Outcome-adaptive randomization is one of the possible elements of an adaptive trial design in which the ratio of patients randomly assigned to the experimental treatment arm versus the control ...treatment arm changes from 1:1 over time to randomly assigning a higher proportion of patients to the arm that is doing better. Outcome-adaptive randomization has intuitive appeal in that, on average, a higher proportion of patients will be treated on the better treatment arm (if there is one). In both the randomized phase II and phase III settings with a short-term binary outcome, we compare outcome-adaptive randomization with designs that use 1:1 and 2:1 fixed-ratio randomizations (in the latter, twice as many patients are randomly assigned to the experimental treatment arm). The comparisons are done in terms of required sample sizes, the numbers and proportions of patients having an inferior outcome, and we restrict attention to the situation in which one treatment arm is a control treatment (rather than the less common situation of two experimental treatments without a control treatment). With no differential patient accrual rates because of the trial design, we find no benefits to outcome-adaptive randomization over 1:1 randomization, and we recommend the latter. If it is thought that the patient accrual rates will be substantially higher because of the possibility of a higher proportion of patients being randomly assigned to the experimental treatment (because the trial will be more attractive to patients and clinicians), we recommend using a fixed 2:1 randomization instead of an outcome-adaptive randomization.
Multiplatform trials in which experimental groups are allowed to enter and exit the trial at different times often include a single control group in which recruitment is not concurrent with that in ...the experimental groups, a fact that complicates assurance that patients in the control and experimental groups are fully matched and comparable.
There is a wide range of adaptive elements of clinical trial design (some old and some new), with differing advantages and disadvantages. Classical interim monitoring, which adapts the design based ...on early evidence of superiority or futility of a treatment arm, has long been known to be extremely useful. A more recent application of interim monitoring is in the use of phase II/III designs, which can be very effective (especially in the setting of multiple experimental treatments and a reliable intermediate end point) but do have the cost of having to commit earlier to the phase III question than if separate phase II and phase III trials were performed. Outcome-adaptive randomization is an older technique that has recently regained attention; it increases trial complexity and duration without offering substantial benefits to the patients in the trial. The use of adaptive trials with biomarkers is new and has great potential for efficiently identifying patients who will be helped most by specific treatments. Master protocols in which trial arms and treatment questions are added to an ongoing trial can be especially efficient in the biomarker setting, where patients are screened for entry into different subtrials based on evolving knowledge about targeted therapies. A discussion of three recent adaptive clinical trials (BATTLE-2, I-SPY 2, and FOCUS4) highlights the issues.
New oncology therapies that extend patients' lives beyond initial expectations and improving later-line treatments can lead to complications in clinical trial design and conduct. In particular, for ...trials with event-based analyses, the time to observe all the protocol-specified events can exceed the finite follow-up of a clinical trial or can lead to much delayed release of outcome data. With the advent of multiple classes of oncology therapies leading to much longer survival than in the past, this issue in clinical trial design and conduct has become increasingly important in recent years. We propose a straightforward prespecified backstop rule for trials with a time-to-event analysis and evaluate the impact of the rule with both simulated and real-world trial data. We then provide recommendations for implementing the rule across a range of oncology clinical trial settings.
As precision medicine becomes more precise, the sizes of the molecularly targeted subpopulations become increasingly smaller. This can make it challenging to conduct randomized clinical trials of the ...targeted therapies in a timely manner. To help with this problem of a small patient subpopulation, a study design that is frequently proposed is to conduct a small randomized clinical trial (RCT) with the intent of augmenting the RCT control arm data with historical data from a set of patients who have received the control treatment outside the RCT (historical control data). In particular, strategies have been developed that compare the treatment outcomes across the cohorts of patients treated with the standard (control) treatment to guide the use of the historical data in the analysis; this can lessen the potential well-known biases of using historical controls without any randomization. Using some simple examples and completed studies, we demonstrate in this commentary that these strategies are unlikely to be useful in precision medicine applications.
When designing a randomized clinical trial to compare two treatments, the sample size required to have desired power with a specified type 1 error depends on the hypothesis testing procedure. With a ...binary endpoint (e.g., response), the trial results can be displayed in a 2 × 2 table. If one does the analysis conditional on the number of positive responses, then using Fisher's exact test has an actual type 1 error less than or equal to the specified nominal type 1 error. Alternatively, one can use one of many unconditional “exact” tests that also preserve the type 1 error and are less conservative than Fisher's exact test. In particular, the unconditional test of Boschloo is always at least as powerful as Fisher's exact test, leading to smaller required sample sizes for clinical trials. However, many statisticians have argued over the years that the conditional analysis with Fisher's exact test is the only appropriate procedure. Since having smaller clinical trials is an extremely important consideration, we review the general arguments given for the conditional analysis of a 2 × 2 table in the context of a randomized clinical trial. We find the arguments not relevant in this context, or, if relevant, not completely convincing, suggesting the sample‐size advantage of the unconditional tests should lead to their recommended use. We also briefly suggest that since designers of clinical trials practically always have target null and alternative response rates, there is the possibility of using this information to improve the power of the unconditional tests.
The use of biomarkers to identify patients who can benefit from treatment with a specific anticancer agent has the potential to both improve patient care and accelerate drug development. The ...development of targeted agents and their accompanying biomarkers frequently occurs contemporaneously, and confidence in a putative biomarker's performance might, therefore, be insufficient to restrict the definitive testing of a new agent to the subgroup of biomarker-positive patients. This Review considers which clinical trial designs and analysis strategies are appropriate for use in phase III, biomarker-driven, randomized clinical trials, on the basis of pre-existing evidence that the biomarker can successfully identify patients who will respond to the treatment in question. The types of interim monitoring that are appropriate for these trials are also discussed. In addition, enrichment strategies based on the use of prognostic biomarkers to separate a population into subgroups with better and worse outcomes, regardless of treatment, are described. Finally, the possibility of formally using a biomarker during phase II drug development, to select what type of biomarker-driven strategy should be used in the phase III trial, is discussed.
A Problematic Biomarker Trial Design Freidlin, Boris; Korn, Edward L
JNCI : Journal of the National Cancer Institute,
02/2022, Letnik:
114, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Abstract
Efficient biomarker-driven randomized clinical trials are a key tool for implementing precision oncology. A commonly used biomarker phase III design is focused on testing the treatment ...effect in biomarker-positive and overall study populations. This approach may result in recommending new treatments to biomarker-negative patients when these treatments have no benefit for these patients.
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