Subjects: Medicine, Pharmacy >> Clinical Medicine Subjects: Management Science >> Science ology and Management submitted time 2023-12-09
Abstract： A comparison of pivotal studies for high-risk medical devices between China and the US including 40 approved products and some undergoing studies, covering coronary intervention, structural heart disease, left ventricular assistant device, neuromodulation, and electrophysiology, showed that pivotal studies in China were relatively simple-designed, with more quantitative endpoints, and relatively small sample size. Direct comparison of pivotal studies between different class medical devices is difficult, but achieving consensus between sponsors and regulation administration agency before conducting of the pivotal studies should be our future direction.
Peer Review Status:
Awaiting Review
Subjects: Medicine, Pharmacy >> Clinical Medicine Subjects: Management Science >> Other Disciplines of Management Science submitted time 2023-12-09
Abstract： We collected summaries/review reports of fractional flow reserve (FFR) and related products approved in the US and China, and together with relevant clinical literature and clinical guidelines, analyzed the registration administration strategy for such medical device software, especially clinical data requirement. In general, the US is more flexible regarding clinical data request, which was not even submitted for early FFR products when clinical benefits were not claimed. Clinical data was submitted for the following products during equivalence demonstration; however, mainly being diagnostic consistence comparison study with predicate device instead of strict prospective clinical trials. When the clinical benefits were confirmed in clinical trials with clinical outcome as the primary endpoint, this claim was added to product indications. Besides, the practice of efficiently leveraging social resources via data sharing in the US is worth learning.
Peer Review Status:Awaiting Review
Subjects: Medicine, Pharmacy >> Preclinical Medicine Subjects: Mathematics >> Statistics and Probability submitted time 2023-04-19
Abstract： Objective To investigate the current status of data transformation in Meta analysis of single proportions. Methods A literature search in PubMed was performed to retrieve researches of Meta analysis of single proportions published in 2017, and 481 records were returned. Results In 123 researches with full texts, only 33 (26.8%) described data transformation for proportions. Among which, double arcsine transformation was used 20 times, logit transformation 8, squared arcsine transformation 3, log transformation 1 and raw proportion 1. There was no relation between pooled portions and data transformation method (P=0.217). Conclusion Data transformation is important in Meta analysis of single proportions, however, it is yet to determined which transformation method is best. So, data transformation should be clarified in published papers.
Peer Review Status:Awaiting Review
Subjects: Medicine, Pharmacy >> Preclinical Medicine Subjects: Mathematics >> Statistics and Probability submitted time 2023-04-19
Abstract： Objective To compare different data transformations in Meta analysis for single proportions. Methods Two simulation data were constructed for Meta analysis under fixed effect model and random effect model, different adding values when event number was zero, and five different data transformation methods (raw proportion, log transformation, logit transformation, arcsine transformation and double arcsine transformation). Mean of pooled portion, bias, proportion bias, mean squared error, proportion mean squared error, proportion mean squared error and 95% confidence coverage were calculated. Results For Meta analysis of single proportions based on binomial distribution, generally, arcsine transformation performed best. When event number was zero, different values added to it did not improve the result much. Bias of pooled proportion was rather big when the population proportion is below 0.05. Conclusion Arcsine data transformation performed best during the simulation study for Meta analysis of single proportions. Caution should be used when dealing with population proportion less than 0.05.
Peer Review Status:Awaiting Review
Subjects: Medicine, Pharmacy >> Preclinical Medicine Subjects: Mathematics >> Statistics and Probability submitted time 2023-04-19
Abstract： The common method of sample size calculation for single proportion comparison (performance goal) is normal asymptotic approach, sometimes with corresponding data transformation such as squared arcsine, while exact probability usually needs commercial statistics software or programming. We use the free software R to calculate the sample size for single proportion via exact probability, and considering of the non-monotone increasing relationship between power and sample size with exact probability, we provide intuitive figure demonstration besides giving direct calculation results. We hope this will facilitate study design with performance goal.
Peer Review Status:Awaiting Review
Subjects: Medicine, Pharmacy >> Clinical Medicine Subjects: Statistics >> Biomedical Statistics submitted time 2022-11-21
Abstract： Sample size determination for clinical trials is one of the key components of study design. Based on medical device registration review recently published by National Medical Products Administration, Center for Medical Device Evaluation, and other public information, we conducted an analysis of the sample size for medical device registration clinical trials, including study design, part of which being compared with that in the US. Our results showed that the median sample size for Class III medical device registration trials is 120 (IQR 90~167.5). Sample size was influenced significantly by regulation policies, and some differed significantly from that in the US. Disclose of registration review is a giant leap for medical device regulation in China; however, the disclosed information needs to be further improved.
Peer Review Status:Awaiting Review
Subjects: Statistics >> Biomedical Statistics submitted time 2022-05-02
Abstract：
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Peer Review Status:Awaiting Review
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