Group sequential designs for developing and testing biomarker-guided personalized therapies in comparative effectiveness research

Abstract

Biomarker-guided personalized therapies offer great promise to improve drug development and improve patient care, but also pose difficult challenges in designing clinical trials for the development and validation of these therapies. We first give a review of the existing approaches, briefly for clinical trials in new drug development and in more detail for comparative effectiveness trials involving approved treatments. We then introduce new group sequential designs to develop and test personalized treatment strategies involving approved treatments.

Adaptive design of confirmatory trials: Advances and challenges

“…A subsequent refinement by Lai [54] shows the relatively simple rule that chooses the treatment with the largest upper confidence bound $U_k^{\left(n\right)}$ for θ k to be asymptotically optimal if the upper confidence bound at stage n , with n > k , is defined by $$U_k^{\left(n\right)}=\inf true\{\theta \in A:\theta \ge {\hat{\theta }}_k\text{and}2T_n\left(k\right)I({\stackrel{true^}{\theta }}_k,\theta )\ge h^2(T_n\left(k\right)∕n)true\},\inf \varnothing =\infty ,$$ where A is some open interval known to contain θ , ${\hat{\theta }}_k$ is the maximum likelihood estimate of θ k , I ( θ, λ ) is the KullbackLeibler information number, and the function h has a closed-form approximation. It is noted in [55] that the upper confidence bound $U_k^{\left(n\right)}$ corresponds to inverting a generalized likelihood ratio (GLR) test based on the GLR statistic $T_n\left(k\right)I({\stackrel{true^}{\theta }}_k,\theta )$ for testing θ k = θ .…”

“…Making use of these ideas, Lai, Liao and Kim [55] have recently introduced a frequentist alternative to the Bayesian adaptive design of the BATTLE trial. While the spirit of the BATTLE trial focuses on attaining the best response rate for patients in the trial, it does not establish which treatment is the best for future patients, with a guaranteed probability of correct selection.…”

Clinical trial designs incorporating predictive biomarkers

“…Lai et al published a novel group sequential design for developing and testing biomarker-based treatment strategies in the area of comparative effectiveness research, where the relative efficacy of approved rather than new treatments are of interest [58]. With emphasis on attaining the best response rates for patients in the trial through adaptive randomization, the design addressed three objectives: (1) treating accrued patients with the best (yet unknown) available treatment, (2) developing a treatment strategy for future patients, and (3) demonstrating that the strategy developed has better outcomes than the historical mean effect of standard-of-care therapy plus some threshold indicating a clinically significant improvement.…”

Treatment evaluation for a data‐driven subgroup in adaptive enrichment designs of clinical trials

“…Alternatively, a target subpopulation can be identified at the end of a broad eligibility trial under an adaptive signature design, [7][8][9][10] or at an interim analysis (with the possibility of restricting subsequent enrollment) under an adaptive enrichment design (AED). [11][12][13][14][15][16][17][18][19] The various approaches have been discussed and compared by Wang, O'Neill and Hun, 20 FDA, 21 Wang and Hung, 22 Chen et al, 23 and Simon. 24 Most of the existing literature on AEDs (cited above) deals with one or two predefined subgroups, although Lai, Liao, and Kim 17 and Magnusson and Turnbull 18 consider multiple predefined subgroups.…”

“…[11][12][13][14][15][16][17][18][19] The various approaches have been discussed and compared by Wang, O'Neill and Hun, 20 FDA, 21 Wang and Hung, 22 Chen et al, 23 and Simon. 24 Most of the existing literature on AEDs (cited above) deals with one or two predefined subgroups, although Lai, Liao, and Kim 17 and Magnusson and Turnbull 18 consider multiple predefined subgroups. Predefined subgroups are not always available, however.…”