1. The process starts with the biomarker profile assessment of all eligible patients and then according to the profile of each individual, the study population will be assigned to the different biomarker groups. Due to the fact that at the beginning of the trial we do not know the true disease control rate (i.e., the proportion of patients who demonstrate response to a treatment) the trial begins with equal randomization so that each treatment by biomarker subgroup is composed of at least one individual with a known disease control status (whether the patient will experience progression given a certain treatment).
2. Next, the trial continues with adaptive randomization of patients; this is achieved by using the Bayesian probit model to calculate the posterior disease control rate. After the posterior rate is found, we define the randomization rate as the posterior mean of the disease control rate of each treatment in each biomarker-defined subgroup.
3. The adaptive randomization process continuous until the last individual is enrolled and can stop early only in case that all treatments are dropped due to inefficacy.

Note: Whereas in many trial designs the baseline covariate (in this case the biomarker) is considered as prognostic, the design proposed by Zhou et al. (2008) allows for modelling the treatment by biomarker interactions where the biomarker is in fact predictive. The incorporation of the above hierarchical Bayesian structure allows ‘borrowing strength’ or information-sharing across patients receiving the same treatment but with different biomarker profiles, Zhou et al. (2008).

Advantages

• Smart, novel and ethical approach
• Permits updating patient's outcome.
• Can result in high probability of success of the trial as there is increase in the number of patients who receive effective treatments.
• Type I and II errors can be controlled by carefully calibrating the design parameters
• Can boost patients' ethics as patients are assigned to the best available therapy

Limitations

• Complexity in terms of building-up the trial design, conduct and analysis of the trial
• Can make incorrect decisions in case of incorrect biomarker selection as the design is based on the accumulated data about how well the biomarker performs.
• Requirement of relatively short biomarker and endpoint assessment
• Likely to introduce bias due to time trends in the prognostic mix of individuals enrolled to the study.

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• Description: The Bayesian Covariate Adjusted Response-Adaptive Randomization (BCARA) which combines a Bayesian, an adaptive and biomarker classification approach aims to match patients with the most efficacious treatments by utilizing patient’s biomarker information becoming available during the conduct of the clinical trial. It is also considered as a response-adaptive randomization strategy as the allocation of the study population depends on the responses of previous outcomes. A partial least square logistic regression approach is conducted to determine adaptively predictive biomarker-defined subsets.

• Application: This strategy may be useful in the explanatory phase II setting of the drug development .

• Alternative names: No alternative names found for this trial deisgn
• Methodology:

  1. R andomly assign the first  patients to the different treatment arms where J the number of different treatment groups and K the number of biomarkers. At least one response should be observed in each of the different treatment groups before moving to the Bayesian response adaptive randomization

  2. A fter each new individual has been enrolled in the study, predictive biomarker-defined groups are determined by utilizing a partial least squares logistic regression strategy (PLSLR) which can predict whether the patient can benefit from the treatment. The biomarker status is determined before the randomization .

  3. A fter the establishment of the biomarker status and biomarker-defined groups of each new individual, the individual is then randomly assigned into one of the treatment arms using a BCARA randomization .

  4. A ccording to the results of the BCARA randomization the trial either stops or continues based on decision rules proposed by Eickhoff et al. (2010) [53]. The Bayesian covariate adjusted response-adaptive trial design has the ability to identify the biomarker-defined groups likely to  respond to a treatment but it does not control the Type I error and in order to ensure that the identified result is true, a Phase III study should be conducted

• Adavantages and limitations:

Advantages

Limitations

Ability to incorporate prior knowledge from biomarkers into the design. Identification of the subgroups for which a particular experimental treatment is more effective. Can result in reduction of the number of patients required when compared to alternative designs (i.e, non-adaptive trial designs). Solves the issue of the incorporation of information of multiple and possibly correlated biomarkers.

The Type I error is not controlled in the traditional sense. An independent Phase III study focused on the selected biomarker-defined subgroups is required to show that the identified promising result is definitely true.

• References:

Eickhoff JC, Kim K, Beach J, Kolesar JM, Gee JR. A Bayesian adaptive design with biomarkers for targeted therapies. Clinical trials (London, England). 2010;7(5):546–56. doi: 10.1177/1740774510372657. View Article PubMed/NCBI Google Scholar

Tajik P, Zwinderman AH, Mol BW, Bossuyt PM. Trial designs for personalizing cancer care: a systematic review and classification. Clinical cancer research: an official journal of the American Association for Cancer Research. 2013;19(17):4578–88. doi: 10.1158/1078-0432.CCR-12-3722. View Article PubMed/NCBI   Google Scholar