Targeted minimum lossbased estimation (TMLE) of the average causal effect of communitybased intervention(s) at a single time point on an individualbased outcome of interest. It provides three approaches to analyze hierarchical data: communitylevel TMLE, inidividuallevel TMLE and stratified TMLE. Implementations of the inverseprobabilityof treatmentweighting (IPTW) and the Gcomputation formula (GCOMP) are also available for each approach. The package supports multivariate arbitrary interventions (deterministic or stochastic) with a binary or continuous outcome. The tmleCommunity() function calculates the marginal treatment effect among independent community units (or i.i.d individual units if no hierarchical structure) using TMLE. Besides, it allows userspecified dataadaptive machine learning algorithms through SuperLearner and h2oEnsemble packages. The input dataset should be made up of rows of communityspecific and individualspecific observations, with each row i (in community j) containing random variables (W_{i,j}, E_j, A_j, Y_{i,j}), where E_j represents a vector of community j's environmental baseline covariates, W_{i,j}represents a vector of individual i's individuallevel baseline covariates, A_j is the exposure(s) (can be univariate or multivariate, can be binary, categorical or continuous) assigned or naturally occurred in community j and Y_{i,j} is i's outcome (either binary or continuous). More details can be found in '?tmleCommunitypackage' and '?tmleCommunity'.
Package details 


Maintainer  Chi Zhang <[email protected]> 
License  GPL2 
Version  0.1.0 
URL  https://github.com/chizhangucb/tmleCommunity 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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