In prospective randomized trials differences in population based means can be considered as estimates of mean causal exposure or treatment effects. Nevertheless, occurence of intermediate events in the course of a trial may lead to biased estimates of treatment effects. Particularly this will be the case, if the probability of such an event is not independent of initial treatment allocation and the intermediate event is both a risk factor for the main outcome parameter (e.g. survival) and a predictor of subsequent treatment. This situation was referred as

'treatment by indication problem' by Robins (1992) and is common in epidemiological trials. Robins demonstrated that the usual approach of an adjusted estimation of treatment

effect using a time-dependent proportional hazards model may be biased in this situation, whether or not one further adjusts for past confounder history in the analysis. In this thesis a novel inference procedure for randomized trials is introduced which is based on the idea of Robin's G-Estimation principle but particularly consideres specifics of randomized trials. The suggested procedure allows for an unbiased and consistent estimation of a treatment effect parameter (or paremeter vector) which is connected by a real-valued function to the parameters of an underlying distribution of survival times. In fulfilment of the requirements of a causal individual-level based model, a link of a subject's observed and counterfactual

survival time is directly achieved by the inverse distribution function of survival times in a reference treatment arm. In this term, causal inference is

feasible based on the likelihood function and its corresponding test statistics, even under appropriate consideration of time-dependent confounders.