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Published on: September 20, 2019
Uncertainty directed factorial clinical trials
Gopal Kotecha1,2, Steffen Ventz3, Sandra Fortini4
1Department of Biostatistics, Harvard School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA.
Abstract:
The development and evaluation of novel treatment combinations is a key component of modern clinical research. The primary goals of factorial clinical trials of treatment combinations range from the estimation of intervention-specific effects, or the discovery of potential synergies, to the identification of combinations with the highest response probabilities. Most factorial studies use balanced or block randomization, with an equal number of patients assigned to each treatment combination, irrespective of the specific goals of the trial. Here, we introduce a class of Bayesian response-adaptive designs for factorial clinical trials with binary outcomes. The study design was developed using Bayesian decision-theoretic arguments and adapts the randomization probabilities to treatment combinations during the enrollment period based on the available data. Our approach enables the investigator to specify a utility function representative of the aims of the trial, and the Bayesian response-adaptive randomization algorithm aims to maximize this utility function. We considered several utility functions and factorial designs tailored to them. Then, we conducted a comparative simulation study to illustrate relevant differences of key operating characteristics across the resulting designs. We also investigated the asymptotic behavior of the proposed adaptive designs. We also used data summaries from three recent factorial trials in perioperative care, smoking cessation, and infectious disease prevention to define realistic simulation scenarios and illustrate advantages of the introduced trial designs compared to other study designs.
Insights
This study introduces Bayesian response-adaptive designs for factorial clinical trials. These adaptive designs optimize treatment allocation based on accumulating data, aiming to maximize trial objectives more efficiently than traditional methods.
Area of Science:
- Clinical Trials
- Biostatistics
- Bayesian Methodology
Background:
- Factorial clinical trials are crucial for evaluating novel treatment combinations and identifying optimal strategies.
- Traditional factorial designs often use balanced randomization, which may not be optimal for all trial objectives.
- There is a need for adaptive designs that can efficiently allocate patients to treatment combinations based on emerging data.
Purpose of the Study:
- To introduce a class of Bayesian response-adaptive designs for factorial clinical trials with binary outcomes.
- To develop an algorithm that adapts randomization probabilities based on a specified utility function representing trial aims.
- To compare the performance of these novel adaptive designs against traditional designs.
Main Methods:
- Developed Bayesian decision-theoretic arguments to create response-adaptive randomization for factorial trials.
- Incorporated investigator-defined utility functions to guide the adaptive algorithm.
- Conducted comparative simulation studies using realistic scenarios from perioperative care, smoking cessation, and infectious disease prevention trials.
Main Results:
- The proposed Bayesian response-adaptive designs demonstrated potential advantages over traditional designs in simulation studies.
- Different utility functions led to tailored factorial designs with distinct operating characteristics.
- Asymptotic behavior of the adaptive designs was investigated, providing theoretical insights.
Conclusions:
- Bayesian response-adaptive designs offer a flexible and potentially more efficient approach for factorial clinical trials.
- These adaptive strategies can be tailored to specific trial goals, such as estimating intervention effects or finding synergistic combinations.
- The findings suggest that adaptive randomization can improve the efficiency and effectiveness of factorial trial designs in various medical fields.
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