Schuster, Tibor (2009): Verteilungsbasierte kausale Inferenzmodelle zur Schätzung von Therapieffekten in randomisierten kontrollierten klinischen Studien. Dissertation, LMU München: Faculty of Mathematics, Computer Science and Statistics 

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Abstract
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 timedependent 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 GEstimation 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 realvalued function to the parameters of an underlying distribution of survival times. In fulfilment of the requirements of a causal individuallevel 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 timedependent confounders.
Item Type:  Thesis (Dissertation, LMU Munich) 

Keywords:  verteilungsbasierte kausale Inferenz, distributionbased causal inference, Gestimation, randomized trials in oncology 
Subjects:  600 Natural sciences and mathematics > 510 Mathematics 600 Natural sciences and mathematics 
Faculties:  Faculty of Mathematics, Computer Science and Statistics 
Language:  German 
Date Accepted:  17. December 2009 
1. Referee:  Ulm, Kurt 
Persistent Identifier (URN):  urn:nbn:de:bvb:19110341 
MD5 Checksum of the PDFfile:  4001a94b7f03f0cacec694602d24e18f 
Signature of the printed copy:  0001/UMC 18322 
ID Code:  11034 
Deposited On:  03. Feb 2010 12:57 
Last Modified:  19. Jul 2016 16:28 