Event history analysis and inference from observational epidemiology.
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.
Keiding
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Department of Biostatistics, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark. N.Keiding@biostat.ku.dk
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
D007166:Immunosuppressive Agents; D008727:Methotrexate