The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
|
Activity Number:
|
428
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
WNAR
|
| Abstract - #303000 |
|
Title:
|
multiPIM: An R Package for Variable Importance Analysis
|
|
Author(s):
|
Stephan Johannes Ritter*+ and Nicholas P. Jewell and Alan Hubbard
|
|
Companies:
|
University of California at Berkeley and University of California at Berkeley and University of California at Berkeley
|
|
Address:
|
Group in Biostatistics, , ,
|
|
Keywords:
|
R packages ;
variable importance ;
super learner
|
|
Abstract:
|
We have written an R package, multiPIM, which performs variable importance analysis. The user must input one or more exposures, one or more outcomes, and optionally, one or more covariates to include in the adjustment set. An effect measure (and an associated standard error) is calculated for each exposure-outcome pair. PIM stands for Population Intervention Model. The parameter of interest is a type of attributable risk, and the default is to use a double-robust inverse probability of censoring weighted estimator for this parameter. The default method of estimating the nuisance parameters of each model is to combine several regression algorithms in a super learner. A re-analysis of data from the Western Collaborative Group Study will also be described.
|
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 2011 program
|
2011 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.