Guest post by Safeer Khan, Lecturer at Department of Pharmaceutical Sciences, Government College University, Lahore, Pakistan

Multi-Arm & Multi-Stage (MAMS) Clinical Trials Design Tips

The design of clinical trials is increasingly challenged by the Rising Costs, limited availability of eligible patient populations, and the growing demand for timely therapeutic evaluation. Traditional parallel-group designs, which typically compare a single intervention to a control, are often insufficient to meet these pressures in terms of speed, efficiency, and resource utilization.

In response, multi-arm and multi-stage (MAMS) trial designs have gained significant attention. These designs enable the simultaneous assessment of multiple interventions within a single trial protocol. They also incorporate pre-specified interim analyses to support early decisions regarding the continuation or discontinuation of individual study arms. This blog presents a set of evidence-informed design recommendations aimed at optimizing MAMS trials.

Why Researchers Use MAMS Trials

1. Shared Control Groups: The use of a single control group across multiple treatment arms reduces the overall sample size and enhances statistical efficiency. This approach also improves the internal validity of the trial by facilitating direct comparisons among interventions under consistent control conditions.
2. Operational Efficiency: Coordinating a single, integrated trial framework is often more efficient than managing multiple standalone studies. Centralized processes for site activation, participant recruitment, and data collection streamline operations and reduce administrative burden.
3. Accelerated Insights: Evaluating multiple investigational treatments concurrently allows for faster identification of both efficacious and non-efficacious options. This is particularly advantageous in settings where timely therapeutic advancement is critical, such as in high-burden or rapidly evolving disease areas. The increased speed and breadth of evidence also contribute to enhanced multi-arm clinical study informativeness.

Complexities in MAMS Clinical Trials

Complexity in Design and Implementation

One of the foremost challenges associated with MAMS trials is the inherent complexity in their design and implementation. The multi-arm clinical study design farmwork requires precise planning to ensure that multiple treatment groups can be effectively managed while adhering to regulatory requirements and maintaining scientific validity [1]. Implementing adaptive designs introduces real-time decision-making based on interim data, which can complicate the trial process. This complexity often necessitates advanced statistical expertise and robust data management systems, which may not always be available to all research teams [2].

Increased Statistical Challenges

The statistical considerations in MAMS trials are more intricate than those in traditional trial designs. Properly controlling for Type I error rates—a critical aspect since these trials involve multiple hypotheses—presents significant difficulties. For instance, managing the family-wise error rate (FWER) becomes crucial to avoid false positives when assessing several treatment arms simultaneously [3]. Furthermore, specific designs that allow for adaptive randomization may compromise the rigor of statistical analysis if not managed correctly, potentially leading to biased results [3].

Resource Intensiveness

MAMS trials can be resource-intensive due to their demand for continuous monitoring and data analysis throughout the trial period. Identifying and maintaining the appropriate infrastructure for managing data collection, interim analysis, and participant feedback can lead to substantial operational costs. This poses a barrier, particularly for smaller research institutions that may lack the necessary funding or expertise to undertake such trials effectively [4, 5]. Due to this reason strategic multi-arm clinical study cost modelling becomes essential in these settings to forecast resource needs and mitigate the budgetary strain.

Designing MAMS Trials: Key Considerations

Pre-Specified Stopping Rules

Effective MAMS trials require well-defined interim analyses with clear stopping rules. These rules determine when to halt a treatment arm based on pre-established criteria for efficacy or futility [6]. Proper implementation of these rules can lead to reduced trial durations and minimized patient exposure to ineffective treatments. Frameworks, such as Decision-Theoretic Designs, have been proposed to guide these processes by optimizing the identification of effective treatments [7].

Utilization of Adaptive Randomization

Adaptive randomization methods can enhance the likelihood of patient assignment to effective arms based on accumulating results throughout the trial [8]. For example, Bayesian adaptive designs allow for the ongoing adjustment of probabilities associated with treatment efficacy, thereby improving overall allocation efficiency [9]. This approach can lead to an increased chance of successful outcomes while reducing recruitment time and resource use.

Informed Sample Size Determination

Accurate sample size calculations are essential for the success of any clinical trial. MAMS designs can achieve greater statistical power with fewer participants due to their ability to eliminate ineffective treatments early. Literature suggests that using adaptive designs may substantially reduce expected sample sizes compared to traditional fixed designs [10]. Read more about: Adaptive Clinical Study Design. This reduction not only speeds up the trial but also frees up resources [11]. These efficiencies can be better understood and achieved through robust multi-arm Clinical Study Cost Modelling.

Incorporating Covariate Information

Covariate adaptation refers to the process of modeling patient-specific factors, such as demographics or biomarkers, which may influence treatment outcomes [12]. By stratifying patients effectively, researchers can ensure that treatment effects are balanced across differing population segments, thus increasing the relevance and robustness of findings. Improving adaptability to participant characteristics can also enhance recruitment and retention strategies [7].

Interim Monitoring for Decision Making

Continual evaluation of treatment arms through interim analyses is a hallmark of MAMS trials. These checkpoints allow for the reassessment of ongoing efficacy and safety of treatments, leading to more informed decision-making [3]. An effective monitoring plan derived from Bayesian principles can aid trial investigators in navigating complex adaptive designs, ensuring timely responses to collected data [8]. This ongoing oversight helps mitigate multi-stage clinical study risk while enhancing overall trial informativeness.

