clinical trial investigators who do not have the statistical capacity for trial simulations available in their team. The software can be accessed via any web browser via the following link: https://mtek.shinyapps.io/hect/
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Thorlund, Kristian, Shirin Golchi, Jonas Haggstrom, and Edward Mills. "Highly Efficient Clinical Trials Simulator (HECT): Software application for planning and simulating platform adaptive trials." Gates Open Research 3 (March 18, 2019): 780. http://dx.doi.org/10.12688/gatesopenres.12912.2.
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Background: Adaptive designs and platform designs are among two common clinical trial innovations that are increasingly being used to manage medical intervention portfolios and attain faster regulatory approvals. Planning of adaptive and platform trials necessitate simulations to understand how a set of adaptation rules will likely affect the properties of the trial. Clinical trial simulations, however, remain a black box to many clinical trials researchers who are not statisticians. Results: In this article we introduce a simple intuitive open-source browser-based clinical trial simulator for planning adaptive and platform trials. The software application is implemented in RShiny and features a graphical user interface that allows the user to set key clinical trial parameters and explore multiple scenarios such as varying treatment effects, control response and adherence, as well as number of interim looks and adaptation rules. The software provides simulation options for a number of designs such as dropping treatment arms for futility, adding a new treatment arm (i.e., platform design), and stopping a trial early based on superiority. All available adaptations are based on underlying Bayesian probabilities. The software comes with a number of graphical outputs to examine properties of individual simulated trials. The main output is a comparison of trial design performance across several simulations, graphically summarizing type I error (false positive risk), power, and expected cost/time to completion of the considered designs. Conclusion: We have developed and validated an intuitive highly efficient clinical trial simulator for planning of clinical trials. The software is open-source and caters to clinical trial investigators who do not have the statistical capacity for trial simulations available in their team. The software can be accessed via any web browser via the following link: https://mtek.shinyapps.io/hect/
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Anderer, Arielle, Hamsa Bastani, and John Silberholz. "Adaptive Clinical Trial Designs with Surrogates: When Should We Bother?" Management Science 68, no. 3 (March 2022): 1982–2002. http://dx.doi.org/10.1287/mnsc.2021.4096.
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The success of a new drug is assessed within a clinical trial using a primary endpoint, which is typically the true outcome of interest—for example, overall survival. However, regulators sometimes approve drugs using a surrogate outcome—an intermediate indicator that is faster or easier to measure than the true outcome of interest—for example, progression-free survival—as the primary endpoint when there is demonstrable medical need. Although using a surrogate outcome (instead of the true outcome) as the primary endpoint can substantially speed up clinical trials and lower costs, it can also result in poor drug-approval decisions because the surrogate is not a perfect predictor of the true outcome. In this paper, we propose combining data from both surrogate and true outcomes to improve decision making within a late-phase clinical trial. In contrast to broadly used clinical trial designs that rely on a single primary endpoint, we propose a Bayesian adaptive clinical trial design that simultaneously leverages both observed outcomes to inform trial decisions. We perform comparative statics on the relative benefit of our approach, illustrating the types of diseases and surrogates for which our proposed design is particularly advantageous. Finally, we illustrate our proposed design on metastatic breast cancer. We use a large-scale clinical trial database to construct a Bayesian prior and simulate our design on a subset of clinical trials. We estimate that our design would yield a 16% decrease in trial costs relative to existing clinical trial designs, while maintaining the same Type I/II error rates. This paper was accepted by J. George Shanthikumar, Management Science Special Section on Data-Driven Prescriptive Analytics.
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Rojas-Cordova, Alba C., and Niyousha Hosseinichimeh. "Trial Termination and Drug Misclassification in Sequential Adaptive Clinical Trials." Service Science 10, no. 3 (September 2018): 354–77. http://dx.doi.org/10.1287/serv.2018.0217.
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Van Norman, Gail A. "Phase II Trials in Drug Development and Adaptive Trial Design." JACC: Basic to Translational Science 4, no. 3 (June 2019): 428–37. http://dx.doi.org/10.1016/j.jacbts.2019.02.005.
