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DESCRIPTION:Marginal Meta-Analysis for Combining Multiple Randomized Clinic
 al Trials with Rare Events – Lessons Learned from Avandia Story \n\nYi Hua
 ng\, PhD\, Associate Professor – Dept. of Mathematics and Statistics\, Uni
 versity of Maryland\, Baltimore\n\nALL ARE WELCOME\n\nTuesday\, 16 May 201
 7\; 12:15-1:15pm - Purvis Hall\, 1020 Pine Ave. West\, Room 25\n\nAbstract
 : Meta analysis (MA) is commonly used in the post-marketing safety studies
  for FDA regulated medical products\, including drugs\, medical device\, a
 nd etc.  Avandia Studies (Nissen et al\, 2007\, 2010) is a powerful exampl
 e to show how important MA is in real life for quantifying the safety conc
 erns with policy impacts.  However\, the fact that the re-analysis of same
  Avandia data could reach different conclusions showed clearly the statist
 ical challenges and difficulties associated with standard fixed effect and
  random effect MA methods. Specifically\, the inclusion and exclusion of z
 ero trials\, changing the effect estimand to risk difference\, and/or usin
 g other fixed effect MA methods rather than Peto\, would all lead to diffe
 rent results.  Lesson learned from Avandia studies inspired our discovery 
 of the problems associated with “homogeneous effects” or “effect at random
 ” assumption – the validity assumption underlying standard MA approaches\,
  and led to a set of more relaxed Study at Random assumptions. Additionall
 y\, two more concerns motivated our research on this marginal meta analysi
 s: (1)\, rare events in safety studies often lead to low power in homogene
 ity test associated with standard MA approaches. Even though they may bias
  the results\, various types of add-hoc continuation corrections were prop
 osed and widely used to improve the performance of standard MA estimators.
  (2) Non-collapsibility issues associated with odds ratio limit the interp
 retability of many popular MA estimators too.  As a result\, based on the 
 new flexible study homogeneity assumption\, we proposed a marginal meta an
 alysis approach with natural weights which provided a consistent treatment
  effect estimate for marginal causal effects combining randomized clinical
  trials in safety studies.  This estimator is particularly useful when the
  outcome is rare\, and double zero trials are naturally accounted in the e
 stimation without any add-doc continuity correction.  Systematic simulatio
 n studies show that the proposed estimator performs reasonably well under 
 different rationales.  This method is re-applied in Avandia safety evaluat
 ion as a real case application.  This is a joint work with my students\, E
 lande Baro\, Yun-Yu Cheng\, and colleague from FDA\, Guoxing Soon. \n\nYi 
 Huang1\,  Elande Baro2\, Yun-Yu Cheng1\,  Guoxing Soon2\n\n1: Dept. of Mat
 hematics and Statistics\, University of Maryland\, Baltimore County\n\n2: 
 Office of Biostatistics\, OTS\, CDER\, US FDA\n\nKeywords:  Meta Analysis\
 , Rare Events\, Homogeneity Assumptions\, Effect at Random\, Avandia\, Zer
 o trials.\n\n                                                             
                                        \n\nBio: Dr. Huang is Associate Pro
 fessor in the Department of Mathematics and Statistics at University of Ma
 ryland\, Baltimore County\, and an affiliated faculty in the joint Doctora
 l Program of Gerontology\, School of Medicine\, University of Maryland.  S
 he completed the Biostatistics Ph.D. training at Johns Hopkins Bloomberg S
 chool of Public Health in 2007. As a biostatistician\, her research focus 
 on propensity score related causal inference methodology\, comparative eff
 ectiveness research methods (e.g. meta-analysis)\, and public health orien
 ted collaborations. Current projects include post-marketing safety studies
 \, gerontology projects\, health policy study for Maryland state\, and enr
 ichment design for efficacy trials. http://www.math.umbc.edu/people/yihuan
 g.htm\n\n \n\nwww.mcgill.ca/epi-biostat-occh/news-events/seminars/biostati
 stics\n
DTSTART:20170516T161500Z
DTEND:20170516T171500Z
LOCATION:Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue des Pins 
 Ouest
SUMMARY:SPECIAL SEMINAR: BIOSTATISTICS
URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/special-seminar-b
 iostatistics-267950
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