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International Biometric Society
PHASE II TRIAL DESIGN WITH BAYESIAN RESPONSE ADAPTIVE COVARIATE BALANCED
RANDOMIZATION USING LONGITUDINAL PATIENT OUTCOMES
Tomoyoshi Hatayama*1,2 Satoshi Morita*3
1: Department of Biostatistics and Epidemiology, Graduate School of Medicine,
Yokohama City University, Yokohama, Japan
2: Department of Biostatistics, ACRONET Corporation, Tokyo, Japan
3: Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate
School of Medicine, Kyoto, Japan
[Background]
The response adaptive randomization (RAR) method is used to increase the number of
patients assigned to more efficacious treatment arms in clinical trials. In many trials
evaluating longitudinal patient outcomes, RAR methods based only on the final
measurement may not benefit significantly from RAR due to its delayed initiation.
Alternatively, initiation of RAR can be hastened by using longitudinal patient outcomes
evaluated previous to the last measurement (longitudinal RAR). In addition, when treatments
are compared, it is important to ensure any observed treatment effect is attributable to the
treatment itself rather than to any particular patient characteristic.
[Aim]
We propose a Bayesian covariate balanced (CB) RAR method that improves RAR
performance by accounting for longitudinal patient outcomes while controlling baseline
covariates imbalance.
[Methods]
We use a Bayesian linear mixed effects model to analyze longitudinal continuous patient
outcomes for calculating longitudinal RAR based allocation probability. To control the
covariate imbalance during RAR, we use the idea of the biased coin design where the
assignment that will result in the minimum covariate imbalance is given a higher probability
[1]. We use a combination of the two probability of RAR based allocation probability and
probability of biased coin design as the allocation probability for assigning more patients to
presumably superior treatment while controlling covariate imbalance (longitudinal CBRAR).
Additionally, it is known that RAR implementation can lead to excessively large patient
allocation imbalances and may result in substantial loss of statistical power. Therefore, we
aim to mitigate the loss of statistical power due to large patient allocation imbalances by
embedding penalties into the longitudinal RAR based allocation probability calculation. We
evaluated the operating characteristics of our proposed longitudinal CBRAR using extensive
simulation.
[Results]
Simulation results showed that our proposed longitudinal CBRAR method assigned more
patients to the presumably superior treatment arm compared to the RAR method based only
on the final measurement and longitudinal CBRAR method achieved smaller covariate
imbalance than any randomization method without covariate adjustment. In addition, the
embedded penalty effectively worked to prevent extreme patient allocation imbalances.
Although our longitudinal CBRAR method decreased the statistical power compared to a
completely randomization (CR) method, our method more desirably performed in reducing
the number of patients who would not be benefited.
[Conclusion]
This study suggests that accounting for longitudinal outcome measurements in calculating
patient allocation probabilities may lead to improving the performance of a RAR method.
Moreover, proposed method improves RAR by controlling covariate imbalance between
treatment arms.
[Reference]
1. Ning J, Huang X. Response-adaptive randomization for clinical trials with adjustment for
covariate imbalance. Stat Med 2010; 29: 1761-68.
International Biometric Conference, Florence, ITALY, 6 – 11 July 2014