This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 237
Type: Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #308631
Title: Variable Importance Estimation in a Kaiser Permanente Database
Author(s): Sherri Rose*+ and Bruce Fireman and Mark J. Van der Laan
Companies: University of California, Berkeley and Kaiser Permanente and University of California, Berkeley
Address: , Berkeley, CA, ,
Keywords: variable importance ; targeted maximum likelihood estimation ; super learner ; effect estimation ; methods

Kaiser Permanente is located in Northern California and provided medical services to approximately 350,000 persons over the age of 65 in the year 2003. We were interested in establishing which of 184 disease and diagnosis variables (medical flags) from 2003 had the greatest impact on death in 2004. Variable importance measures were estimated for the top medical flags using targeted maximum likelihood estimation (TMLE). TMLE is a flexible procedure that allows the use of machine learning (such as super learner) to estimate both causal and variable importance parameters. This method provided a ranked list of medical flags in terms of their contributions to determining the outcome of death, adjusted for the remaining medical flags as well as age and gender. Variable importance analysis for 2004, 2005, and 2006 was also performed.

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