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Allison, Malorye. "Biomarker-led adaptive trial blazes a trail in breast cancer." Nature Biotechnology 28, no. 5 (May 2010): 383–84. http://dx.doi.org/10.1038/nbt0510-383.
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Moatti, M., S. Zohar, W. F. Rosenberger, and S. Chevret. "A Bayesian Hybrid Adaptive Randomisation Design for Clinical Trials with Survival Outcomes." Methods of Information in Medicine 55, no. 01 (2016): 4–13. http://dx.doi.org/10.3414/me14-01-0132.
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SummaryBackground: Response-adaptive randomisation designs have been proposed to im -prove the efficiency of phase III randomised clinical trials and improve the outcomes of the clinical trial population. In the setting of failure time outcomes, Zhang and Rosen -berger (2007) developed a response-adaptive randomisation approach that targets an optimal allocation, based on a fixed sample size. Objectives: The aim of this research is to propose a response-adaptive randomisation procedure for survival trials with an interim monitoring plan, based on the following optimal criterion: for fixed variance of the esti -mated log hazard ratio, what allocation minimizes the expected hazard of failure? We demonstrate the utility of the design by re -designing a clinical trial on multiple myeloma. Methods: To handle continuous monitoring of data, we propose a Bayesian response-adap -tive randomisation procedure, where the log hazard ratio is the effect measure of interest. Combining the prior with the normal likelihood, the mean posterior estimate of the log hazard ratio allows derivation of the optimal target allocation. We perform a simu lationstudy to assess and compare the perform -ance of this proposed Bayesian hybrid adaptive design to those of fixed, sequential or adaptive – either frequentist or fully Bayesian – designs. Non informative normal priors of the log hazard ratio were used, as well as mixture of enthusiastic and skeptical priors. Stopping rules based on the posterior dis -tribution of the log hazard ratio were com -puted. The method is then illus trated by redesigning a phase III randomised clinical trial of chemotherapy in patients with multiple myeloma, with mixture of normal priors elicited from experts. Results: As expected, there was a reduction in the proportion of observed deaths in the adaptive vs. non-adaptive designs; this reduction was maximized using a Bayes mix -ture prior, with no clear-cut improvement by using a fully Bayesian procedure. The use of stopping rules allows a slight decrease in the observed proportion of deaths under the alternate hypothesis compared with the adaptive designs with no stopping rules. Conclusions: Such Bayesian hybrid adaptive survival trials may be promising alternatives to traditional designs, reducing the duration of survival trials, as well as optimizing the ethical concerns for patients enrolled in the trial.
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Fine, Michael S., and Kurt A. Thoroughman. "Trial-by-Trial Transformation of Error Into Sensorimotor Adaptation Changes With Environmental Dynamics." Journal of Neurophysiology 98, no. 3 (September 2007): 1392–404. http://dx.doi.org/10.1152/jn.00196.2007.
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Humans can rapidly change their motor output to make goal-directed reaching movements in a new environment. Theories that describe this adaptive process have long presumed that adaptive steps scale proportionally with error. Here we show that while performing a novel reaching task, participants did not adopt a fixed learning rule, but instead modified their adaptive response based on the statistical properties of the movement environment. We found that as the directional bias of the force distribution shifted from strongly biased to unbiased, participants transitioned from an adaptive process that scaled proportionally with error to one that adapted to the direction, but not magnitude, of error. Participants also modified their response as the likelihood of the perturbation changed; as the likelihood decreased from 80 to 20% of trials, participants adopted an increasingly disproportional strategy. We propose that people can rapidly switch between learning processes within minutes of experiencing a novel environment.
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Grayling, Michael John, and Graham Mark Wheeler. "A review of available software for adaptive clinical trial design." Clinical Trials 17, no. 3 (February 17, 2020): 323–31. http://dx.doi.org/10.1177/1740774520906398.
