JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 324
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #305117
Title: Data-Adaptive Estimation of IPW Weights for Causal Effect Estimation in Large Longitudinal Data Sets
Author(s): Susan Gruber*+ and Miguel HernĂ¡n
Companies: Harvard School of Public Health and Harvard School of Public Health
Address: 109 St. Paul Street, Brookline, MA, 02446, United States
Keywords: causal inference ; data adaptive ; super learner ; MSM ; IPW
Abstract:

The parameters of a marginal structural model can be estimated using inverse probability weights (IPW). Correct specification of the conditional probabilities of receiving treatment at each time point given baseline and time-dependent confounders ensures consistent causal effect estimates, assuming underlying causal assumptions are met. Yet, even when all important confounders are known and have been measured, the true functional forms of the relevant relationships are generally unknown. We applied a data-adaptive technique known as super learning (van der Laan, Polley, and Hubbard, 2007) to estimate conditional treatment assignment probabilities at multiple time points for the numerator and denominator of stabilized weights. Because super learning can be computationally expensive, there is a trade off between the scope of the search over the model space and computation time. We examine these issues in the context of estimating the hazard of mortality for cART vs. no cART in patients with HIV, using data from the HIV-CAUSAL collaboration (2011).


The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program




2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.