How we do it

We apply the in silico trials standard methodology with the most robust and versatile technology platform in our industry

#EffectModel #diseasemodels #virtualpatients #WISE #GitHealth #SimWork #Haskell

An effect model for the assessment of drug benefit: example of antiarrhythmic drugs in post-myocardial infarction patients

Boissel JP, Collet JP, Lièvre M, Girard P,
J Cardiovasc Pharmacol 1993; 22: 356-33

Abstract

An effect model is a function that defines the relationship between the clinical efficacy of a treatment and specific covariates. The simplest effect model defines the probability of failure in treated patients as a linear function of the probability for these patients if they received no treatment. We used this approach to explore the effects of Class I antiarrhythmic agents in patients after myocardial infarction. Evidence from one large trial, the Cardiac Arrhythmic Suppression Trial (CAST), and the pooling of data from several smaller trials suggests that these agents have harmful effects in postmyocardial infarction patients. The relevance of results from pooled data is dependent on the homogeneity of the trials and is assessed by a heterogeneity test that is dependent on the analytical method used, i.e., odds ratio or rate difference methods, which correspond to two different effect models. We have developed an effect model that considers both iatrogenic effects of these drugs, i.e., depression of ventricular function and arrhythmogenic effects. When applied to the data from 13 published trials (including CAST), we found that these drugs may be beneficial in high-risk patients (with a 1-year mortality rate of > or = 15%) and that the background lethal iatrogenic effect is likely to affect low- and very low-risk patients (1-year mortality rate of < or = 5%). The accuracy of the proposed model was confirmed with use of the results from the recent CAST II study.