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Background/aims: The increasing cost of the drug development process has seen interest in the use of adaptive trial designs grow substantially. Accordingly, much research has been conducted to identify barriers to increasing the use of adaptive designs in practice. Several articles have argued that the availability of user-friendly software will be an important step in making adaptive designs easier to implement. Therefore, we present a review of the current state of software availability for adaptive trial design. Methods: We review articles from 31 journals published in 2013–2017 that relate to methodology for adaptive trials to assess how often code and software for implementing novel adaptive designs is made available at the time of publication. We contrast our findings against these journals’ policies on code distribution. We also search popular code repositories, such as Comprehensive R Archive Network and GitHub, to identify further existing user-contributed software for adaptive designs. From this, we are able to direct interested parties toward solutions for their problem of interest. Results: Only 30% of included articles made their code available in some form. In many instances, articles published in journals that had mandatory requirements on code provision still did not make code available. There are several areas in which available software is currently limited or saturated. In particular, many packages are available to address group sequential design, but comparatively little code is present in the public domain to determine biomarker-guided adaptive designs. Conclusions: There is much room for improvement in the provision of software alongside adaptive design publications. In addition, while progress has been made, well-established software for various types of trial adaptation remains sparsely available.
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Giovagnoli, Alessandra. "The Bayesian Design of Adaptive Clinical Trials." International Journal of Environmental Research and Public Health 18, no. 2 (January 10, 2021): 530. http://dx.doi.org/10.3390/ijerph18020530.
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This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.
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Giovagnoli, Alessandra. "The Bayesian Design of Adaptive Clinical Trials." International Journal of Environmental Research and Public Health 18, no. 2 (January 10, 2021): 530. http://dx.doi.org/10.3390/ijerph18020530.
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This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.
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Bretz, Frank, Paul Gallo, and Willi Maurer. "Adaptive designs: The Swiss Army knife among clinical trial designs?" Clinical Trials 14, no. 5 (March 22, 2017): 417–24. http://dx.doi.org/10.1177/1740774517699406.
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There has been considerable progress in the development and implementation of adaptive designs over the past 30 years. A major driver for this class of novel designs is the possibility to increase the information value of clinical trial data to enable better decisions, leading to more efficient drug development processes and improved late-stage success rates. In the first part of this article, we review the development of adaptive designs from different perspectives. We trace back key historical papers, report on landmark adaptive design clinical trials, review major cross-industry collaborations, and highlight key regulatory guidance documents. In the second, more technical part of this article, we address the question of whether it is possible to define factors which guide the choice between a fixed or an adaptive design for a given trial. We show that in non-linear regression models with a moderate variance of the responses, the first-stage sample size of an adaptive design should be chosen sufficiently large in order to address variability in the interim parameter estimate. In conclusion, the choice between an adaptive and a fixed design depends in a sensitive manner on the specific statistical problem under investigation.
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Burkhardt, B., A. Faldum, and R. Schmidt. "Adaptive Designs with Discrete Test Statistics and Consideration of Overrunning." Methods of Information in Medicine 54, no. 05 (2015): 434–46. http://dx.doi.org/10.3414/me14-02-0023.
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Summary Background: Interim analyses are used in clinical trials in order to enable early decisions for medical, ethical, and economic reasons. However, it appears unfeasible to stop a trial during such an interim analysis. New patients will thus enter the trial while the interim analysis is ongoing. Moreover, depending on the event kinetics of the specific disease, the trial design, and the corresponding endpoints, some patients might still be unevaluable at the interim analysis due to not yet completed follow-up. Occur-rence of these types of patients is characteristic for sequentially analyzed trials. Such patients are referred to as interim patients. In trials with multiple primary endpoints, another type of interim patients occurs. If some but not all null hypotheses can be rejected at the interim analysis, the trial might be continued to a second stage in order to answer the remaining questions. These second stage patients, however, provide new data to all trial questions including the already rejected ones and thus formally act as interim patients regarding the already rejected null hypotheses. Although all kinds of interim patients are not part of the interim analysis, the data collected on those patients have to be sent to the office of regulatory affairs and will be analyzed. If a smaller or contrasting treatment effect is observed in interim patients, this might lead to a withdrawal of an earlier superiority proof. Objectives: Presently, interim patients and their data are usually not considered in the confirmatory test. We offer a strategy to deal with interim patients in sequentially analyzed trials with discrete test statistics. The method covers sequentially analyzed single-and multi-arm trials with one or multiple primary endpoints. Methods: When planning adaptive designs, it is common practice to assume that the stage-wise p-values are independent and standard uniformly distributed under the null hypothesis. In the context of discrete test statistics, this implies conservative tests. We provide an algorithm which iteratively optimizes an initially given design while adjusting for both discreteness of test statistics and interim patients. The algorithm is described verbally, graphically and formally to facilitate immediate implementation in computer software. Results: The optimized design exploits the aspired significance level better and is more powerful than the initial one. The algorithm applies to fixed sample and planned flexible adaptive designs for single- and multi-arm trials with one or multiple primary endpoints. The benefit increases with the number of interim patients. Conclusions: When planning a trial with interim analyses, the rules for decisions must be adjusted to interim patients. Otherwise, the test procedure is conservative resulting in loss of power. This is essential in situations where the number of interim patients is important compared to the first stage, particularly in trials with multiple primary endpoints.
