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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Effect Model law ： ウィキペディア英語版 Effect Model law The Effect Model law states that a natural relationship exists for each individual between the frequency (observation) or the probability (prediction) of a morbid event without any treatment $\backslash scriptstyle$ and the frequency or probability of the same event with a treatment $\backslash scriptstyle$. This relationship applies to a single individual, individuals within a population, or groups. This law enables the prediction of the (absolute) benefit ($\backslash scriptstyle$) of a treatment for a given patient. It has wide-reaching implications in R&D for new pharmaceutical products as well as personalized medicine. The law was serendipitously discovered in the 1990s by Jean-Pierre Boissel. While studying the effectiveness of class-I antiarrhythmic drugs in the prevention of death after myocardial infarction,〔Boissel, J. P.; Collet, J. P.; Lievre, M.; Girard, P. An effect model for the assessment of drug benefit: example of antiarrhythmic drugs in postmyocardial infarction patients. J. Cardiovasc. Pharmacol. 1993, 22, 356–363.〕 he stumbled upon a situation which contradicts one of the basic premises of meta-analysis theory, i.e. that the heterogeneity test was significant at the same time for the assumption “the relative risk ($\backslash scriptstyle$) is a constant” and “$\backslash scriptstyle$ is a constant”. Boissel formulated the hypothesis that the antiarrhythmic drugs efficacy was a function combining a beneficial effect ($\backslash scriptstyle$) that is proportional to $\backslash scriptstyle$ and a constant adverse effect ($\backslash scriptstyle$), independent of $\backslash scriptstyle$. The mathematical expression of this model is a linear equation with two parameters, the risk of lethal adverse event caused by treatment and the slope of the line which represents the true beneficial risk reduction. This equation gives the treatment net mortality reduction:〔Boissel, J. P. Individualizing aspirin therapy for prevention of cardiovascular events. JAMA 1998, 280, 1949–1950.〕〔Li, W.; Gueyffier, F.; Boissel, J. P.; Girard, P.; Boutitie, F.; Cucherat, M. (and prediction of responders to a therapy. A model and its preliminary application to hypertension ). Arch Mal Coeur Vaiss 1998, 91, 1059–1063.〕〔Boissel, J. P.; Cucherat, M.; Nony, P.; Chabaud, S.; Gueyffier, F.; Wright, J. M.; Lièvre, M.; Leizorovicz, A. New insights on the relation between untreated and treated outcomes for a given therapy effect model is not necessarily linear. Journal of Clinical Epidemiology 2008, 61, 301–307.〕〔Boissel, J.-P.; Kahoul, R.; Amsallem, E.; Gueyffier, F.; Haugh, M.; Boissel, F.-H. Towards personalized medicine: exploring the consequences of the effect model-based approach. Personalized Medicine 2011, 8, 581–586.〕 $\backslash scriptstyle$ == Illustration in the (Rc,Rt) plane == In 1987, L'Abbe, Detsky and O'Rourke recommended including a graphical representation of the various trials while designing a meta-analysis. For each trial, on the x-axis the frequency (risk) of the studied criterion in the control group $\backslash scriptstyle$ should be represented, and on the y-axis, the risk in the treated group $\backslash scriptstyle$ 〔L’Abbe, K. A.; Detsky, A. S.; O’Rourke, K. Meta-analysis in clinical research. Annals of Internal Medicine 1987, 107, 224–233.〕 (Figure 1 and Figure 2). The shape of the resulting scatter plot illustrates some important aspects of the information concerning the effect of the treatment: * On an individual basis, the ability to measure and predict the absolute benefit of a treatment for a patient characterized by his or her idiosyncratic risk parameters (e.g., cholesterol level, systolic blood pressure, etc.); * Over a given population (e.g., French, Chinese, etc.), the ability to measure and predict health outcomes for a treatment available on the market or a drug candidate at any stage in the R&D process (from the target identification phase to clinical trials). The law is expressed in two ways. # the $\backslash scriptstyle$ function: $\backslash scriptstyle$ or $\backslash scriptstyle$, equation in which $\backslash scriptstyle$ is implicit. # the absolute benefit function $\backslash scriptstyle$ : $\backslash scriptstyle$, equation in which $\backslash scriptstyle$ and $\backslash scriptstyle$ are implicit. The forms above lead to as many values in the $\backslash scriptstyle$ plane as there are patients, each one being represented by a dot which is more or less close to the neutrality frontier. The expression of the absolute benefit $\backslash scriptstyle$ (i.e. the vertical distance to the neutrality frontier) has the advantage of leading directly to an individual prediction, making personalized medicine a practical reality. The $\backslash scriptstyle$ and $\backslash scriptstyle$ are, respectively, patients descriptors linked with $\backslash scriptstyle$ and with the treatment$\backslash scriptstyle$patient interactions. By summing up $\backslash scriptstyle=Rt\_-Rc\_\}$ This illustrates an intuition that all doctors have, and that Kaurer and Kassirer emphasized in 1980: a treatment can yield little benefit; even worse, it can be more harmful than beneficial for "moderately sick" patients.〔Pauker, S. G.; Kassirer, J. P. The threshold approach to clinical decision making. N. Engl. J. Med. 1980, 302, 1109–1117.〕 In the case where the Effect Model is curvilinear, as shown in Figure 2, it is easy to understand intuitively that: * Low-risk patients (e.g. $\backslash scriptstyle$) don’t benefit from the treatment as much as higher-risk patients; * High-risk patients (e.g. $\backslash scriptstyle$) are less likely to avoid the clinical event, irrespective of the treatment. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Effect Model law」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.