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DESCRIPTION:Matching methods for evaluating the effect of a time-dependent 
 treatment on the survival function.\n\nWeb site : www.mcgill.ca/epi-biosta
 t-occh/news-events/seminars/biostatistics\n\n \n\nDouglas Schaubel is Prof
 essor of Biostatistics at the University of Michigan\, School of Public He
 alth. He received his Ph.D. in Biostatistics in 2002 from the University o
 f North Carolina at Chapel Hill. Professor Schaubel's methodologic researc
 h interests mostly involve survival analysis and the analysis of recurrent
  event data. Along those lines\, he has developed methods in the areas of 
 time-dependent treatments\, causal inference\, time-varying treatment effe
 cts\, biased sampling\, and dependent censoring. Much of his methods resea
 rch has been funded through two previous R01 grants\, 'Survival Analysis M
 ethods for Organ Failure Data' and current R01\, 'Methods for the Analysis
  of Survival Processes Arising in End-Stage Renal Disease''. Professor Sch
 aubel's collaborative work is mostly in the area of end-stage renal diseas
 e and liver transplantation\, with his collaborators including the Univers
 ity of Michigan Kidney Epidemiology and Cost Center (KECC) and Arbor Resea
 rch Collaborative for Health. KECC projects that he works on include a mea
 sure development project funded by the Centers for Medicare and Medicaid S
 ervices\, and the United States Renal Data System (USRDS). At Arbor Resear
 ch\, he mostly works on the Dialysis Outcomes and Practice Patterns Study 
 (DOPPS). Professor Schaubel is a Fellow of the American Statistical Associ
 ation\, and serves as Associate Editor for Biometrics\, Statistics in the 
 Biosciences\, Lifetime Data Analysis\, and the Journal of the American Sta
 tistical Association (Theory and Methods). For more information\, please v
 isit: https://sph.umich.edu/faculty-profiles/schaubel-douglas.htmlWe consi
 der observational studies of survival time featuring a binary time-depende
 nt treatment. We propose flexible methods applicable to big data sets for 
 the purpose of estimating the causal effect of treatment among the treated
  with respect to survival probability. The objective is to compare post-tr
 eatment survival with the survival function that would have been observed 
 in the absence of treatment. The proposed methods utilize prognostic score
 s\, but are otherwise nonparametric. Essentially\, each treated patient is
  matched to a group of similar not-yet-treated patients. The treatment eff
 ect is then estimated through a difference in weighted Nelson-Aalen surviv
 al curves\, which can be subsequently integrated to obtain the correspondi
 ng difference in restricted mean survival time (area between the survival 
 curves). Large-sample properties are derived\, with finite-sample properti
 es evaluated through simulation. The proposed methods are then applied to 
 estimate the effect on survival of kidney transplantation. This is joint w
 ork with Kevin He\, Yun Li and Danting Zhu.\n
DTSTART:20190122T203000Z
DTEND:20190122T213000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Douglas Schaubel\, PhD\, University of Michigan
URL:/mathstat/channels/event/douglas-schaubel-phd-univ
 ersity-michigan-293438
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