## Lecture: Stratified Covariate Balancing
## Assigned Reading- Session overview YouTube►
- Description of Stratified Covariate Balancing method
- Overlap calculations
- Code for covariate balancing:
## AssignmentsSubmit assignments 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."
- Visually show that Dr. Smith see a different set of patients than his peer group. Show a tree where the nodes are the diagnoses and the consequences are length of stay within the tree branch. Decision Tree software►
- Balance the data through stratified covariate balancing. Graphically show that the weighting procedure of stratified covariate balancing results in same number of different types of patients treated by Dr. Smith or his peer. Switch the tree structure of peer group (but not the length of stay) with Dr. Smith's tree. This could be the output from R package on Covariate Balancing. Joseph's Teach One►
- Report the un-confounded impact of Dr. Smith on length of stay
using the common odds ratio of having above average length of stay.
SQL Common Odds►
- Reported the impact of Dr. Smith on length of stay using the weighted length of stay. SQL Weighted LOS►
- Using SQL, group the diagnoses into commonly occurring strata.
- Within each strata, calculate the odds of mortality from cancer.
- Calculate the common odds ratio across strata.
- Conduct sensitivity analysis for the calculated common odds
ratio. Sensitivity analysis is the process of changing one
variable and re-examining the conclusions. Drop one of the 35
comorbidities from the analysis and repeat the entire analysis and
check that 65% of cases are matched to controls. The percent of
cases that are matched is called overlap. It is defined as:
In most problems, one wants to maximize the overlap to be around at least 80%, so that findings can be generalized to the original cases.
Report how the un-confounded and confounded odds of mortality from stomach cancer are different from each other? Data► Using Synthetic Controls► Using Parents in Markov Blanket► Ujwala's Teach One►
The data report the experience of approximately 4,000 patients with various antidepressants: citalopram, bupropion, mirzapine, buspirone, lithium, nortriptyline, sertraline, thyroid, tranylclypromine, and venlafaxine. The following table shows the distribution of the covariates in the data.
The data are reported for a total of 22,254 visits. Visits may be 2 week or more apart. Not every patient shows for every scheduled visit. Organize the data so there is one row for each patient and each antidepressant trial (known in the data as Concat). Note that this field considers combination of antidepressants as a new antidepressant. Ignore the dose of the medication. Patients received multiple antidepressants during these trials until something worked for them. Include each time a new antidepressant was tried as a separate trial. If the patient has taken the antidepressant at any time during the trial, then mark it as 1, otherwise 0. Notice that some patients have taken the medication and others have not. Patients who have not taken a particular medication have taken other medications, so at any time we are comparing one medication to alternative treatments. The medication is considered to have caused the remission if the patient is referred to follow up portion of the study, at any point while taking the medication; i.e. the variable "Treatment_plan_equal_3" is set to 1 while taking the medication . - Clean the data SQL►
- For 3 antidepressants, balance the data using SQL and stratified covariate balancing.
- If necessary use parents in Markov Blanket of the medication to improve overlap beyound 80%.
- Describe which of the 3 medications should a patient who has PTSD and neurological disorders take. SQL►
- Does inability to eat increase probability of mortality in 6 months? Use SQL and stratified covariate balancing to determine if inability to eat contributes to mortality, after controlling for other disabilities of the patient. Data► Adel's Teach One► Adel's SQL Code►
- Does inability to toilet contribute to survival? To answer this question several steps must be taken. First, later events must be ignored. These are events that occur after the patient is unable to toilet. Second, the stratification need only occur among parents in Markov Blanket of "Unable to toilet". A quick way to identify the parents in the Markov Blanket of "Unable to Toilet" is to regress it on all the variables that precede it. Then the variables that are signficant and have large effect size (procedure to simulate LASSO regression) must be stratified. SQL and R Code►
- Simplify the database. Please organize the data so all independent variables are measured in the next to last available year. The outcome variable is diabetes and should be measured in the last year. Ignore all other data, including data on independent variables in prior years.
- Describe the data. Check that all independent variables are positively and monotonely related to prevalence of diabetes in the last year in the county. Split variables that are not monotonely related to prevalence of diabetes into 2 or more variables that are positively and monotonely related to diabetes
- Calculate impact of variables on diabetes in the last year
- Calculate the impact of obesity in prior year on diabetes in the last year, while controlling for other variables in prior year. Use Stratified Covariate Balancing to calculate the impact.
- Calculate the impact of access to food sources in prior year on diabetes in last year, while controlling for other variables. Use Stratified Covariate Balancing to calculate the impact.
## MoreFor additional information (not part of the required reading), please see the following links: - Alemi and Amr's original paper on covariate balancing Pubmed►
- Predictors of response to citalopram Read►
- Does citalopram help anxious depressions Read►
- Collapsing strata Read►
This page is part of the course on Comparative Effectiveness by Farrokh Alemi, Ph.D. Home► Email► |