Code for the simulation study and the analysis of the Pro-CCM data corresponding to draft manuscript "Estimating Marginal Treatment Effect in Cluster Randomized Trials with Multi-level Missing Outcomes". The data that support the findings in this paper are openly available at https://doi.org/10.7910/DVN/IIDE2B.
Contains R code for cleaning data and running the analyses for the Pro-CCM data:
data-application.R
: main code file that (1) cleans and reorganizes the data from the manuscript "Proactive Community Case Management Decreased Malaria Prevalence in Rural Madagascar: Results from a Cluster Randomized Trial" (Ratovoson et. al., 2022) and (2) fits four estimators (CC-GEE, IPW-GEE, MIPW-GEE-EM, and MMR-GEE) to the cleaned data.data-application-bootstrap.R
: code for obtaining standard error estimates through the cluster bootstrap approach.EM.R
: helper function for implementing the EM algorithm.MR.R
: helper function for estimating the multiply robust weights.
Contains R code for the simulation study:
run-methods.R
: main code file for (1) estimating beta parameters and (2) obtaining bootstrap standard error using the cluster bootstrap approach for the simulation.data-generation
: subfolder includesdata-generation1.R
,data-generation2.R
,data-generation3.R
for simulating the data under the Org-Pro-CCM, Alt-1, and Alt-2 design, respectively.methods
: subfolder includesEM.R
for implementing the EM algorithm,MR.R
for estimating the multiply robust weights,run-GEE-no-EM.R
for implementing CC-GEE, IPW-GEE, MIPW-GEE, and MMR-GEE without the EM algorithm,run-GEE-EM.R
for implementing CC-GEE, IPW-GEE, MIPW-GEE, and MMR-GEE with the EM algorithm.