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Crawford, Mike J., Kirsten Barnicot, Sue Patterson, and Christian Gold. "Negative results in phase III trials of complex interventions: Cause for concern or just good science?" British Journal of Psychiatry 209, no. 1 (July 2016): 6–8. http://dx.doi.org/10.1192/bjp.bp.115.179747.
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SummaryNot all interventions that show promise in exploratory trials will be supported in phase III studies. But the high failure rate in recent trials of complex mental health interventions is a concern. Proper consideration of trial processes and greater use of adaptive trial designs could ensure better use of available resources.
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Gömöry, D., L. Paule, and E. Gömöryová. "Effects of microsite variation on growth and adaptive traits in a beech provenance trial." Journal of Forest Science 57, No. 5 (May 16, 2011): 192–99. http://dx.doi.org/10.17221/88/2010-jfs.
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ABSTRACT: The effects of the within-trial spatial variation of environmental factors on phenotypic traits were studied in the Slovak plot of the international beech provenance trial coordinated by BFH Grosshansdorf with 32 provenances, established under a randomized complete block design with three adjacent blocks. Five indicators of soil properties (soil moisture, bulk density and pH) and microclimate (average daily temperature and temperature amplitude) were assessed at 96 points distributed over a 10 × 10 m grid and their values for the positions of individual trees were estimated by ordinary point kriging. The evaluation of phenotypic variation (height, diameter, Julian days of spring flushing and autumn leaf discoloration, vegetation period length, late frost damage) using a common two-way analysis of variance showed a significant provenance × block interaction effect indicating the heterogeneity of blocks. Analysis of covariance using single-tree kriging estimates of environmental variables as covariates showed that in addition to provenance, all phenotypic traits were significantly affected by microsite, especially by temperature fluctuation. Employing methods incorporating the spatial component in the evaluation of tree breeding field experiments is advocated.
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Ji, Lingyun, Lisa M. McShane, Mark Krailo, and Richard Sposto. "Bias in retrospective analyses of biomarker effect using data from an outcome-adaptive randomized trial." Clinical Trials 16, no. 6 (October 3, 2019): 599–609. http://dx.doi.org/10.1177/1740774519875969.
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Background/Aims Biomarker-stratified outcome-adaptive randomization trials, in which randomization probabilities depend on both biomarker value and outcomes of previously treated patients, are receiving increased attention in oncology research. Data from these trials can also form the basis of investigation of additional biomarkers that may not have been incorporated into the original trial design. In this article, we investigate the validity of a standard analytical method that utilizes data from a biomarker-stratified outcome-adaptive randomization trial to assess the effect of a newly identified biomarker on patient outcomes. Methods In the context of an ancillary biomarker study for a two-arm phase II trial with a response endpoint, we conduct analytic and simulation studies to investigate bias in estimated biomarker effects under outcome-adaptive randomization. Conditions under which bias arises and magnitude of the bias are examined in several settings. We then propose unbiased estimators of biomarker effects with appropriate variance estimators. Results We demonstrate that use of biomarker-stratified outcome-adaptive randomization perturbs the patient population and treatment assignments. Consequently, application of standard analysis methods to data from an outcome-adaptive randomization trial either to estimate prognostic effect of a new biomarker in uniformly treated patients or to estimate effect of treatment in relation to the new biomarker can lead to substantially biased estimates. The proposed adjusted estimators are asymptotically unbiased, and the proposed variance estimators correctly reflect the sample variability in the estimators. Conclusion This article demonstrates existence of bias when standard, naïve statistical methods are utilized to assess biomarker effects using data from a biomarker-stratified outcome-adaptive randomization trial, and hence that results from naïve analyses must be interpreted with great caution. These findings highlight that, in an era where data and specimens are increasingly being shared for biomarker studies, care must be taken to document and understand implications of the study design under which specimens or data have been obtained.
