Fully Adaptive Designs for Clinical Trials: Simultaneous Learning from Multiple Patients

Ahuja, Vishal; Birge, John R.

doi:10.2139/ssrn.2126906

Cited by 1 publication

(8 citation statements)

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“…Another method is to use polynomials, for example least squares approximation using Chebyshev polynomials (Judd, 1998). (Ahuja and Birge, 2014) is the only study that has used approximation in the context of adaptive designs for clinical trials; they use a truncated-horizon or limited-lookahead approximation method.…”

Section: Literaturementioning

confidence: 99%

“…We follow the Bayes-adaptive Markov decision process (BAMDP) model developed in (Ahuja and Birge, 2014). The state in the BAMDP model is a vector with dimension equal to the number of treatmentoutcome combinations, also called health conditions.…”

Section: Modelmentioning

confidence: 99%

“…Calculating theoretical bounds on the optimality loss is a subject of future work. The rest of the parameters and modeling assumptions remain the same as in (Ahuja and Birge, 2014).…”

Section: Modelmentioning

confidence: 99%

“…(Berry and Fristedt, 1985)), where patients are treated one at a time (in a sequence), and each patient's responses is available before making an allocation decision for the next patient. (Ahuja and Birge, 2014) extends this model to incorporate simultaneous allocation of multiple patients and show that this results in an improved objective function value (e.g., expected patient successes) compared to naive implementation of sequential designs, thus substantially widening the potential for applicability of such designs.…”

Section: Introductionmentioning

confidence: 99%

“…A major barrier to implementing adaptive designs in practice is computational. Bandit problems in clinical trials context are typically modeled as MDP's, where the solution is obtained by solving a finitehorizon dynamic program (Ahuja and Birge, 2014). However, the problem size increases exponentially as the number of time periods, patients, or treatmentoutcome combinations increase, commonly referred to as the curse of dimensionality (Powell, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Approximation Methods for Determining Optimal Allocations in Response Adaptive Clinical Trials

Ahuja

Birge

Ryan

2014

Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems

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Clinical trials have traditionally followed a fixed design, in which patient allocation to treatments is fixed throughout the trial and specified in the protocol. The primary goal of this static design is to learn about the efficacy of treatments. Response-adaptive designs, where assignment to treatments evolves as patient outcomes are observed, are gaining in popularity due to potential for improvements in cost and efficiency over traditional designs. Such designs can be modeled as a Bayesian adaptive Markov decision process (BAMDP). Given the forward-looking nature of the underlying algorithms which solve BAMDP, the problem size grows as the trial becomes larger or more complex, often exponentially, making it computationally challenging to find an optimal solution. In this study, we propose grid-based approximation to reduce the computational burden. The proposed methods also open the possibility of implementing adaptive designs to large clinical trials. Further, we use numerical examples to demonstrate the effectiveness of our approach, including the effects of changing the number of observations and the grid resolution.

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Section: Literaturementioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

“…Calculating theoretical bounds on the optimality loss is a subject of future work. The rest of the parameters and modeling assumptions remain the same as in (Ahuja and Birge, 2014).…”

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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