{"id":{"repo_id":"lmu-germany","oai_identifier":"oai:edoc.ub.uni-muenchen.de:590"},"canonical_url":"https://search.dev.ndltd.org/etd/lmu-germany/oai:edoc.ub.uni-muenchen.de:590","repository":{"repo_id":"lmu-germany","name":"Ludwig Maxmilians Universität München","base_url":"https://edoc.ub.uni-muenchen.de/cgi/oai2"},"display":{"title":"Responder Identification in Clinical Trials","abstract":"The thesis gives an overview of the techniques used up to now for responder identification and it proposes a new method for systematic search for responders. The responder identification method consists of the following three steps: 1. Identification of prognostic factors (e.g. via Cox-PH model on the standard treatment arm) 2. Identification of patients in the new treatment arm, who's survival is badly estimated by the prognostic model (e.g. via search for outliers in the deviance or martingale residuals) 3. Identification of predictive factors, which describe common features of the patients with residual outliers, namely the positive and negative responders (e.g. via regression tree or bump hunting analysis, or via the suggested stabilized bump hunting procedure) The method is evaluated with a simulation study and applied on the EMIAT data se","abstract_html":"The thesis gives an overview of the techniques used up to now for responder identification and it proposes a new method for systematic search for responders. The responder identification method consists of the following three steps: 1. Identification of prognostic factors (e.g. via Cox-PH model on the standard treatment arm) 2. Identification of patients in the new treatment arm, who's survival is badly estimated by the prognostic model (e.g. via search for outliers in the deviance or martingale residuals) 3. Identification of predictive factors, which describe common features of the patients with residual outliers, namely the positive and negative responders (e.g. via regression tree or bump hunting analysis, or via the suggested stabilized bump hunting procedure) The method is evaluated with a simulation study and applied on the EMIAT data se","abstract_has_math":false,"creators":["Kehl, Victoria"],"institution":"Ludwig-Maximilians-Universität","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2002,"date_issued":"2002-11-21","date_published":"2002-11-21","updated_at":"2026-07-24T02:51:33Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://edoc.ub.uni-muenchen.de/590/","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Kehl, Victoria"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Universitätsbibliothek der Ludwig-Maximilians-Universität"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Ludwig-Maximilians-Universität"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The thesis gives an overview of the techniques used up to now for responder identification and it proposes a new method for systematic search for responders. The responder identification method consists of the following three steps: 1. Identification of prognostic factors (e.g. via Cox-PH model on the standard treatment arm) 2. Identification of patients in the new treatment arm, who's survival is badly estimated by the prognostic model (e.g. via search for outliers in the deviance or martingale residuals) 3. Identification of predictive factors, which describe common features of the patients with residual outliers, namely the positive and negative responders (e.g. via regression tree or bump hunting analysis, or via the suggested stabilized bump hunting procedure) The method is evaluated with a simulation study and applied on the EMIAT data se"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Responder Identification in Clinical Trials"]}]}],"canonical_facts":{"dc:creator":["Kehl, Victoria"],"dc:description.abstract":["The thesis gives an overview of the techniques used up to now for responder identification and it proposes a new method for systematic search for responders. The responder identification method consists of the following three steps: 1. Identification of prognostic factors (e.g. via Cox-PH model on the standard treatment arm) 2. Identification of patients in the new treatment arm, who's survival is badly estimated by the prognostic model (e.g. via search for outliers in the deviance or martingale residuals) 3. Identification of predictive factors, which describe common features of the patients with residual outliers, namely the positive and negative responders (e.g. via regression tree or bump hunting analysis, or via the suggested stabilized bump hunting procedure) The method is evaluated with a simulation study and applied on the EMIAT data se"],"dc:format.medium":["application/pdf"],"dc:publisher":["Universitätsbibliothek der Ludwig-Maximilians-Universität"],"dc:title":["Responder Identification in Clinical Trials"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["Ludwig-Maximilians-Universität"]},"updated_at":"2026-07-24T02:51:33Z"}