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Simon, Richard. "Review of Statistical Methods for Biomarker-Driven Clinical Trials." JCO Precision Oncology, no. 3 (December 2019): 1–9. http://dx.doi.org/10.1200/po.18.00407.
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The discovery of somatic driver mutations in kinases and receptors has stimulated the development of molecularly targeted treatments that require companion diagnostics and new approaches to clinical development. This article reviews some of the clinical trial designs that have been developed to address these opportunities, including phase II basket and platform trials as well as phase III enrichment and biomarker adaptive designs. It also re-examines some of the conventional wisdom that previously dominated clinical trial design and discusses development and internal validation of a predictive biomarker as a new paradigm for optimizing the intended-use subset for a treatment. Statistical methods now being used in adaptive biomarker-driven clinical trials are reviewed. Some previous paradigms for clinical trial design can limit the development of more effective methods on the basis of prospectively planned adaptive methods, but useful new methods have been developed for analysis of genome-wide data and for the design of adaptively enriched studies. In many cases, the heterogeneity of populations eligible for clinical trials as traditionally defined makes it unlikely that molecularly targeted treatments will be effective for a majority of the eligible patients. New methods for dealing with patient heterogeneity in therapeutic response should be used in the design of phase III clinical trials.
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Buyse, Marc. "Limitations of Adaptive Clinical Trials." American Society of Clinical Oncology Educational Book, no. 32 (June 2012): 133–37. http://dx.doi.org/10.14694/edbook_am.2012.32.13.
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Overview: Adaptive designs are aimed at introducing flexibility in clinical research by allowing important characteristics of a trial to be adapted during the course of the trial based on data coming from the trial itself. Adaptive designs can be used in all phases of clinical research, from phase I to phase III. They tend to be especially useful in early development, when the paucity of prior data makes their flexibility a key benefit. The need for adaptive designs lessened as new treatments progress to later phases of development, when emphasis shifts to confirmation of hypotheses using fully prespecified, well-controlled designs.
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Metcalfe, Andrew, Elke Gemperle Mannion, Helen Parsons, Jaclyn Brown, Nicholas Parsons, Josephine Fox, Rebecca Kearney, et al. "Protocol for a randomised controlled trial of Subacromial spacer for Tears Affecting Rotator cuff Tendons: a Randomised, Efficient, Adaptive Clinical Trial in Surgery (START:REACTS)." BMJ Open 10, no. 5 (May 2020): e036829. http://dx.doi.org/10.1136/bmjopen-2020-036829.
