## Lecture: Remove Confounding through Propensity Scoring
## Assigned Reading- Session overview YouTube►
- 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►
- R code►
- Lavanya's 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
## 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."
- 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
- Using logistic regression, calculate the propensity to have cancer.
- Group the diagnoses using SQL. Within the naturally occurring groups of diagnoses, calculate probability of cancer. Calculate the logit of the probability. Regress the logit function on the diagnoses using ordinary regression. SQL►
- Report how the coefficients for the comorbidities of stomach cancer. How do these coefficients change across the two methods?
- Data►
- Answer by Shukri►
- Pooja's Teach One►
- Pooja's SQL Code►
- Tetteh's Teach One Slides►
- See R code for doing this in chapter 13 Statistical Analysis of Electronic Health Records in Big Data in Healthcare, page 339 to 343.
Balance the data to remove the effects of covariates. Show visually that you have successfully balanced the data. Use the following steps to accomplish this: **Calculate Propensity Score**: Calculate the propensity of taking the antidepressant. Regress taking of the antidepressant on the covariates.**Weights**: Calculate inverse propensity weights**Verify Balance:**Verify that weighted regression removes the effects of all covariates. Regress the antidepressants on the 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 taking the 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) Downloadâ–ş
- 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►
- Teach one by Sankeerthi Mummidsetty Read► SQL code►
- Solutions can be obtained using different software. Answer►
For patients in low severity, calculate the inverse propensity for treatment. Which of the following is correct? - 5.5
- 0.08
- 1.69
- 0.44
- None of the above
- Calculation of conditional probabilities from joint distribution is explained in "Chapter 3, Introduction to Probability and Relationships" in Statistical Analysis of Electronic Health Records in Big Data in Healthcare, pages 58 to 62 and pages 66 to to 68.
- The answer is 2.67, calculated from the difference of the average impact, (4+8+14)/3 - (2+6+10)/3 = 2.67
- The answer is 5.8, calculated from the difference of expected value for treated and untreated. Expected value for treated is 4(0.08/0.4) + 8(0.12/0.4) + 14(0.20/0.4) = 10.20 and expected value of untreated is 2(0.36/0.6) + 6*(0.12/0.6) + 10*(0.12/0.6) = 4.4
- The answer is 2.64 calculated as treatment difference in each strata weighted by the frequency of the strata 2(0.44) + 2*(0.24) + 4*(0.32) = 2.64
- Insufficient information is available to answer the question
- What does ChatGPT say is the correct answer? Ask your question from ChatGPT in a way that it can be answered by it. We want to know if we should take the weighted average of the difference or the difference of weighted average. Play with your question until you get an answer that is specific to this problem. Provide your question and ChatGPT answer in your submission.
- For Morgan and Harding discussion of this problem in a different context see page 15 and 16 Read►
- See chapter 13 Statistical Analysis of Electronic Health Records in Big Data in Healthcare, formula for average treatment effect, page 338.
- Data►
- Sherline's Teach One YouTube►
- For an alternative method of analysis of these data see case-based reasoning through the data (Use Instructor's last name as password) Read► Python Code►
- When there is a low overlap between matched cases and controls, then study findings do not generalize to many situations.
- A case with extremely low weight may count for too many controls, thus findings are sensitive to changes in a single case
- Too much of an overlap between cases and controls is a waste of data as it reinforces the obvious
For more, see chapter 13 Statistical Analysis of Electronic Health Records in Big Data in Healthcare, page 343. ## MoreFor additional information (not part of the required reading), please see the following links:/p> This page is part of the course on Comparative Effectiveness by Farrokh Alemi PhD Home► Email► | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||