Design And Analysis Of Clinical Experiments

Design And Analysis Of Clinical Experiments – Research Methods and Reports Key Design Considerations for Adaptive Clinical Trials: For Primary Clinicians 2018; 360 doi: https://doi.org/10.1136/.k698 (Published: March 8, 2018) Citation: 2018;360:k698

This article reviews important aspects for researchers designing adaptive clinical trials. It differs from traditional clinical trials in that it allows and even forces key components of the study design to be continuously modified during data collection. This innovative approach reduces the use of resources, reduces the time required to complete the study, limits the allocation of participants to a lower level of intervention, and increases the likelihood that trial results will be scientifically or clinically relevant. Adaptive designs have been used mostly in drug evaluation trials, but their use is becoming more widespread. The US Food and Drug Administration recently issued guidance on adaptive trial design that highlights general principles and different types of adaptive clinical trials, but does not provide specific guidance on important aspects of designing such trials. The decision to adapt an experiment is not arbitrary; it is based on decision rules that have been thoroughly verified by statistical simulations before the first trial participants are enrolled. The authors review important features of adaptive trials and common types of trial modifications, and provide practical guidance illustrated with case studies to assist investigators in designing adaptive clinical trials.

Design And Analysis Of Clinical Experiments

Adaptive clinical trials can be completed earlier than conventional (non-adaptive) designed trials. The US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have recently issued guidance on the adaptive design of licensure.12 But little guidance exists on how investigators should approach the design and planning of adaptive clinical trials. during. We outline and discuss common features of adaptive trials, review adaptations, and provide practical design guidance for designing and interpreting adaptive clinical trials.

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Adaptive design allows key components to be modified. Unlike traditional designs, where learning usually occurs after the experiment is complete, adaptive designs aim for continuous learning through the accumulation of data. Some features are more common or unique in adaptive trials than in traditional trials (Box 1). The allocation ratio, the total sample size and the eligibility criteria can be changed, the study can be extended to the II. from phase III. per phase, and control arms can be added or removed. Adaptive trials can reduce time to completion, reduce resource requirements, and reduce the number of patients exposed to treatment and the overall likelihood of trial success. But it also comes with the risk of not properly planning for efficiency. Any potential decision to adapt should be subject to a rigorous risk-benefit assessment to ensure that the potential scientific and ethical benefits outweigh the risk of bias or trial failure.

Common types of adaptive clinical trials include sample size reassessment, 23 response-adaptive randomization and dropout of the inferior treatment arm, 4 adaptive enrichment, 5 and “plain” designs (Figure 1). Sample size reassessment uses event-based assessment during the trial to determine true performance.36 Response-adaptive randomization allows the randomization rate to be changed during the trial, so that if interim results are favorable, newly enrolled patients are more likely to be assigned to the treatment arm.4 Adaptive enrichment refers to modifying study eligibility criteria or outcome assessment; if an interim analysis shows that one of the subgroups has a more favorable response, the study can be “enriched” by modifying the eligibility criteria, whether only or mainly patients from this subgroup are included.5 Likewise, the study can be improved with clinical and biochemical results. the relevance, broad application or likelihood of success of the trial. A seamless adaptation trial design allows for progression from one phase to another, typically Phase II. from phase III. phase. The II. the results of phase III experiments can be used to determine initial allocation rates, planned total sample sizes, and potentially enriched population sets for the next phase III. phase.

Common types of adaptive testing. Sample size re-evaluation: if the interim analysis shows worse than expected results, the sample size can be re-evaluated and increased to ensure adequate performance of the trial. Response-modified randomization: if the interim analysis shows promising results for the treatment, the allocation rate can be adjusted in favor of registration of the treatment. Adaptive enrichment: if the interim analysis shows that the treatment produces more promising results in a subgroup of patients, the study’s eligibility criteria can be adjusted to examine the effectiveness of the intervention in that subgroup, provided by a reassessment of the sample size. adequate sample size. SSR = sample size reassessment

Some traditional analyzes have an intermediate analysis that usually uses predefined rules for early termination (such as non-adaptive O’Brien-Fleming observation limits).7 But all adaptive tests have an intermediate analysis that allows for design adaptation, so their design is more widespread . Investigators should consider and anticipate the challenges associated with all possible adaptation trajectories and design decision rules that minimize the risk of biased or ineffective adaptation. They should perform extensive experimental simulations on various scenarios for risk-benefit assessment.

