Περίληψη σε άλλη γλώσσα
Classic statistical analysis methods examine whether observed relationships are due to chance and provide us with inference concerning non-circumstantial associations between variables, that may however be non-causally interpreted. Unfortunately, it has been shown that in the context of a longitudinal observational study, when a covariate affected by past exposure is both a predictor of the future exposure and the outcome, i.e there exists time-dependent confounding, standard analysis approaches for the estimation of the exposure’s effect, may produce biased estimates. The g-methods are a class of methods introduced to estimate causal effects. The most recent of them is the Inverse Probability of Treatment Weighting (IPTW), which is applied to estimate the parameters of the Marginal Structural Models (MSMs). The aim of this thesis was to assess the performance of the MSMs in situations often met in longitudinal observational studies with survival endpoints. Following an exact simulati ...
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