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IntroductionShoulder pain due to irreparable rotator cuff tears can cause substantial disability, but treatment options are limited. A balloon spacer is a relatively simple addition to a standard arthroscopic debridement procedure, but it is costly and there is no current randomised trial evidence to support its use. This trial will evaluate the clinical and cost-effectiveness of a subacromial balloon spacer for individuals undergoing arthroscopic debridement for irreparable rotator cuff tears.New surgical procedures can provide substantial benefit to patients. Good quality randomised controlled trials (RCTs) are needed, but trials in surgery are typically long and expensive, exposing patients to risk and the healthcare system to substantial costs. One way to improve the efficiency of trials is with an adaptive sample size. Such methods are well established in drug trials but have rarely, if ever, been used in surgical trials.Methods and analysisSubacromial spacer for Tears Affecting Rotator cuff Tendons: a Randomised, Efficient, Adaptive Clinical Trial in Surgery (START:REACTS) is a participant and assessor blinded, adaptive, multicentre RCT comparing arthroscopic debridement with the InSpace balloon (Stryker, USA) to arthroscopic debridement alone for people with a symptomatic irreparable rotator cuff tear. It uses a group sequential adaptive design where interim analyses are performed using all of the 3, 6 and 12-month data that are available at each time point. A maximum of 221 participants will be randomised (1:1 ratio), this will provide 90% power (at the 5% level) for a 6 point difference in the primary outcome; the Oxford Shoulder Score at 12 months. A substudy will use deltoid-active MRI scans in 56 participants to assess the function of the balloon. Analysis will be on an intention-to-treat basis and reported according to principles established in the Consolidated Standards of Reporting Trials statement.Ethics and disseminationNRES number 18/WM/0025. The results will be disseminated via peer-reviewed publications, presentations at conferences, lay summaries and social media.Trial registration numberISRCTN17825590
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Mueller, R., E. Rahm, J. Ramsch, B. Heller, M. Loeffler, and U. Greiner. "AdaptFlow: Protocol-based Medical Treatment Using Adaptive Workflows." Methods of Information in Medicine 44, no. 01 (2005): 80–88. http://dx.doi.org/10.1055/s-0038-1633926.
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Summary Objectives: In many medical domains investigator-initiated clinical trials are used to introduce new treatments and hence act as implementations of guideline-based therapies. Trial protocols contain detailed instructions to conduct the therapy and additionally specify reactions to exceptional situations (for instance an infection or a toxicity). To increase quality in health care and raise the number of patients treated according to trial protocols, a consultation system is needed that supports the handling of the complex trial therapy processes efficiently. Our objective was to design and evaluate a consultation system that should 1) observe the status of the therapies currently being applied, 2) offer automatic recognition of exceptional situations and appropriate decision support and 3) provide an automatic adaptation of affected therapy processes to handle exceptional situations. Methods: We applied a hybrid approach that combines process support for the timely and efficient execution of the therapy processes as offered by workflow management systems with a knowledge and rule base and a mechanism for dynamic workflow adaptation to change running therapy processes if induced by changed patient condition. Results and Conclusions: This approach has been implemented in the AdaptFlow prototype. We performed several evaluation studies on the practicability of the approach and the usefulness of the system. These studies show that the AdaptFlow prototype offers adequate support for the execution of real-world investigator-initiated trial protocols and is able to handle a large number of exceptions.
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Holm Hansen, Christian, Pamela Warner, Richard A. Parker, Brian R. Walker, Hilary OD Critchley, and Christopher J. Weir. "Development of a Bayesian response-adaptive trial design for the Dexamethasone for Excessive Menstruation study." Statistical Methods in Medical Research 26, no. 6 (September 30, 2015): 2681–99. http://dx.doi.org/10.1177/0962280215606155.
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It is often unclear what specific adaptive trial design features lead to an efficient design which is also feasible to implement. This article describes the preparatory simulation study for a Bayesian response-adaptive dose-finding trial design. Dexamethasone for Excessive Menstruation aims to assess the efficacy of Dexamethasone in reducing excessive menstrual bleeding and to determine the best dose for further study. To maximise learning about the dose response, patients receive placebo or an active dose with randomisation probabilities adapting based on evidence from patients already recruited. The dose-response relationship is estimated using a flexible Bayesian Normal Dynamic Linear Model. Several competing design options were considered including: number of doses, proportion assigned to placebo, adaptation criterion, and number and timing of adaptations. We performed a fractional factorial study using SAS software to simulate virtual trial data for candidate adaptive designs under a variety of scenarios and to invoke WinBUGS for Bayesian model estimation. We analysed the simulated trial results using Normal linear models to estimate the effects of each design feature on empirical type I error and statistical power. Our readily-implemented approach using widely available statistical software identified a final design which performed robustly across a range of potential trial scenarios.
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Supawattan, Busaba, and Lily Ingsrisawa. "Bayesian Adaptive Randomization Designs for Clinical Trial." Journal of Applied Sciences 15, no. 2 (January 15, 2015): 374–76. http://dx.doi.org/10.3923/jas.2015.374.376.
