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Sessions Were Renumbered as of May 19.

Legend:
CC-W = McCormick Place Convention Center, West Building,   CC-N = McCormick Place Convention Center, North Building
H = Hilton Chicago,   UC= Conference Chicago at University Center
* = applied session       ! = JSM meeting theme

Activity Details

497 Wed, 8/3/2016, 8:30 AM - 10:20 AM CC-W175b
Novel Methods for Addressing Confounding Bias in Observational Studies — Contributed Papers
Section on Statistics in Epidemiology , International Chinese Statistical Association
Chair(s): Robert Hirsch, Stat-Aid Consulting
8:35 AM Using Propensity Scores to Infer Causal Effects on Heart Health from Chemotherapy Treatment of Breast Cancer Patients — John Craycroft, University of Louisville ; Maiying Kong, University of Louisville ; Carrie Lenneman, University of Louisville
8:50 AM Propensity Scoring Methods for Ordinal Treatments — Thomas Greene, The University of Texas Health Science Center at Houston ; Stacia DeSantis, The University of Texas Health Science Center at Houston ; Michael D. Swartz, The University of Texas Health Science Center at Houston
9:05 AM False Discovery Rate Control for Effect Modification in Observational Studies — Bikram Karmakar, University of Pennsylvania ; Ruth Heller, Tel-Aviv University ; Dylan Small, University of Pennsylvania
9:20 AM Mediation Analysis with Multilevel Additive Models — Qingzhao Yu, Louisiana State University Health Sciences Center ; Bin Li, Louisiana State University ; Richard Scribner, Louisiana State University Health Sciences Center
9:35 AM Weighted Estimation in Confounded Binary Data Subject to Outcome Misclassification — Christopher A. Gravel, McGill University ; Robert W. Platt, McGill University
9:50 AM Assessing Sensitivity to Unmeasured Confounding in Multilevel Models Using a Simulated Potential Confounder — Nicole Carnegie, University of Wisconsin - Milwaukee ; Jennifer L. Hill, New York University ; Vincent Dorie, New York University
10:05 AM From Presence of Pathogens to Etiology of Disease: An Innovative Latent Class Model with Two Latent Variables — Nong Shang, CDC
 
 
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