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Published on: September 20, 2019
Considerations on covariates and endpoints in multi-arm multi-stage clinical trials selecting all
1Medical and Pharmaceutical Research Unit, Department of Mathematics and Statistics, Lancaster University, UK. jaki.thomas@gmail.com
Abstract:
In early stages of drug development, there is often uncertainty about the most promising among a set of different treatments. To ensure the best use of resources in such situations, it is important to decide which, if any, of the treatments should be taken forward for further testing. In later development, it has been shown that evaluating more than one dose increases the chance of success substantially. In this work, we discuss how multi-arm multi-stage trials can be designed such that all promising treatments are kept in the study at the interim analyses. We first investigate the impact of deviating from the planned design and show how confidence intervals can be constructed before we consider the impact of important covariates. We show that under orthogonality, the inclusion of covariates has no effect on familywise error rate control in the strong sense. We further show that the derived methodology can be used to investigate non-normal endpoints.
Insights
Multi-arm multi-stage trials help select promising drug treatments early. This design ensures effective resource allocation by keeping viable options in study, improving drug development success rates.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Drug Development
Background:
- Early drug development faces uncertainty in selecting the most promising treatments.
- Resource optimization is crucial for efficient progression of potential therapies.
- Evaluating multiple doses in later development significantly enhances success probability.
Purpose of the Study:
- To design multi-arm multi-stage (MAMS) trials that retain promising treatments at interim analyses.
- To investigate the impact of deviations from planned trial designs.
- To assess the influence of covariates on trial outcomes and error rate control.
Main Methods:
- Utilizing multi-arm multi-stage trial designs.
- Analyzing deviations from planned trial protocols.
- Constructing confidence intervals.
- Incorporating covariate analysis.
- Evaluating familywise error rate control under orthogonality.
- Applying methodology to non-normal endpoints.
Main Results:
- MAMS trials can be designed to preserve promising treatments through interim analyses.
- Deviations from planned designs can be managed, and confidence intervals constructed.
- Covariate inclusion does not impact strong familywise error rate control under orthogonality.
- The methodology extends to the analysis of non-normal endpoints.
Conclusions:
- Multi-arm multi-stage trial designs offer a robust framework for efficient early-stage drug development.
- The proposed methods provide flexibility in trial design and analysis, including covariate adjustment.
- The approach supports the investigation of diverse endpoints, enhancing the utility of MAMS trials.
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