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Register a new userA Simulation Study Evaluating Phase I Clinical Trial Designs for Combinational Agents
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Shu Wang
Publication date
2021
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Mathematical Statistics
and research's language is
English
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Nowadays, more and more clinical trials choose combinational agents as the intervention to achieve better therapeutic responses. However, dose-finding for combinational agents is much more complicated than single agent as the full order of combination dose toxicity is unknown. Therefore, regular phase I designs are not able to identify the maximum tolerated dose (MTD) of combinational agents. Motivated by such needs, plenty of novel phase I clinical trial designs for combinational agents were proposed. With so many available designs, research that compare their performances, explore parameters impacts, and provide recommendations is very limited. Therefore, we conducted a simulation study to evaluate multiple phase I designs that proposed to identify single MTD for combinational agents under various scenarios. We also explored influences of different design parameters. In the end, we summarized the pros and cons of each design, and provided a general guideline in design selection.
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Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory Phase III studies.
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Some years ago, Snapinn and Jiang[1] considered the interpretation and pitfalls of absolute versus relative treatment effect measures in analyses of time-to-event outcomes. Through specific examples and analytical considerations based solely on the exponential and the Weibull distributions they reach two conclusions: 1) that the commonly used criteria for clinical effectiveness, the ARR (Absolute Risk Reduction) and the median (survival time) difference (MD) directly contradict each other and 2) cost-effectiveness depends only the hazard ratio(HR) and the shape parameter (in the Weibull case) but not the overall baseline risk of the population. Though provocative, the first conclusion does not apply to either the two special cases considered or even more generally, while the second conclusion is strictly correct only for the exponential case. Therefore, the implication inferred by the authors i.e. all measures of absolute treatment effect are of little value compared with the relative measure of the hazard ratio, is not of general validity and hence both absolute and relative measures should continue to be used when appraising clinical evidence.
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