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.
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.
Response-adaptive randomization, which changes the randomization ratio as a randomized clinical trial progresses, is inefficient as compared to a fixed 1:1 randomization ratio in terms of increased ...required sample size. It is also known that response-adaptive randomization leads to biased treatment effects if there are time trends in the accruing outcome data, for example, due to changes in the patient population being accrued, evaluation methods, or concomitant treatments. Response-adaptive-randomization analysis methods that account for potential time trends, such as time-block stratification or re-randomization, can eliminate this bias. However, as shown in this Commentary, these analysis methods cause a large additional inefficiency of response-adaptive randomization, regardless of whether a time trend actually exists.
Establishing trial-level surrogacy of an intermediate endpoint for predicting survival benefit in future trials is extremely challenging because of the extrapolations required, but there are other ...useful drug development and patient management applications of intermediate endpoints.
.
Purpose: Many anticancer therapies benefit only a subset of treated patients and may be overlooked by the traditional broad eligibility
approach to design phase III clinical trials. New ...biotechnologies such as microarrays can be used to identify the patients
that are most likely to benefit from anticancer therapies. However, due to the high-dimensional nature of the genomic data,
developing a reliable classifier by the time the definitive phase III trail is designed may not be feasible.
Experimental Design: Previously, Freidlin and Simon ( Clinical Cancer Research , 2005) introduced the adaptive signature design that combines a prospective development of a sensitive patient classifier
and a properly powered test for overall effect in a single pivotal trial. In this article, we propose a cross-validation extension
of the adaptive signature design that optimizes the efficiency of both the classifier development and the validation components
of the design.
Results: The new design is evaluated through simulations and is applied to data from a randomized breast cancer trial.
Conclusion: The cross-validation approach is shown to considerably improve the performance of the adaptive signature design. We also
describe approaches to the estimation of the treatment effect for the identified sensitive subpopulation. Clin Cancer Res;
16(2); 691–8
Purpose: A new generation of molecularly targeted agents is entering the definitive stage of clinical evaluation. Many of these drugs
benefit only a subset of treated patients and may be overlooked ...by the traditional, broad-eligibility approach to randomized
clinical trials. Thus, there is a need for development of novel statistical methodology for rapid evaluation of these agents.
Experimental Design: We propose a new adaptive design for randomized clinical trials of targeted agents in settings where an assay or signature
that identifies sensitive patients is not available at the outset of the study. The design combines prospective development
of a gene expression–based classifier to select sensitive patients with a properly powered test for overall effect.
Results: Performance of the adaptive design, relative to the more traditional design, is evaluated in a simulation study. It is shown
that when the proportion of patients sensitive to the new drug is low, the adaptive design substantially reduces the chance
of false rejection of effective new treatments. When the new treatment is broadly effective, the adaptive design has power
to detect the overall effect similar to the traditional design. Formulas are provided to determine the situations in which
the new design is advantageous.
Conclusion: Development of a gene expression–based classifier to identify the subset of sensitive patients can be prospectively incorporated
into a randomized phase III design without compromising the ability to detect an overall effect.
Background:
Restricted mean survival time methods compare the areas under the Kaplan–Meier curves up to a time
τ
for the control and experimental treatments. Extraordinary claims have been made about ...the benefits (in terms of dramatically smaller required sample sizes) when using restricted mean survival time methods as compared to proportional hazards methods for analyzing noninferiority trials, even when the true survival distributions satisfy proportional hazardss.
Methods:
Through some limited simulations and asymptotic power calculations, the authors compare the operating characteristics of restricted mean survival time and proportional hazards methods for analyzing both noninferiority and superiority trials under proportional hazardss to understand what relative power benefits there are when using restricted mean survival time methods for noninferiority testing.
Results:
In the setting of low-event rates, very large targeted noninferiority margins, and limited follow-up past
τ
, restricted mean survival time methods have more power than proportional hazards methods. For superiority testing, proportional hazards methods have more power. This is not a small-sample phenomenon but requires a low-event rate and a large noninferiority margin.
Conclusion:
Although there are special settings where restricted mean survival time methods have a power advantage over proportional hazards methods for testing noninferiority, the larger issue in these settings is defining appropriate noninferiority margins. We find the restricted mean survival time methods lacking in these regards.
We review how overall survival (OS) comparisons should be interpreted with increasing availability of effective therapies that can be given subsequently to the treatment assigned in a randomized ...clinical trial (RCT). We examine in detail how effective subsequent therapies influence OS comparisons under varying conditions in RCTs. A subsequent therapy given after tumor progression (or relapse) in an RCT that works better in the standard arm than the experimental arm will lead to a smaller OS difference (possibly no difference) than one would see if the subsequent therapy was not available. Subsequent treatments that are equally effective in the treatment arms would not be expected to affect the absolute OS benefit of the experimental treatment but will make the relative improvement in OS smaller. In trials in which control arm patients cross over to the experimental treatment after their condition worsens, a smaller OS difference could be observed than one would see without cross-overs. In particular, use of cross-over designs in the first definitive evaluation of a new agent in a given disease compromises the ability to assess clinical benefit. In disease settings in which there is not an intermediate end point that directly measures clinical benefit, OS should be the primary end point of an RCT. The observed difference in OS should be considered the measure of clinical benefit to the patients, regardless of subsequent therapies, provided that the subsequent therapies used in both treatment arms follow the current standard of care.
Efficient development of targeted therapies that may only benefit a fraction of patients requires clinical trial designs that use biomarkers to identify sensitive subpopulations. Various randomized ...phase III trial designs have been proposed for definitive evaluation of new targeted treatments and their associated biomarkers (eg, enrichment designs and biomarker-stratified designs). Before proceeding to phase III, randomized phase II trials are often used to decide whether the new therapy warrants phase III testing. In the presence of a putative biomarker, the phase II trial should also provide information as to what type of biomarker phase III trial is appropriate. A randomized phase II biomarker trial design is proposed, which, after completion, recommends the type of phase III trial to be used for the definitive testing of the therapy and the biomarker. The recommendations include the possibility of proceeding to a randomized phase III of the new therapy with or without using the biomarker and also the possibility of not testing the new therapy further. Evaluations of the proposed trial design using simulations and published data demonstrate that it works well in providing recommendations for phase III trial design.
PDF
