Use of Single Index Model for estimation of survival of the patients with acute myocardial infarction

The Cox proportional hazard model is the standard approach for analyzing survival data in many cases. One restriction of this method، however، is that it assumes that the log of the hazard function relates to the covariates through a linear function. As a consequence، it fails to estimate efficiently the effect of the non-linear terms. But we can estimate the survival rate by using spline functions. Our goal is to investigate the appropriateness of an alternative method، the so-called single index model، for estimation of the survival rate of the patients with acute myocardial infarction. This was a descriptive analytical cohort study which included 650 subjects with acute myocardial infarction. The patients were followed up for one year to ensure survival or detect death events. Data were recorded in a pre-defined check list. In this study the relationship between the log of the hazard function and covariates were considered unknown. We estimated the coefficients of the model by using the polynomial spline and penalized partial likelihood. Data analysis was carried out by using R version 2. 12 software and significant levels were considered 0. 05. We found the Cox model with unknown link function to have larger log likelihood than the standard Cox model. The effects of estimated parameters in both models were relatively different. Effects of diabetes and arrhythmia in Cox model with unknown link function were significant (P<0. 05). In standard Cox model unlike Cox model with unknown link function the age was significant (P<0. 05). Considering the results of this study Cox model with unknown link function could estimate the effect of factors such as diabetes and arrhythmia in the survival of the patients، in addition to the effects of streptokinase and ejection fraction.