## Lecture: Propensity Scoring## Overview- Session overview YouTube►
## Readings and Lecture Resources- Propensity Scoring
- Propensity score quintile matching
- Read chapter 13 Statistical Analysis of Electronic Health Records in Big Data in Healthcare, pages 332 to 337
- Propensity Score with Inverse Probability Matching
- Read chapter 13 Statistical Analysis of Electronic Health Records in Big Data in Healthcare, pages 332 to 337 to 343
- A tutorial and case study in propensity score analysis Read►
- Lavanya's R code► Tutorial Part 1► Tutorial Part 2► Data 2010►
- Soleymani's Tutorial►
- Measuring treatment effects Read►
- Matching on propensity scores Read►
- Propensity scores and time to events Read►
- Propensity scoring of cost data Data► MatchIT R Code►
## AssignmentAssignments should be submitted in Blackboard. Include in the first page a summary page. In the summary page write statements comparing your work to answers given or videos. For example, "I got the same answers as the Teach One video for question 1." You are welcomed to use any software. - Convert STATA code to R ChatGPT►
- Answer in chapter 13 Statistical Analysis of Electronic Health Records in Big Data in Healthcare, pages 338. Don't do this in R, it is a lot more work than needed. Note that the data here and the data in the book in page 335 differ in a significant way. The data here is in quartile of severity of illness, while the data in the book are in quartile of propensity score. The correct way to solve this data is to estimate propensity weights and multiple costs by these weights to calculate average treatment effect.
- Answer in Excel Image►
- 0.014
- 0.986
- 71.43
- Cannot be determined
- 1.01
- None of these
Answer in chapter 13 Statistical Analysis of Electronic Health Records in Big Data in Healthcare, pages 337 to 338
- Calculate the propensity of taking the antidepressant. Regress taking of the antidepressant on the covariates.
- Calculate inverse propensity weights
- Verify that weighted regression removes the effects of all covariates. Regress the antidepressants on the weighted covariates and verify that none have a statistically significant effect on selection of the antidepressant. Visually show that the data have been balanced.
- Estimate Impact on Response: Regress response to the antidepressant on the covariates and treatment (taking the bupropion antidepressant). Describe how well the model was balanced and how well the impact of antidepressant was estimated.
- Data (Use instructor's last name as password) Download►
- See also pages 338 through 342 inn Chapter 13 Propensity scoring for example R code
- NIMH Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study Questions and Answers►
- Sankeerthi Mummidsetty's Answer► SQL code►
- Solutions can be obtained using different software. Answer►
- Vladimir Cardenas's Answer► R-Code►
- Shaan Muberra Khan's Teach One Slides► R-code► R-file► YouTube►
- Data Download►
- Yamani's answer YouTube►
- Carlos's Teach One YouTube► SQL Code►
- Sowmya Chakravarthy's Answer► R-Code►
- Tumen Sosorburam's Teach One YouTube►
- Calculate for each assessment, whether the person died before the next 6 months. Exclude single assessments, for which it is not possible to determine if the person dies in the next 6 months.
- Examine if unable to eat increases 6-month mortality risk, after controlling for other variables. In this analysis, unable to eat is an exposure variable and should be regressed on other covariates. Then inverse propensity weights should be used to remove the effects of other covariates before regressing 6-month mortality on unable to eat and other covariates.
- Data Download► List of Variables Read►
- Gidewon Tesfai's Answer► R-code►
- Yili Lin's Teach One Slides►YouTube►
## MoreFor additional information (not part of the required reading), please see the following links: This page is part of the HAP 819 course on Advanced Statistics organized by Farrokh Alemi PhD Home► Email► | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||