Targeted Maximum Likelihood Estimation for Longitudinal Data
Source:R/ltmle-package.R
ltmle-package.RdTargeted Maximum Likelihood Estimation (TMLE) of treatment/censoring specific mean outcome or marginal structural model for point-treatment and longitudinal data. Also provides Inverse Probability of Treatment/Censoring Weighted estimate (IPTW) and maximum likelihood based G-computation estimate (G-comp). Can be used to calculate additive treatment effect, risk ratio, and odds ratio.
References
Bang, Heejung, and James M. Robins. "Doubly robust estimation in missing data and causal inference models." Biometrics 61.4 (2005): 962-973.
Lendle SD, Schwab J, Petersen ML and van der Laan MJ (2017). "ltmle: An R Package Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data." _Journal of Statistical Software_, *81*(1), pp. # ' 1-21. doi: 10.18637/jss.v081.i01 doi:10.18637/jss.v081.i01
Petersen, Maya, Schwab, Joshua and van der Laan, Mark J, "Targeted Maximum Likelihood Estimation of Marginal Structural Working Models for Dynamic Treatments Time-Dependent Outcomes", Journal of Causal Inference, 2014 https://pubmed.ncbi.nlm.nih.gov/25909047/
Robins JM, Sued M, Lei-Gomez Q, Rotnitsky A. (2007). Comment: Performance of double-robust estimators when Inverse Probability weights are highly variable. Statistical Science 22(4):544-559.
van der Laan, Mark J. and Gruber, Susan, "Targeted Minimum Loss Based Estimation of an Intervention Specific Mean Outcome" (August 2011). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 290. https://biostats.bepress.com/ucbbiostat/paper290/
van der Laan, Mark J. and Rose, Sherri, "Targeted Learning: Causal Inference for Observational and Experimental Data" New York: Springer, 2011.
Author
Joshua Schwab, Samuel Lendle, Maya Petersen, and Mark van der Laan, with contributions from Susan Gruber
Maintainer: Joshua Schwab jschwab77@berkeley.edu