tmleLite-package {tmleLite}R Documentation

Targeted Maximum Likelihood Estimation (Lite)

Description

Targeted maximum likelihood estimation of marginal additive treatment effect of a binary point treatment on a continuous or binary outcome, adjusting for baseline covariates. Missingness in the outcome is accounted for in the estimation procedure. Optional data-adaptive estimation of Q and g portions of the likelihood using the DSA algorithm is available. This “Lite” version of the software implements a simplified approach to targeted maximum likelihood estimation.

Details

Package: tmleLite
Type: Package
Version: 1.0-1
Date: 2009-12-16
License: (none specified)
LazyLoad: no

Author(s)

Susan Gruber, in collaboration with Mark van der Laan.

Maintainer: Susan Gruber, sgruber@berkeley.edu

References

1. Mark J. van der Laan and Daniel Rubin (2006), “Targeted Maximum Likelihood Learning”. The International Journal of Biostatistics, 2(1). http://www.bepress.com/ijb/vol2/iss1/11/

2. Susan Gruber and Mark J. van der Laan (2009), “Targeted Maximum Likelihood Estimation: A Gentle Introduction”. U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 252. http://www.bepress.com/ucbbiostat/paper252

3. Mark J. van der Laan, Sherri Rose, Susan Gruber editors, (2009) “Readings in Targeted Maximum Likelihood Estimation” . U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper xxx. http://www.bepress.com/ucbbiostat

4. Sandra E. Sinisi and Mark J. van der Laan, (2004). “Loss-Based Cross-Validated Deletion/Substitution/Addition Algorithms in Estimation”. U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 143. http://www.bepress.com/ucbbiostat/paper143

See Also

tmle, DSA,


[Package tmleLite version 1.0-1 Index]