The effect model law states that a natural relationship exists between the frequency (observation) or the probability (prediction) of a morbid event without any treatment and the frequency or probability of the same event with a treatment. This relationship is called the effect model. It applies to a single individual, individuals within a population, or groups. In the latter case, frequencies or probabilities are averages of the group. The relationship is specific to a therapy, a disease or an event, and a period of observation. If one single disease is expressed through several distinct events, a treatment will be characterized by as many effect models. Empirical evidence, simulations with models of diseases and therapies and virtual populations, as well as theoretical derivation support the existence of the law. The effect model could be estimated through statistical fitting or mathematical modelling. It enables the prediction of the (absolute) benefit of a treatment for a given patient. It thus constitutes the theoretical basis for the design of practical tools for personalized medicine.
References
[1]
L'Abbe, K.A.; Detsky, A.S.; O'Rourke, K. Meta-analysis in clinical research. Ann. Intern. Med. 1987, 107, 224–233, doi:10.7326/0003-4819-107-2-224.
[2]
Lubsen, J.; Tijssen, J.G. Large trials with simple protocols: Indications and contraindications. Control Clin. Trials. 1989, 10, 151S–160S, doi:10.1016/0197-2456(89)90054-8.
[3]
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, doi:10.1097/00005344-199309000-00003.
[4]
Pauker, S.G.; Kassirer, J.P. The threshold approach to clinical decision making. N. Engl. J. Med. 1980, 302, 1109–1117, doi:10.1056/NEJM198005153022003.
[5]
Boissel, J.P. Individualizing aspirin therapy for prevention of cardiovascular events. JAMA 1998, 280, 1949–1950, doi:10.1001/jama.280.22.1949.
[6]
Novadiscovery Homepage. Available online: http://www.novadiscovery.com (accessed on 12 August 2013).
[7]
Glasziou, P.P.; Irwig, L.M. An evidence based approach to individualising treatment. Br. Med. J. 1995, 311, 1356–1359, doi:10.1136/bmj.311.7016.1356.
[8]
Cucherat, M.; Boissel, J.P. A mathematical model for the determination of the optimum value of the treatment threshold for a continuous risk factor. Eur. J. Epidemiol. 1998, 14, 23–29, doi:10.1023/A:1007423730270.
[9]
Li, W.; Girard, P.; Boissel, J.P.; Gueyffier, F. The calculation of a confidence interval on the absolute estimated benefit for an individual patient. Comput. Biomed. Res. 1998, 31, 244–256, doi:10.1006/cbmr.1998.1477.
[10]
Boissel, J.P.; Cucherat, M.; Nony, P.; Chabaud, S.; Gueyffier, F.; Wright, J.M.; Lievre, M.; Leizorovicz, A. New insights on the relation between untreated and treated outcomes for a given therapy effect model is not necessarily linear. J. Clin. Epidemiol. 2008, 61, 301–307, doi:10.1016/j.jclinepi.2007.07.007.
[11]
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. Pers. Med. 2011, 8, 581–586, doi:10.2217/pme.11.54.
[12]
Wang, H.; Boissel, J.P.; Nony, P. Revisiting the relationship between baseline risk and risk under treatment. Emerg. Themes Epidemiol. 2009, 6, e1, doi:10.1186/1742-7622-6-1.
[13]
Boutitie, F.; Gueyffier, F.; Pocock, S.J.; Boissel, J.P. Assessing treatment-time interaction in clinical trials with time to event data: A meta-analysis of hypertension trials. Stat. Med. 1998, 17, 2883–2903, doi:10.1002/(SICI)1097-0258(19981230)17:24<2883::AID-SIM900>3.0.CO;2-L.
[14]
Kassai, B.; Gueyffier, F.; Boissel, J.P.; Boutitie, F.; Cucherat, M. Absolute benefit, number needed to treat and gain in life expectancy: Which efficacy indices for measuring the treatment benefit? J. Clin. Epidemiol. 2003, 56, 977–982, doi:10.1016/S0895-4356(03)00159-8.
[15]
Marchant, I.; Boissel, J.P.; Kassai, B.; Bejan, T.; Massol, J.; Vidal, C.; Amsallem, E.; Naudin, F.; Galan, P.; Czernichow, S.; et al. SCORE should be preferred to Framingham to predict cardiovascular death in French population. Eur. J. Cardiovasc. Prev. Rehabil. 2009, 16, 609–615, doi:10.1097/HJR.0b013e32832da006.
[16]
Kahoul, R.; Gueyffier, F.; Amsallem, E.; Haugh, M.; Marchant, I.; Boissel, F.H.; Boissel, J.P. Comparison of an effect-model-law-based method versus traditional clinical practice guidelines for optimal treatment decision-making: Application to statin treatment in the French population. BMC Med. Inform. Decis. Mak. 2013. submitted.
[17]
Chabaud, S.; Girard, P.; Nony, P.; Boissel, J.P.; MOdeling, H.E.; Simulation, G. Clinical trial simulation using therapeutic effect modeling: Application to ivabradine efficacy in patients with angina pectoris. J. Pharmacokinet. Pharmacodyn. 2002, 29, 339–363, doi:10.1023/A:1020953107162.
[18]
Sharp, S.J.; Thompson, S.G.; Altman, D.G. The relation between treatment benefit and underlying risk in meta-analysis. Br. Med. J. 1996, 313, 735–738, doi:10.1136/bmj.313.7059.735.
[19]
Dronne, M.A.; Grenier, E.; Chapuisat, G.; Hommel, M.; Boissel, J.P. A modelling approach to explore some hypotheses of the failure of neuroprotective trials in ischemic stroke patients. Prog. Biophys. Mol. Biol. 2008, 97, 60–78, doi:10.1016/j.pbiomolbio.2007.10.001.
[20]
Eckermann, S.; Coory, M.; Willan, A.R. Indirect comparison: Relative risk fallacies and odds solution. J. Clin. Epidemiol. 2009, 62, 1031–1036, doi:10.1016/j.jclinepi.2008.10.013.
[21]
Boland, M.V.; Lehmann, H.P. A new method for determining physician decision thresholds using empiric, uncertain recommendations. BMC Med. Inform. Decis. Mak. 2010, 10, e20, doi:10.1186/1472-6947-10-20.
[22]
Levels of Evidence: Applicability of Evidence in the Context of a Relative Effectiveness Assessment of Pharmaceuticals; EUnetHTA: Copenhagen, Denmark, 2013.
[23]
Varadhan, R.; Stuart, E.A.; Louis, T.A.; Segal, J.B.; Weiss, C.O. Review of Guidance Documents for Selected Methods in Patient Centered Outcomes Research: Standards in Addressing Heterogeneity of Treatment Effectiveness in Observational and Experimental Patient Centered Outcomes Research; Patient-Centered Outcomes Research Institute: Washington, DC, USA, 2012.
