Dear all, Please join us for the Applied Statistics Workshop (Gov 3009) this Wednesday, October 5 from 12.00 - 1.30 pm in CGIS Knafel Room 354. Victoria Liublinska, a Ph.D. candidate from the Statistics Department at Harvard University, will present a paper entitled "Addressing missing data issues in a study with rare binary outcomes constrained by a small sample size". As always, a light lunch will be provided. The abstract for the presentation is: *We (re)analyze the data obtained in a recent study conducted to evaluate safety and efficacy of a new device designed for vertebroplasty. The following are just a few issues that had to be addressed: missing data in some covariates, incorrect analysis applied initially to the primary endpoint, missing data in secondary endpoints. The latter involved additional challenges such as panel data (responses were collected twice over time with a non monotone missingness pattern), secondary endpoints were rare binary events. The analysis was complicated by a relatively small sample size. Our work demonstrates how a complex missing data issue can be broken down into a set of small tasks that are solved individually. Some tasks involved multivariate missing data imputation using chained equations (van Buuren and Oudshoorn 2000; Raghunathan et al. 2001) with carefully chosen conditional models. Other tasks called for new state-of-the-art solutions, such as z-transformation procedure for combining repeated p-values (D. Rubin et al. 2011 (to be submitted), C. Licht 2009 Ph.D. thesis) or enhanced tipping-point graphs that assess sensitivity to various deviations from assumptions made about the missing data mechanism (Yan et al. 2009, Campbell et al. 2011).* This is joint work with D. Rubin and R. Gutman. An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin -- Konstantin Kashin Ph.D. Student in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://people.fas.harvard.edu/~kkashin/