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A Bayesian multi-arm multi-stage clinical trial design incorporating information about treatment ordering.

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.

SUBMITTER: Serra A

PROVIDER: S-EPMC10962588 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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A Bayesian multi-arm multi-stage clinical trial design incorporating information about treatment ordering.

Serra Alessandra A Mozgunov Pavel P Jaki Thomas T

Statistics in medicine 20230509 16

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 propo ...[more]

PMID: 37158302

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Project description:BackgroundRandomised controlled trials are becoming increasingly costly and time-consuming. In 2011, Royston and colleagues proposed a particular class of multi-arm multi-stage (MAMS) designs intended to speed up the evaluation of new treatments in phase II and III clinical trials. Their design, which controls the type I error rate and power for each pairwise comparison, discontinues randomisation to poorly performing arms at interim analyses if they fail to show a pre-specified level of benefit over the control arm. Arms in which randomisation is continued to the final stage of the trial are compared against the control on a definitive time-to-event outcome measure. To increase efficiency, interim comparisons can be made on an intermediate time-to-event outcome which is on the causal pathway to the definitive outcome.MethodsWe adapt Royston's MAMS design to binary outcomes observed at the end of a fixed follow-up period and analysed using an absolute difference in proportions. We apply the design to tuberculosis (TB), an area where many new drugs are in development, and demonstrate how it can greatly accelerate the evaluation of new TB regimens. We use simulations to support the extensions to the methodology and to investigate the amount of bias in the estimated treatment effects of arms in which randomisation is ceased at the first interim analysis and arms which continue to the final stage of the trial.ResultsThe proposed seamless phase II/III TB trial designs are shown to greatly reduce sample size requirements and trial duration compared to conducting separate phase II and III trials. The bias in the estimated treatment effects for the definitive outcome is shown to be small, especially when treatment selection is based on an intermediate outcome or when a reanalysis is performed at the planned end of the trial after all recruited patients have completed follow-up.ConclusionsThe proposed designs are practical and could be used in a variety of disease areas. They hold considerable promise for speeding up the evaluation of new treatments particularly in TB where many new regimens will soon be available for testing in phase II and phase III trials.

Project description:BackgroundMulti-Arm Multi-Stage designs aim at comparing several new treatments to a common reference, in order to select or drop any treatment arm to move forward when such evidence already exists based on interim analyses. We redesigned a Bayesian adaptive design initially proposed for dose-finding, focusing our interest in the comparison of multiple experimental drugs to a control on a binary criterion measure.MethodsWe redesigned a phase II clinical trial that randomly allocates patients across three (one control and two experimental) treatment arms to assess dropping decision rules. We were interested in dropping any arm due to futility, either based on historical control rate (first rule) or comparison across arms (second rule), and in stopping experimental arm due to its ability to reach a sufficient response rate (third rule), using the difference of response probabilities in Bayes binomial trials between the treated and control as a measure of treatment benefit. Simulations were then conducted to investigate the decision operating characteristics under a variety of plausible scenarios, as a function of the decision thresholds.ResultsOur findings suggest that one experimental treatment was less efficient than the control and could have been dropped from the trial based on a sample of approximately 20 instead of 40 patients. In the simulation study, stopping decisions were reached sooner for the first rule than for the second rule, with close mean estimates of response rates and small bias. According to the decision threshold, the mean sample size to detect the required 0.15 absolute benefit ranged from 63 to 70 (rule 3) with false negative rates of less than 2 % (rule 1) up to 6 % (rule 2). In contrast, detecting a 0.15 inferiority in response rates required a sample size ranging on average from 23 to 35 (rules 1 and 2, respectively) with a false positive rate ranging from 3.6 to 0.6 % (rule 3).ConclusionAdaptive trial design is a good way to improve clinical trials. It allows removing ineffective drugs and reducing the trial sample size, while maintaining unbiased estimates. Decision thresholds can be set according to predefined fixed error decision rates.Trial registrationClinicalTrials.gov Identifier: NCT01342692 .

Project description:BackgroundMulti-arm multi-stage trials are an efficient, adaptive approach for testing many treatments simultaneously within one protocol. In settings where numbers of patients available to be entered into trials and resources might be limited, such as primary postpartum haemorrhage, it may be necessary to select a pre-specified subset of arms at interim stages even if they are all showing some promise against the control arm. This will put a limit on the maximum number of patients required and reduce the associated costs. Motivated by the World Health Organization Refractory HaEmorrhage Devices trial in postpartum haemorrhage, we explored the properties of such a selection design in a randomised phase III setting and compared it with other alternatives. The objectives are: (1) to investigate how the timing of treatment selection affects the operating characteristics; (2) to explore the use of an information-rich (continuous) intermediate outcome to select the best-performing arm, out of four treatment arms, compared with using the primary (binary) outcome for selection at the interim stage; and (3) to identify factors that can affect the efficiency of the design.MethodsWe conducted simulations based on the refractory haemorrhage devices multi-arm multi-stage selection trial to investigate the impact of the timing of treatment selection and applying an adaptive allocation ratio on the probability of correct selection, overall power and familywise type I error rate. Simulations were also conducted to explore how other design parameters will affect both the maximum sample size and trial timelines.ResultsThe results indicate that the overall power of the trial is bounded by the probability of 'correct' selection at the selection stage. The results showed that good operating characteristics are achieved if the treatment selection is conducted at around 17% of information time. Our results also showed that although randomising more patients to research arms before selection will increase the probability of selecting correctly, this will not increase the overall efficiency of the (selection) design compared with the fixed allocation ratio of 1:1 to all arms throughout.ConclusionsMulti-arm multi-stage selection designs are efficient and flexible with desirable operating characteristics. We give guidance on many aspects of these designs including selecting the intermediate outcome measure, the timing of treatment selection, and choosing the operating characteristics.

Dataset Information

A Bayesian multi-arm multi-stage clinical trial design incorporating information about treatment ordering.

Publications

A Bayesian multi-arm multi-stage clinical trial design incorporating information about treatment ordering.

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