arXiv subjects

Thevaa Chandereng

Publications and source records attributed to Thevaa Chandereng.

3 recordsLinked to original sources

arXiv2021

A Markov Decision Process for Response-Adaptive Randomization in Clinical Trials

In clinical trials, response-adaptive randomization (RAR) has the appealing ability to assign more subjects to better-performing treatments based on interim results. The traditional RAR strategy alters the randomization ratio on a patient-by-patient basis; this has been heavily criticized for bias due to time-trends. An alternate approach is blocked RAR, which groups patients together in blocks and recomputes the randomization ratio in a block-wise fashion; the final analysis is then stratified by block. However, the typical blocked RAR design divides patients into equal-sized blocks, which is not generally optimal. This paper presents TrialMDP, an algorithm that designs two-armed blocked RAR clinical trials. Our method differs from past approaches in that it optimizes the size and number of blocks as well as their treatment allocations. That is, the algorithm yields a policy that adaptively chooses the size and composition of the next block, based on results seen up to that point in the trial. TrialMDP is related to past works that compute optimal trial designs via dynamic programming. The algorithm maximizes a utility function balancing (i) statistical power, (ii) patient outcomes, and (iii) the number of blocks. We show that it attains significant improvements in utility over a suite of baseline designs, and gives useful control over the tradeoff between statistical power and patient outcomes. It is well suited for small trials that assign high cost to failures. We provide TrialMDP as an R package on GitHub: https://github.com/dpmerrell/TrialMDP

stat.ME↗

arXiv2019

Robust Blocked Response-Adaptive Randomization Designs

In most clinical trials, patients are randomized with equal probability among treatments to obtain an unbiased estimate of the treatment effect. Response-adaptive randomization (RAR) has been proposed for ethical reasons, where the randomization ratio is tilted successively to favor the better performing treatment. However, the substantial disagreement regarding bias due to time-trends in adaptive randomization is not fully recognized. The type-I error is inflated in the traditional Bayesian RAR approaches when a time-trend is present. In our approach, patients are assigned in blocks and the randomization ratio is recomputed for blocks rather than traditional adaptive randomization where it is done per patient. We further investigate the design with a range of scenarios for both frequentist and Bayesian designs. We compare our method with equal randomization and with different numbers of blocks including the traditional RAR design where randomization ratio is altered patient by patient basis. The analysis is stratified if there are two or more patients in each block. Small blocks should be avoided due to the possibility of not acquiring any information from the $μ_i$. On the other hand, RAR with large blocks has a good balance between efficiency and treating more subjects to the better-performing treatment, while retaining blocked RAR's unique unbiasedness.

stat.AP↗

arXiv2018

Imbalanced Randomization in Clinical Trials

Randomization is a common technique used in clinical trials to eliminate potential bias and confounders in a patient population. Equal allocation to treatment groups is the standard due to its optimal efficiency in many cases. However, in certain scenarios, unequal allocation can improve efficiency. In superiority trials with more than two groups, the optimal randomization is not always a balanced randomization. In non-inferiority trials, additive margin with equal variance is the only instance with balanced randomization. Optimal randomization for non-inferiority trials can be far from 1:1 and can greatly improve efficiency, a fact which is commonly overlooked. A tool for sample size calculation for non-inferiority trials with additive or multiplicative margin with normal, binomial or Poisson distribution is available at http://www.statlab.wisc.edu/shiny/SSNI/.

stat.AP↗