Lecture: Stratified Covariate Balancing  


Assigned Reading


For this assignment you can use any statistical software or use R software prepared by the instructor for stratified covariate balancing  Download R►

Find response to citalopram for patients with different types of depression. For each diagnosis use the remaining diagnoses as covariates and identify for which diagnoses citalopram is best.  For the analysis use data from STAR*D experiment conducted by NIMH.

  1. Read about the study protocol. Protocol►
  2. Download data.  Use instructor's last name as password.  Must enter password twice. Data 2010► Data 2003► Complete Data►
  3. Create a stratified covariate balancing model of impact of citalopram on depression.
  4. Use at least 10 variables to remove confounding in the model.  Balance the data to remove the effects of other types of co-occurring mental health diagnoses.  Show visually that the stratified covariate balancing has been able to remove the effects of other variables from response to citalopram
  5. Is there a smaller set of variables that you could stratify. Identify the Markov blanket of citalopram in predicting response to treatment. Vang's Slides►
  6. Describe what predicts success of citalopram.
  7. Describe how well the model predicts response to citalopram.

See work done by Mazloum► Elashka & Aiyar►


For additional information (not part of the required reading), please see the following links:

  1. Predictors of response to citalopram Read►
  2. Does citalopram help anxious depressions Read►
  3. Collapsing strata Read►

This page is part of the course on Comparative Effectiveness by Farrokh Alemi, Ph.D. Home► Email►