R/ltmle-package.R

In joshuaschwab/ltmle: Longitudinal Targeted Maximum Likelihood Estimation

#' Targeted Maximum Likelihood Estimation for Longitudinal Data
#'
#' Targeted 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.
#'
#'
#' @name ltmle-package
#' @docType package
#' @author Joshua Schwab, Samuel Lendle, Maya Petersen, and Mark van der Laan,
#' with contributions from Susan Gruber
#'
#' Maintainer: Joshua Schwab \email{jschwab77@berkeley.edu}
#' @seealso \code{\link{ltmle}}
#' @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
#' \doi{10.1515/jci-2013-0007}
#'
#' 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.
#' \url{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.
#' @keywords package
#' @examples
#'
#' ## For examples see examples(ltmle) and \url{http://joshuaschwab.github.io/ltmle/}
#'
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#' Sample data, regimes, and summary measures
#'
#' Sample data for use with ltmleMSM. Data: n=1000: male age CD4_1 A1 Y1 CD4_2
#' A2 Y2 CD4_3 A3 Y3 A1..A3 are treatment nodes, Y1..Y3 are death, CD4_1..CD4_3
#' are time varying covariates. We are interested in static regimes where a
#' patient switches at some time. In summary.measures, switch.time is first
#' time where At is 1 (4 if never switch), time is the horizon.
#'
#' regimes: 200 x 3 x 4 [n x numACnodes x numRegimes] summary.measures: 4 x 2 x
#' 3 [numRegimes x numSummaryMeasures x numFinalYnodes]
#'
#' @name sampleDataForLtmleMSM
#' @docType data
#' @format List with three components: data, regimes, summary.measures
#' @source simulated data
#' @keywords datasets
#' @examples
#'
#' data(sampleDataForLtmleMSM)
#'
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