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Bandyopadhyay, Uttam, Atanu Biswas, and Shirsendu Mukherjee. "A response-adaptive design in crossover trial." Statistics 46, no. 5 (October 2012): 645–61. http://dx.doi.org/10.1080/02331888.2010.545211.
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Gluud, Christian, Anders Dejgaard, Michael Krams, Ingrid Wallenbeck, Gervais Tougas, Jorn Wetterslev, Carl-Fredrik Burman, and Per Spindler. "International Symposium on Adaptive Clinical Trial Designs." Drug Information Journal 42, no. 1 (January 2008): 93–97. http://dx.doi.org/10.1177/009286150804200113.
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Corey, L., G. J. Nabel, C. Dieffenbach, P. Gilbert, B. F. Haynes, M. Johnston, J. Kublin, et al. "HIV-1 Vaccines and Adaptive Trial Designs." Science Translational Medicine 3, no. 79 (April 20, 2011): 79ps13. http://dx.doi.org/10.1126/scitranslmed.3001863.
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Wang, Mey, Ya-Chi Wu, and Guei-Feng Tsai. "A Regulatory View of Adaptive Trial Design." Journal of the Formosan Medical Association 107, no. 12 (December 2008): S3—S8. http://dx.doi.org/10.1016/s0929-6646(09)60002-4.
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Wason, James, Nigel Stallard, Janet Dunn, and Rob Stein. "An adaptive biomarker strategy clinical trial design." Trials 14, Suppl 1 (2013): O103. http://dx.doi.org/10.1186/1745-6215-14-s1-o103.
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Zapf, Antonia, Maria Stark, Oke Gerke, Christoph Ehret, Norbert Benda, Patrick Bossuyt, Jon Deeks, Johannes Reitsma, Todd Alonzo, and Tim Friede. "Adaptive trial designs in diagnostic accuracy research." Statistics in Medicine 39, no. 5 (November 27, 2019): 591–601. http://dx.doi.org/10.1002/sim.8430.
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Viele, Kert, Kristine Broglio, Anna McGlothlin, and Benjamin R. Saville. "Comparison of methods for control allocation in multiple arm studies using response adaptive randomization." Clinical Trials 17, no. 1 (October 19, 2019): 52–60. http://dx.doi.org/10.1177/1740774519877836.
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Background/Aims: Response adaptive randomization has many polarizing properties in two-arm settings comparing control to a single treatment. The generalization of these features to the multiple arm setting has been less explored, and existing comparisons in the literature reach disparate conclusions. We investigate several generalizations of two-arm response adaptive randomization methods relating to control allocation in multiple arm trials, exploring how critiques of response adaptive randomization generalize to the multiple arm setting. Methods: We perform a simulation study to investigate multiple control allocation schemes within response adaptive randomization, comparing the designs on metrics such as power, arm selection, mean square error, and the treatment of patients within the trial. Results: The results indicate that the generalization of two-arm response adaptive randomization concerns is variable and depends on the form of control allocation employed. The concerns are amplified when control allocation may be reduced over the course of the trial but are mitigated in the methods considered when control allocation is maintained or increased during the trial. In our chosen example, we find minimal advantage to increasing, as opposed to maintaining, control allocation; however, this result reflects an extremely limited exploration of methods for increasing control allocation. Conclusion: Selection of control allocation in multiple arm response adaptive randomization has a large effect on the performance of the design. Some disparate comparisons of response adaptive randomization to alternative paradigms may be partially explained by these results. In future comparisons, control allocation for multiple arm response adaptive randomization should be chosen to keep in mind the appropriate match between control allocation in response adaptive randomization and the metric or metrics of interest.
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Hanscom, Brett, James P. Hughes, Brian D. Williamson, and Deborah Donnell. "Adaptive non-inferiority margins under observable non-constancy." Statistical Methods in Medical Research 28, no. 10-11 (October 8, 2018): 3318–32. http://dx.doi.org/10.1177/0962280218801134.
