tmle: Targeted Maximum Likelihood Estimation
tmle implements targeted maximum likelihood estimation,
first described in van der Laan and Rubin, 2006 (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 (MSM) 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.