Engagement with Regulatory Bodies

Early and consistent dialogue with regulatory agencies is essential when designing MAMS trials. Understanding regulatory requirements regarding multi-arm structures and adaptive protocols can facilitate smoother approvals and enhance credibility [13]. Trial protocols should transparently outline the adaptive strategies employed, including statistical methods and stopping rules, to avoid issues during regulatory review [14].

Case Studies & Applications

Several recent trials exemplify how MAMS frameworks effectively minimize time and resource expenditure while maximizing treatment discovery potential.

STAMPEDE Trial: This trial investigates treatment protocols in prostate cancer using a multi-arm, multi-stage design. It adapts based on the interim results of various treatment arms, demonstrating the capability of MAMS trials to yield efficient outcomes and ethically sound decision-making in cancer treatment [15].

MND-SMART Trial: The Motor Neuron Disease Systematic Multi-Arm Adaptive Randomised Trial (MND-SMART) serves as a platform trial evaluating repurposed drugs for motor neuron disease [16]. Utilizing a MAMS structure allows for rapid assessment of multiple drugs through shared control arms, thus reducing recruitment timelines while ensuring participant safety.

Tuberculosis Treatments: Researchers have successfully employed MAMS designs to identify effective treatments for tuberculosis, allowing for early stopping based on interim results. This rapidly adaptable design has proven instrumental in delivering safe and effective treatment options to patients facing serious health challenges [3].

Conclusion

MAMS clinical trials represent a significant shift towards more agile, efficient, and effective evaluation of new medical treatments. By applying the key methodological considerations, researchers can optimize clinical trial outcomes. As the clinical research environment becomes increasingly complex, the evidence and operational lessons derived from MAMS trial designs will be instrumental in driving more efficient and effective approaches to patient care.

References

1. Noor, N.M., et al., Uptake of the Multi-Arm Multi-Stage (MAMS) Adaptive Platform Approach: A Trial-Registry Review of Late-Phase Randomised Clinical Trials. BMJ Open, 2022. 12(3): p. e055615.

2. Noor, N.M., et al., Adaptive Platform Trials Using Multi-Arm, Multi-Stage Protocols: Getting Fast Answers in Pandemic Settings. F1000research, 2020. 9: p. 1109.

3. Bratton, D.J., P. Phillips, and M. Parmar, A Multi-Arm Multi-Stage Clinical Trial Design for Binary Outcomes With Application to Tuberculosis. BMC Medical Research Methodology, 2013. 13(1).

4. Moore, C.L., et al., Multi-Arm, Multi-Stage Randomised Controlled Trials for Evaluating Therapeutic HIV Cure Interventions. The Lancet Hiv, 2019. 6(5): p. e334-e340.

5. Wu, J., Y. Li, and L. Zhu, Group Sequential Multi‐arm Multi‐stage Trial Design With Treatment Selection. Statistics in Medicine, 2023. 42(10): p. 1480-1491.

6. Eisenstein, E.L., et al., Impact of the Patient-Reported Outcomes Management Information System (PROMIS) Upon the Design and Operation of Multi-Center Clinical Trials: A Qualitative Research Study. Journal of Medical Systems, 2010. 35(6): p. 1521-1530.

7. Jaki, T. and L.V. Hampson, Designing Multi‐arm Multi‐stage Clinical Trials Using a Risk–benefit Criterion for Treatment Selection. Statistics in Medicine, 2015. 35(4): p. 522-533.

8. Bassi, A., et al., Bayesian Adaptive Decision-Theoretic Designs for Multi-Arm Multi-Stage Clinical Trials. Statistical Methods in Medical Research, 2020. 30(3): p. 717-730.

9. Mulier, G., et al., Bayesian Optimal Designs for Multi-Arm Multi-Stage Phase II Randomized Clinical Trials With Multiple Endpoints. Statistics in Biopharmaceutical Research, 2024. 16(3): p. 315-325.

10. Swain-Cabriales, S., et al., Enrollment Onto Breast Cancer Therapeutic Clinical Trials: A Tertiary Cancer Center Experience. Applied Nursing Research, 2013. 26(3): p. 133-135.

11. Jaki, T., P. Pallmann, and D. Magirr, The R Package MAMS for Designing Multi-Arm Multi-Stage Clinical Trials. Journal of Statistical Software, 2019. 88(4).

12. Jaki, T. and D. Magirr, Considerations on Covariates and Endpoints in Multi‐arm Multi‐stage Clinical Trials Selecting All Promising treatments. Statistics in Medicine, 2012. 32(7): p. 1150-1163.

13. Shan, G., H. Zhang, and T. Jiang, Minimax and Admissible Adaptive Two-Stage Designs in Phase II Clinical Trials. BMC Medical Research Methodology, 2016. 16(1).

14. Passia, N., et al., Single Dental Implant Retained Mandibular Complete Dentures – Influence of the Loading Protocol: Study Protocol for a Randomized Controlled Trial. Trials, 2014. 15(1).

15. Carthon, B.C. and E.S. Antonarakis, The STAMPEDE trial: paradigm-changing data through innovative trial design. Translational cancer research, 2016. 5(3 Suppl): p. S485.

16. Wong, C., et al., Motor Neuron Disease Systematic Multi-Arm Adaptive Randomised Trial (MND-SMART): A Multi-Arm, Multi-Stage, Adaptive, Platform, Phase III Randomised, Double-Blind, Placebo-Controlled Trial of Repurposed Drugs in Motor Neuron Disease._ BMJ Open, 2022. 12(7): p. e064173.