Targeted Maximum Likelihood Estimation to adjust for time-varying confounding – a tutorial paper

R Markdown and Analysis Code

Last updated on Oct 22, 2019

This repository contains data and a number of snippets of R code, used in the TMLE tutorial by Clare, Dobbins, Bruno and Mattick.

Description	Markdown	Github R code	Download R code
Creating the longitudinal dataset used in example analyses. Data creation is done using the package ‘simcausal’ (1).	Data Creation Markdown	Data Creation Code	Download code
Cross-sectional TMLE analysis, both manually and using the package ‘tmle’ (2).	Cross-sectional Analysis Markdown	Cross-sectional Analysis Code	Download code
Longitudinal TMLE with a single outcome measurement, both manually and using the package ‘ltmle’ (3).	Single Outcome Longitudinal Markdown	Single Outcome Longitudinal Code	Download code
Longitudinal TMLE with a repeated outcome measurement, both manually and using the package ‘ltmle’ (3).	Repeated Outcome Longitudinal Markdown	Repeated Outcome Longitudinal Code	Download code

The longitudinal dataset, ldata.RData, is also included in the repository: https://github.com/philipclare/tmletutorial

Sofrygin O, van der Laan Mark J, Neugebauer R. simcausal R Package: Conducting Transparent and Reproducible Simulation Studies of Causal Effect Estimation with Complex Longitudinal Data. Journal of Statistical Software. 2017;81(2):1-47.
Gruber S, van der Laan MJ. tmle: An R Package for Targeted Maximum Likelihood Estimation. Journal of Statistical Software. 2012;51(13):1-35.
Lendle SD, Schwab J, Petersen ML, van der Laan MJ. ltmle: An R Package Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data. Journal of Statistical Software. 2017;81(1):1-21. Petersen ML, van der Laan MJ. ltmle: An R Package Implementing Targeted Minimum Loss-Based Estimation for Longitudinal Data. Journal of Statistical Software. 2017;81(1):1-21.

Biostatistician at the National Drug and Alcohol Research Centre, UNSW Sydney.