Medicine has failed to find cures for chronic diseases, but nearly all diseases can spontaneously resolve in reality. The riddle between lack of cures for chronic diseases and disease spontaneous resolution is known for centuries. We suspect that lack of cures and inability to understand disease spontaneous resolution is due to the inherent limitations in the medical research model. We show that chronic diseases are reflection of disturbed metabolic networks, and accuracy and reliability of the medical research model are insufficient for characterizing chronic diseases. We examined implied presumptions in the research model and assumptions used in clinical trials and statistical models, and conducted numerical simulations by using hypothetical model data. We found that clinical trials and statistical analysis introduce too many errors and too bigger inaccuracies that tend to conceal weak and slow-delivering effects of treatments and thus could reject lifestyle factors that would have power to cure chronic diseases. We further found that a large number of prevalent uncontrolled co-causal or interfering factors 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. We concluded that the research model is biased towards rejecting weak lifestyle factors. We believe that misusing statistical models to characterize human metabolic networks is most probably responsible for failure to identify weak treatment effects for chronic diseases and failure to detect harmful effects of toxic substances on humans. We proposed alternative experimental models involving the use of single-person or mini optimization trials for studying weak and slow-delivering treatments.
Author(s): Jianqing Wu, and Ping Zha