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Adaptive design is rooted in simulation (Figure 2). They are expanded and run until investigators and trial statisticians are satisfied that the potential benefits of adaptive design far outweigh the potential risks. After creating a sufficiently robust design, they can finalize the experimental protocol and start the experiment. Performing adaptive testing typically involves an analysis cycle and intermediate results (Figure 2b). Practical case studies of adaptive clinical trials are presented in Box 2 and Figure 3.

The drug for knee pain showed an effect of approximately 10 mg in a preclinical model (Model 1) and between 30 and 90 mg in a pharmacokinetic and pharmacodynamic model based on Phase I data (Model 2; Figure 3a). The aim of this study was first to establish the superior efficacy of the 90 mg dose compared to placebo (phase IIa) and to find the median effective dose (ED50) using dose-response modeling (phase IIb).

Given the budget and time constraints of enrolling 400 patients, preliminary simulations showed that if the hypothesized model (either Model 1 or Model 2) is correct, then four treatment arms with equal distribution are optimal for ED50 and dose- to determine an answer. models. . However, incorrect assumptions about dose response can lead to ineffectiveness. Manufacturers are interested in testing 0, 10, 30, and 90 mg or 0, 30, 60, and 90 mg, depending on which model is most accurate.

Given the expected efficacy from phase I evidence (model 2), our sample size calculation showed that this could be achieved at a 20% one-sided alpha level with 40 patients in each arm. So the first interim analysis was planned after 80 patients, and a decision was made to stop the study if 90 mg showed no effect at this time.

Wp1: Data Collection From Existing Clinical Trials And Design Of New Trials Based On The Findings In This Proposal.

Phase IIb should tell which two of the remaining three doses should be used. If model 1 is the most accurate, then the increase in efficacy will occur between 0 and 30 mg, so classifying patients as 10 mg and 30 mg is optimal for estimating dose response (Figure 3b). If the increase in efficacy occurs mostly between 30 and 90 mg (model 2), the optimal allocation of patients to 30 mg and 60 mg is optimal (Figure 3c). Both models have the same 30 mg, so an additional 100 patients are randomized in the second stage needs to be classified in a ratio of 1:3:1 to obtain 0, 30, and 90 mg, respectively. The decision rule implemented at the end of this section selects 10 mg or 60 mg as the fourth arm, depending on that the 30 mg potency is less than 50% of the 90 mg potency.

At the beginning of the third phase, a total of 180 patients were randomized, of which 60 patients were randomized to all three arms (0 mg, 30 mg and 90 mg). As the study aims to randomize 100 patients to each of the last four arms, an additional 40 patients will be randomized to the existing three arms and 100 patients will be randomized to the fourth arm (10 mg or 60 mg). ‘ Therefore, in the third stage, patients were randomized 2:5:2:2 for model 1 or 2:2:5:2 for model 2 (Figure 3d). According to the initial simulation, the final analysis of 400 patients was >80% to establish the mean effective dose.

Simulation can be used for any type of test design, but is commonly used in adaptive designs due to the large number of trajectories. It is used to determine the statistical and practical properties of adaptive experimental designs. The risk of false positives (type I errors) and false negatives (type II errors) in adaptive trials is difficult (if not impossible) to assess with traditional methods.6 Regulatory agencies generally need these errors and therefore decide must , using metrics such as expected reduction in required sample size, time to completion, number of treatment failures avoided, risk of biased interim effect estimates, and reliability of planned statistical analyzes at trial completion. This can be especially useful for planning a realistic budget and schedule.

Because simulation is an iterative process

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