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A central assumption in the design and conduct of non-inferiority trials is that the active-control therapy will have the same degree of effectiveness in the planned non-inferiority trial as in the prior placebo-controlled trials used to define the non-inferiority margin. This is referred to as the ‘constancy’ assumption. If the constancy assumption fails, decisions based on the chosen non-inferiority margin may be incorrect, and the study runs the risk of approving an inferior product or failing to approve a beneficial product. The constancy assumption cannot be validated in a trial without a placebo arm, and it is unlikely ever to be met completely. When there are strong, observable predictors of constancy, such as dosing and adherence to the active-control product, we can specify conditions where the constancy assumption will likely fail. We propose a method for using measurable predictors of active-control effectiveness to specify non-inferiority margins targeted to the planned study population characteristics. We describe a pre-specified method, using baseline characteristics or post-baseline predictors in the active-control arm, to adapt the non-inferiority margin at the end of the study if constancy is violated. Adaptive margins can help adjust for constancy violations that will inevitably occur in real clinical trials, while maintaining pre-specified levels of Type I error and power.
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Lepore, Michael. "PEOPLE LIVING WITH DEMENTIA GUIDED THE WAY: ADAPTIVE STAKEHOLDER ENGAGEMENT TO DESIGN A PRAGMATIC CLINICAL TRIAL." Innovation in Aging 6, Supplement_1 (November 1, 2022): 467–68. http://dx.doi.org/10.1093/geroni/igac059.1816.
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Abstract Research on non-pharmacologic interventions for people living with dementia (PLWD) has shown many benefits, but healthcare systems do not offer these interventions widely. To strengthen understanding of the real-world effectiveness of evidence-based non-pharmacologic interventions for PLWD requires a pragmatic approach to trialing interventions in the contexts of healthcare systems where they are standardly delivered. Designing pragmatic trials with the engagement of primary stakeholders can support trial feasibility and meaningful outcome measurement, but little evidence is available on approaches for engaging PLWD in designing pragmatic trials. This longitudinal case study introduces adaptive stakeholder engagement, a three-phase approach to engaging PLWD in planning a pragmatic trial. First, a multistakeholder workshop including PLWD was held where participants prioritized topics for research, including research on the impact of increasing social activity. Second, an evidence-based non-pharmacological intervention that addresses this priority area was piloted with PLWD and qualitative feedback was collected from PLWD over the course of intervention piloting to inform the trial design. Finally, PLWD were engaged in facilitated monthly meetings to provide input on the pragmatic trial design. In addition to informing intervention selection, input collected from PLWD informed the pragmatic trial in several ways, including approaches to PLWD recruitment, disclosure of research to PLWD, and intervention orientation processes. The adaptive stakeholder engagement model involved PLWD in different roles, as workshop participants, intervention participants, and trial advisors. This model enabled PLWD to be engaged in priority setting, infrastructure development, and trial protocol design, and may be useful for researchers planning future pragmatic trials.
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Watts, R. J., D. S. Ryder, C. Allan, and S. Commens. "Using river-scale experiments to inform variable releases from large dams: a case study of emergent adaptive management." Marine and Freshwater Research 61, no. 7 (2010): 786. http://dx.doi.org/10.1071/mf09190.
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Case studies of successful adaptive management generally focus on examples that have frameworks for adaptive management embedded from project conception. In contrast, this paper outlines an example of emergent adaptive management. We describe an approach whereby targeted research and collaboration among stakeholders assisted learning, and ultimately the development of interim operational guidelines for increased within-channel flow variability in the highly regulated Mitta Mitta River, which is managed as part of the River Murray System in the Murray–Darling Basin, Australia. Environmental monitoring of four variable flow trials evaluated the response of water column microbial activity, benthic and water column metabolism, the structure and composition of algal biofilms, and benthic macroinvertebrates to increased flow variability created by varying the release from Dartmouth Reservoir. Each trial built upon lessons from previous trials, with collaboration among key stakeholders occurring before, during and after each trial. Institutional conditions encouraged a shift to adaptive management over time that helped to achieve environmental, social and economic objectives downstream of the dam. A key lesson is that adaptive management does not have to be specified a priori, but can emerge within a trusting relationship between stakeholders as long as they are willing and able to change their operational paradigm.