Stratified Covariate Balancing Using R Farrokh Alemi, PhD

Download Stratified Balancing Note Capital Letters

Load Package into Library

Remove Impossible Values Prepare Your Data Remove Impossible Values Visit after Death Negative ID Zero Blood Pressure Pregnant Males

Predict from Other Variables Prepare Your Data Impute Missing Values Predict from Other Variables Use Mode Use Average No Report No Diagnosis

Prepare Your Data Binary Indicators Initial Analysis Binary Indicators More cases match to controls Use R discretization software Above or below average Worst category vs. all others No report then no diagnosis

Prepare Your Data Binary Indicators Initial Analysis Binary Indicators More cases match to controls Use R discretization software Above or below average Worst category vs. all others No report then no diagnosis

Read Simulated Data Download from Read into directory http://openonlinecourses.com/causalanalysis/simulated%20bundled%20data.csv Read into directory

Look at Data Using fix(data)

Select Subset of Data for Analysis Data should list only treatment and covariates to be balanced

Don’t Stratify Variables on Causal Path Examine sequence of events Avoid complications of treatment Don’t stratify mediators Conduct Collider Tests

Balance Data > balanced=stratadisc(4,5,subset)

Common Odds Ratio does not include one so it is significant Balance Data Common Odds Ratio does not include one so it is significant

In large data, effect size should at least doubling Balance Data In large data, effect size should at least doubling

60% of cases matched to controls Balance Data 60% of cases matched to controls

Check the Strata You Created # Check the strata you have created fix(balanced)

Check Balance

Conduct Sensitivity Analysis # Sensitivity analysis of treatment in column 4 # Outcome in column 5 # In data set called subset revised=sensdisc(4,5,subset) revised