title: Event history analysis and inference from observational epidemiology.
abstract: Systematic inclusion of time in observational epidemiological studies may help strengthen the inference to be drawn, but new epidemiological challenges arise, such as time-dependent confounders - covariates which may change from being confounders to being intermediate variables. The focus of this presentation concerns two sets of tools: event history analysis and structural nested failure time models, both applied to a particularly intricate problem in observational epidemiology, of empirically assessing the graft-versus-leukaemia effect after bone marrow transplantation.
authors.affiliation: Department of Biostatistics, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark. N.Keiding@biostat.ku.dk
authors.forename: N
authors.initials: N
authors.lastname: Keiding
mesh_terms: D016026:Bone Marrow Transplantation / Q000009:adverse effects; D002321:Career Choice; D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D003586:Cytomegalovirus Infections / Q000150:complications; D003627:Data Interpretation, Statistical; D016021:Epidemiologic Studies; D006086:Graft vs Host Disease / Q000150:complications / Q000188:drug therapy / Q000401:mortality; D020544:Graft vs Leukemia Effect; D006304:Health Status; D006801:Humans; D007166:Immunosuppressive Agents / Q000627:therapeutic use; D008390:Markov Chains; D008727:Methotrexate / Q000627:therapeutic use; D008954:Models, Biological; D015337:Multicenter Studies as Topic; D016273:Occupational Exposure; D012307:Risk Factors; D013997:Time Factors
chemical_list: D007166:Immunosuppressive Agents; D008727:Methotrexate