Randomized Clinical Trial Is Biased and Invalid In Studying Chronic Diseases, Compared with Multiple Factors Optimization Trial

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 much inaccuracies that tend to hide weak and slow 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 subjects can greatly enlarge the means and the variances of the experimental errors, and the use of high rejection criteria (e.g., low 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 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. Conclusions: The total gain in treatment effects existing in an optimization trial over a clinical trial is (1/g)*k while all test statistics such as T statistic, Z statistic, and F statistic used in hypothesis tests are increased by (1/g)*k*√k, where 1/g is attributed to avoiding negating effects, k is attributed to the additive effect of multiple treatment factors, and √k is attributed to reduction in the error variances. Their collective impacts could be huge. This conclusively shows why medicine could not find “scientific evidence” for any treatment based on a single lifestyle factor.