BOOTSTRAP PADA DATA TERSENSOR UNTUK ESTIMASI KOEFISEN ï¢ DALAM MODEL REGRESI COX - , Azimmatul Ihwah, Prof. Dr. Sri Haryatmi. K, M.Sc. ([Yogyakarta] : Universitas Gadjah Mada, 2012)
Survival regression model is used to make correlation form between survival time (dependent variable) and covariate (independent variable). Multiple linier regression is not well suited to survival data when we have censored data. Cox regression model is one of survival regression that often used in many cases which there is censored data because it does not require any asumptions about the survival time distribution. ï¢ coefficients in Cox regression model estimates with partial likelihood method. Then we use method for inference based on maximum likelihood theory that need large sample. This theory gives bad approximation when we use small sample. So we can say that the accuracy of statistic inference depend on sample size. Beside, we need to know the probability distribution of a statistiscs. Bootstrap method is the resampling method that can be used to handled two of those. Bootstrap methods can use to estimate probability distribution of a statistics with built probability distribution from bootstrap samples. Bootstrap method use to examine the accuracy of statistic by standard error of estimator. In this paper, bootstrap method is used to estimate ï¢ coefficients in Cox regression model. The algorithm of bootstrap method for Cox regression model were presented. Asymptotic properties of the estimates can be shown in some theorems. The histogram of bootstrap estimator were presented to show that the ditribution of the bootstrap estimates limiting to the normal distribution. Based on the properties of estimator, statistical inference can be derived and the one of the result is confidence interval. Numerical simulation with R is used to show how bootstrap method work in censored data.