SMS scnews item created by John Ormerod at Wed 15 Oct 2014 1028
Type: Seminar
Modified: Wed 15 Oct 2014 1101
Distribution: World
Expiry: 18 Oct 2014
Calendar1: 17 Oct 2014 1400-1500
CalLoc1: Carslaw 173
CalTitle1: Statistics Seminar: Louise Rayan (UTS) -- Analysis of correlated multiple outcome data, with application to human reproduction
Auth: jormerod@pjormerod4.pc (assumed)

Statistics Seminar: Louise Ryan (UTS) -- Analysis of correlated multiple outcome data, with application to human reproduction

Abstract: 
In vitro fertilization (IVF) is an increasingly common method of assisted 
reproductive technology. Because of the careful observation and follow-up 
required as part of the procedure, IVF studies provide an ideal opportunity 
to identify and assess clinical and demographic factors along with 
environmental exposures that may impact successful reproduction. A major 
challenge in analyzing data from IVF studies is handling the complexity 
and multiplicity of outcome, resulting from both multiple opportunities 
for pregnancy loss within a single IVF cycle in addition to multiple IVF 
cycles. To date, most evaluations of IVF studies do not make use of full 
data because of its complex structure. This talk will report on work 
recently published in Statistics in Medicine where  we develop statistical 
methodology for analysis of IVF data with multiple cycles and possibly 
multiple failure types observed for each individual. We develop a general 
analysis framework based on a generalized linear modeling formulation that 
allows implementation of various types of models including shared frailty 
models, failure-specific frailty models, and transitional models, using 
standard software. We apply our methodology to data from an IVF study 
conducted at the Brigham and Women’s Hospital, Massachusetts. We also 
summarize the performance of our proposed methods on the basis of a 
simulation study.
 
Maity, Williams, Ryan, Missmer, Coull and Hauser (2014).  Analysis of in 
vitro fertilization data with multiple outcomes using discrete time-to-event 
analysis. Statistics in Medicine, Volume 33: 1738–1749.


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