Cardiac complications after surgery are common irrespective of the underlying condition. The postoperative level of troponin T is a good marker for cardiac complications. Little is known on the pathology of the release of troponin T in the blood, while a better understanding might provide the ability to reduce the complications. The goal of the thesis is to find patterns in intraoperative data that are related to the release of troponin T in the blood during surgery. The states resulting from estimating an MSVAR on intraoperative hemodynamic data were interpreted and related to postoperative troponin T measurements. The MSVAR was estimated in two ways: with the EM algorithm and in Bayesian fashion with the Gibbs sampler. Prior distributions were chosen and a Gibbs sampler was developed for estimating the MSVAR with these priors. The differences between the EM algorithm and the Gibbs sampler are mostly fundamental and not practical. Furthermore, the MSVAR is an appropriate model for modelling intraoperative hemodynamic data. The states of the MSVAR were related to various surgery variables, but did not have any prognostic value for predicting postoperative troponin T. The states related to the external shocks continuously given to the patient during surgery rather than the patient's state.

The Morris method is a widely used screening method in sensitivity analysis. The method assumes that the input parameters are independent of each other. To overcome the assumption a copula-based Morris method is proposed. In this report the results of taking the dependencies into account are analyzed for the Morris method. For two examples sensitivity analysis is performed with the Morris method, with copula-based Morris method and by calculating sample correlations with a Monte Carlo simulation. From the analysis it follows that taking dependencies into account can have varying effects for different methods. It turns out that a straight-forward implementation makes the method often practically unusable. The sampling of model evaluation points becomes too computer expensive. The amount of copula evaluations is growing exponentially with the dimension and for copulas without an analytic expression these are already lengthy. The computational intensity can be reduced in two ways. First, one can approximate the probabilities. Different ways of approximating the probabilities are researched. Numerically integrating with the midpoint rule seems to be the best way of approximating the probabilities in the copula-based Morris method. Next to approximating the probabilities, one can also use the independent groups when implementing the method. When the input parameters are correlated there are usually a few groups of correlated parameters rather than that all the parameters are correlated with each other. This can be utilized to more efficiently implement the copula-based Morris method. When the group sizes are not increasing the computational intensity depends linearly instead of exponentially on the number of model parameters. By using both improvements the method can generally be applied to tens or hundreds of parameters in reasonable time, which is desired for a screening method.