Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006. This version adds the tmleMSM() function to the package, for estimating the parameters of a marginal structural model for a binary point treatment effect. The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
|Author||Susan Gruber [aut, cre], Mark van der Laan [aut]|
|Date of publication||2017-01-07 17:45:06|
|Maintainer||Susan Gruber <firstname.lastname@example.org>|
|License||BSD_3_clause + file LICENSE | GPL-2|
calcParameters: Calculate Parameter Estimates (calcParameters)
calcSigma: Calculate Variance-Covariance Matrix for MSM Parameters...
estimateG: Estimate Treatment or Missingness Mechanism
estimateQ: Initial Estimation of Q portion of the Likelihood
fev: Forced Expiratory Volume (FEV) Data (fev)
summary.tmle: Summarization of the results of a call to the tmle routine
summary.tmleMSM: Summarization of the results of a call to the tmleMSM...
tmle: Targeted Maximum Likelihood Estimation
tmleMSM: Targeted Maximum Likelihood Estimation of Parameter of MSM
tmleNews: Show the NEWS file (tmleNews)
tmle-package: Targeted Maximum Likelihood Estimation with Super Learning