Adaptive Sample Size Adjustment Research Articles

Practical characteristics of adaptive design in phase 2 and 3 clinical trials.

Adaptive design methods are expected to be ethical, reflect real medical practice, increase the likelihood of research and development success and reduce the allocation of patients into ineffective treatment groups by the early termination of clinical trials. However, the comprehensive details regarding which types of clinical trials will include adaptive designs remain unclear. We examined the practical characteristics of adaptive design used in clinical trials. We conducted a literature search of adaptive design clinical trials published from 2012 to 2015 using PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials, with common search terms related to adaptive design. We systematically assessed the types and characteristics of adaptive designs and disease areas employed in the adaptive design trials. Our survey identified 245 adaptive design clinical trials. The number of trials by the publication year increased from 2012 to 2013 and did not greatly change afterwards. The most frequently used adaptive design was group sequential design (n=222, 90.6%), especially for neoplasm or cardiovascular disease trials. Among the other types of adaptive design, adaptive dose/treatment group selection (n=21, 8.6%) and adaptive sample-size adjustment (n=19, 7.8%) were frequently used. The adaptive randomization (n=8, 3.3%) and adaptive seamless design (n=6, 2.4%) were less frequent. Adaptive dose/treatment group selection and adaptive sample-size adjustment were frequently used (up to 23%) in "certain infectious and parasitic diseases," "diseases of nervous system," and "mental and behavioural disorders" in comparison with "neoplasms" (<6.6%). For "mental and behavioural disorders," adaptive randomization was used in two trials of eight trials in total (25%). Group sequential design and adaptive sample-size adjustment were used frequently in phase 3 trials or in trials where study phase was not specified, whereas the other types of adaptive designs were used more in phase 2 trials. Approximately 82% (202 of 245 trials) resulted in early termination at the interim analysis. Among the 202 trials, 132 (54% of 245 trials) had fewer randomized patients than initially planned. This result supports the motive to use adaptive design to make study durations shorter and include a smaller number of subjects. We found that adaptive designs have been applied to clinical trials in various therapeutic areas and interventions. The applications were frequently reported in neoplasm or cardiovascular clinical trials. The adaptive dose/treatment group selection and sample-size adjustment are increasingly common, and these adaptations generally follow the Food and Drug Administration's (FDA's) recommendations.

A Bayesian adaptive blinded sample size adjustment method for risk differences.

Adaptive sample size adjustment (SSA) for clinical trials consists of examining early subsets of on trial data to adjust estimates of sample size requirements. Blinded SSA is often preferred over unblinded SSA because it obviates many logistical complications of the latter and generally introduces less bias. On the other hand, current blinded SSA methods for binary data offer little to no new information about the treatment effect, ignore uncertainties associated with the population treatment proportions, and/or depend on enhanced randomization schemes that risk partial unblinding. I propose an innovative blinded SSA method for use when the primary analysis is a non-inferiority or superiority test regarding a risk difference. The method incorporates evidence about the treatment effect via the likelihood function of a mixture distribution. I compare the new method with an established one and with the fixed sample size study design, in terms of maximization of an expected utility function. The new method maximizes the expected utility better than do the comparators, under a range of assumptions. I illustrate the use of the proposed method with an example that incorporates a Bayesian hierarchical model. Lastly, I suggest topics for future study regarding the proposed methods.

Clinical Evaluation of a Neuroprotective Drug in Patients With Cervical Spondylotic Myelopathy Undergoing Surgical Treatment

Descriptive article and narrative review. To explain the rationale and design of the cervical spondylotic myelopathy (CSM)-Protect clinical trial that aims to elucidate the efficacy and safety of riluzole in the context of CSM. CSM is the most common cause of spinal cord-related dysfunction internationally. Although surgery is effective in preventing the progression of impairment, and in some cases improving functional outcomes, many patients continue to exhibit significant disability in the postoperative setting. Evidence from preclinical studies suggests that glutamate-related excitotoxicity may contribute to the pathology of CSM and that administration of the sodium and glutamate-blocking medication riluzole, when combined with spinal cord decompression, may mitigate this effect and improve neurobehavioral outcomes. Although riluzole is FDA approved and has been shown to be safe and effective in the context of amyotrophic lateral sclerosis, its efficacy and safety in the context of CSM remain unknown. Descriptive article with narrative review of the literature. In addition to providing pertinent preclinical background on the topic, this descriptive article and narrative review discusses the design and current status of an ongoing phase III randomized controlled trial evaluating the efficacy and safety of riluzole, combined with surgical decompression, in the treatment of CSM. On the basis of current projections, we estimate that the interim analysis for this study will take place in the spring of 2014, at which time an adaptive sample size adjustment may take place.

Performance of adaptive sample size adjustment with respect to stopping criteria and time of interim analysis

The benefit of adjusting the sample size in clinical trials on the basis of treatment effects observed in interim analysis has been the subject of several recent papers. Different conclusions were drawn about the usefulness of this approach for gaining power or saving sample size, because of differences in trial design and setting. We examined the benefit of sample size adjustment in relation to trial design parameters such as 'time of interim analysis' and 'choice of stopping criteria'. We compared the adaptive weighted inverse normal method with classical group sequential methods for the most common and for optimal stopping criteria in early, half-time and late interim analyses. We found that reacting to interim data might significantly reduce average sample size in some situations, while classical approaches can out-perform the adaptive designs under other circumstances. We characterized these situations with respect to time of interim analysis and choice of stopping criteria.

A comparison of methods for adaptive sample size adjustment

In fixed sample size designs, precise knowledge about the magnitude of the outcome variable's variance in the planning phase of a clinical trial is mandatory for an adequate sample size determination. Wittes and Brittain introduced the internal pilot study design that allows recalculation of the sample size during an ongoing trial using the estimated variance obtained from an interim analysis. However, this procedure requires the unblinding of the treatment code. Since unblinding of an ongoing trial should be avoided whenever possible, there should be some benefit of this design compared with blinded sample size recalculation procedures to justify the unveiling of the treatment code. In this paper, we compare several sample size recalculation procedures with and without unblinding. The simulation results indicate that the procedures behave similarly. In particular, breaking of the blind is not required for an efficient sample size adjustment. We also compare these pure sample size adaptation procedures with study designs which additionally allow for early stopping. Evaluation of the cumulative distribution function of the resulting sample sizes shows that the option for early stopping may lead to lower expectation but generally to a higher variability. The procedures are illustrated by an example of a trial in the treatment of depression.