Clinical Trial Designs for Testing Companion Diagnostics (CDx)
Il-Jin Kim in Companion Diagnostics (CDx) in Precision Medicine, 2019
An adaptive design is defined as a study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of usually interim data from subjects in the study. A movement toward adaptive biomarker-guided trial designs emerged after the first few years of establishing the fixed designs described above. Subsequently, several adaptive biomarker-guided designs have been submitted.3, 5–8, 10, 17 In these adaptive designs, all of the eligible subjects are recruited during the first stage, followed by an interim analysis to determine the subsequent study design. The study can continue in the subgroup only, or in the full population with or without consideration of the subgroup, or stop for futility or efficacy. The estimation and decision rules for adaptive designs may be performed within either classical or Bayesian statistical paradigms.
Challenges in Cancer Clinical Trials
Wei Zhang, Fangrong Yan, Feng Chen, Shein-Chung Chow in Advanced Statistics in Regulatory Critical Clinical Initiatives, 2022
Trial operational characteristics are evaluated by statistical simulation and are summarized in Table 7.3. For the same average total sample size and PFS events, the fixed design has higher “total power” than the adaptive design when the sub-groups have similar hazard ratios (short as HRs), and this is because the adaptive design faces the statistical penalty from closed hypothesis testing procedure. However, the adaptive design has higher power than the fixed design when the HRs become disparate (e.g., HR of 0.55 for cutaneous subgroup and 0.85 for non-cutaneous subgroup), and this is because that the adaptive design allows for learning and sub-sequent improvement in sample size or change in population so that generates higher power when data falls into “promising zone” or “enrichment zone”. This is the real benefit of adaptive design. Across all scenarios, the adaptive design has longer trial duration than the fixed design and this is because of the splitting of study into two cohorts and the awaiting time of the data maturity in cohort 2 which is enrolled after the interim analysis.
Bayesian Adaptive Designs in Drug Development
Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger in Bayesian Methods in Pharmaceutical Research, 2020
There are many reasons to consider an adaptive design. Benefits apply over a broad range of clinical trials, including studies that occur early in the drug development process. These reasons include some ethical arguments, such as providing information about the superiority (or inferiority) of a new treatment option earlier than one might if one waited until the planned end of the study. Providing this information as soon as the evidence supports the conclusion allows patients and their caregivers to benefit from this knowledge. Patients participating in the study may benefit if the design includes pre-planned modifications to drop apparently less effective therapies during the study, while retaining the more effective or less toxic treatment arms for continued evaluation.
A signature enrichment design with Bayesian adaptive randomization
Published in Journal of Applied Statistics, 2021
Fang Xia, Stephen L. George, Jing Ning, Liang Li, Xuelin Huang
In contrast to traditional non-adaptive design methods, adaptive designs have recently gained popularity due to their flexibility and efficiency. In 2006, the Pharmaceutical Research and Manufactures of America (PhRMA) Working Group defined an adaptive design as a design that allows modification of the on-going study based on accumulating data, without undermining the validity and integrity of the trial [10]. Examples of adaptive designs include those that use adaptive randomization [18], sample size re-estimation [16], changes to eligibility criteria [41], and early stopping rules for safety, futility or efficacy [23]. Adaptive randomization procedures can use a frequentist framework [19] or a Bayesian framework [5]. Commonly used adaptive randomization methods include treatment-adaptive randomization, covariate adjustment randomization and response adaptive randomization [43]. Bayesian adaptive randomization methods allow the combination of prior knowledge and observed data to learn about parameters of interest [11]. Bayesian adaptive randomization procedures apply Bayes theorem repetitively based on accumulating data to adjust the randomization probability for each newly enrolled patient. Such procedures may be considered more ethical by assigning more patients to the more effective treatment arm. Several randomized phase II clinical trials have adopted Bayesian adaptive randomization, including BATTLE and I-SPY2 [2,14,22,36]. Randomized phase II trials are becoming attractive in the modern era because they greatly enhance the potential for biomarker discovery and can assure optimal use of limited phase III financial and patient resources [24]. Based on this consideration, in this article, we propose a new design for randomized phase II trials, which are exploratory rather than confirmatory in nature.
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