临床试验不能为研究慢性疾病治疗所需的弱因子提供足够的准确性

Wu Jianqing, Zhao Ping
{"title":"临床试验不能为研究慢性疾病治疗所需的弱因子提供足够的准确性","authors":"Wu Jianqing, Zhao Ping","doi":"10.17352/2581-5407.000044","DOIUrl":null,"url":null,"abstract":"Chronic diseases are still known as incurable diseases, and we suspect that the medical research model is unfit for characterizing chronic diseases. In this study, we examined accuracy and reliability required for characterizing chronic diseases, reviewed implied presumptions in clinical trials and assumptions used in statistical analysis, examined sources of variances normally encountered in clinical trials, and conducted numeric simulations by using hypothetical data for several theoretical and hypothetical models. We found that the sources of variances attributable to personal differences in clinical trials can distort hypothesis test outcomes, that clinical trials introduce too many errors and too many inaccuracies that tend to hide weak and slow-delivering effects of treatments, and that the means of treatments used in statistical analysis have little or no relevance to specific patients. We further found that a large number of uncontrolled co-causal or interfering factors normally seen in human beings can greatly enlarge the means and the variances or experimental errors, and the use of high rejection criteria (e.g., small p values) further raises the chances of failing to find treatment effects. As a whole, we concluded that the research model using clinical trials is wrong on multiple grounds under any of our realistic theoretical and hypothetical models, and that misuse of statistical analysis is most probably responsible for failure to identify treatment effects for chronic diseases and failure to detect harmful effects of toxic substances in the environment. We proposed alternative experimental models involving the use of single-person or mini optimization trials for studying low-risk weak treatments.","PeriodicalId":73166,"journal":{"name":"Global journal of cancer therapy","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical trials cannot provide sufficient accuracy for studying weak factors necessary for curing chronic diseases\",\"authors\":\"Wu Jianqing, Zhao Ping\",\"doi\":\"10.17352/2581-5407.000044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Chronic diseases are still known as incurable diseases, and we suspect that the medical research model is unfit for characterizing chronic diseases. In this study, we examined accuracy and reliability required for characterizing chronic diseases, reviewed implied presumptions in clinical trials and assumptions used in statistical analysis, examined sources of variances normally encountered in clinical trials, and conducted numeric simulations by using hypothetical data for several theoretical and hypothetical models. We found that the sources of variances attributable to personal differences in clinical trials can distort hypothesis test outcomes, that clinical trials introduce too many errors and too many inaccuracies that tend to hide weak and slow-delivering effects of treatments, and that the means of treatments used in statistical analysis have little or no relevance to specific patients. We further found that a large number of uncontrolled co-causal or interfering factors normally seen in human beings can greatly enlarge the means and the variances or experimental errors, and the use of high rejection criteria (e.g., small p values) further raises the chances of failing to find treatment effects. As a whole, we concluded that the research model using clinical trials is wrong on multiple grounds under any of our realistic theoretical and hypothetical models, and that misuse of statistical analysis is most probably responsible for failure to identify treatment effects for chronic diseases and failure to detect harmful effects of toxic substances in the environment. We proposed alternative experimental models involving the use of single-person or mini optimization trials for studying low-risk weak treatments.\",\"PeriodicalId\":73166,\"journal\":{\"name\":\"Global journal of cancer therapy\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global journal of cancer therapy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17352/2581-5407.000044\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global journal of cancer therapy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17352/2581-5407.000044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

慢性病仍然被认为是不治之症,我们怀疑医学研究模式不适合表征慢性病。在本研究中,我们检查了表征慢性疾病所需的准确性和可靠性,回顾了临床试验中的隐含假设和统计分析中使用的假设,检查了临床试验中通常遇到的方差来源,并通过使用几种理论和假设模型的假设数据进行了数值模拟。我们发现,临床试验中可归因于个人差异的差异来源可能会扭曲假设检验结果,临床试验引入了太多的错误和不准确,这些错误和不准确往往会掩盖治疗的微弱和缓慢递送效果,并且统计分析中使用的治疗手段与特定患者的相关性很小或没有相关性。我们进一步发现,通常在人类中看到的大量不受控制的共同因果或干扰因素可以极大地扩大平均值和方差或实验误差,并且使用高排斥标准(例如,小p值)进一步增加了未能发现治疗效果的机会。总的来说,我们得出的结论是,使用临床试验的研究模型在我们任何现实的理论和假设模型下都是错误的,而且统计分析的滥用很可能是未能确定慢性病的治疗效果和未能检测到环境中有毒物质的有害影响的原因。我们提出了其他实验模型,包括使用单人试验或小型优化试验来研究低风险弱治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clinical trials cannot provide sufficient accuracy for studying weak factors necessary for curing chronic diseases
Chronic diseases are still known as incurable diseases, and we suspect that the medical research model is unfit for characterizing chronic diseases. In this study, we examined accuracy and reliability required for characterizing chronic diseases, reviewed implied presumptions in clinical trials and assumptions used in statistical analysis, examined sources of variances normally encountered in clinical trials, and conducted numeric simulations by using hypothetical data for several theoretical and hypothetical models. We found that the sources of variances attributable to personal differences in clinical trials can distort hypothesis test outcomes, that clinical trials introduce too many errors and too many inaccuracies that tend to hide weak and slow-delivering effects of treatments, and that the means of treatments used in statistical analysis have little or no relevance to specific patients. We further found that a large number of uncontrolled co-causal or interfering factors normally seen in human beings can greatly enlarge the means and the variances or experimental errors, and the use of high rejection criteria (e.g., small p values) further raises the chances of failing to find treatment effects. As a whole, we concluded that the research model using clinical trials is wrong on multiple grounds under any of our realistic theoretical and hypothetical models, and that misuse of statistical analysis is most probably responsible for failure to identify treatment effects for chronic diseases and failure to detect harmful effects of toxic substances in the environment. We proposed alternative experimental models involving the use of single-person or mini optimization trials for studying low-risk weak treatments.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信