Assigned Reading
- Effectiveness of antidepressants
PubMed►
- Proxy measure for remission of depression symptoms
PubMed►
- SAFE procedures for excluding some independent variables from LASSO regressions Read►
Assignment
The semester long project in this course is to assess the effectiveness of an existing guide to depression medications in minority populations.
(A) Register for All of US. This step was assigned prior to start of the course. If you have not done all registration steps, including training, then
you will need to solve this problem quickly. This registration may take several weeks for students who do not have a State ID. Otherwise, it
should take about 90 minutes. Also make sure that you remember your password as there are multiple accounts set up in this process. You
need to write down the password for each sign in separately on a piece of paper as you may confuse which password is needed when.
- Register for an account on @researchallofus.org
- Change from temporary password to a new password and record your password on paper somewhere.
- Turn on Google 2-Step Verification
- Verify your identity with Login.gov. This step requires a state ID or Drivers License, and text phone.
- There are multiple passwords that you should keep in mind. There is your GMU password, your research workbench password on All of
Us and your computer password, and your Google password. Please make sure that you keep these accounts separate and read the messages
carefully to see which password is needed.
- Complete All of Us Registered Tier Training
- You do not need to get additional data access beyond registration data. George Mason University does not allow access to Controlled Tier.
- Sign the Code of Conduct Sign Data User Code of Conduct
When you have registered completely, you should see something like this
page:
(B) Create Cohort and Related Data Sets. Note that a
cohort and data sets are different concepts.
- Create your cohort in All of Us.
- Limit the cohort by patients who have bipolar depression.
- Create the concept for bipolar depression. Review in PubMed how
investigators have defined Major depression in EHRs. Alternatively, use conditions defined within All of Us to select
the right definition of Major Depression.
- The unit of analysis is medications and not individuals. An
individual can have multiple medications. Define the database so
that there is one entry for each bipolar depression medication.
- Create your data sets, for your cohort. Do not include non-EHR data or surveys.
Note that creation of medication data set requires creation of
concepts that capture the medication in the data. In your cohort, select demographics (age, gender)
and all conditions as independent variables of interest. No survey responses are needed for independent variables. Rely only
on EHR data only. You also need the date of
first use (purchase) of the medication. The date of occurrence of the
response variable is the first time the variable/condition has occurred. Here are the
data points that you need to include in your data sets:
- ID of antidepressant
- ID of person
- Age at first intake of
antidepressant
- Sex at birth
- 50,000 conditions
The following resources may be of use in this task:
- Organizing antidepressants
CSV►
- Creating survival variable
Read►
- Vlad Cardenas Teach One Part 1 YouTube► Slides►
- Vlad Cardenas Teach One Part 2
You Tube►
Slides►
Code►
- Rasil Alamri’s Teach One Part 1
YouTube►
- Mona Mohamed’s Teach One Part 2
YouTube►
- Source Code Part 2: Adding AI Predictors, Generating Prediction,
and Executing Regressions
PDF►
- Reference Data Mapping File for AI Predictors CSV►
- Divya Bhavanam's Teach One on predicting from the AI system and All of Us data
YouTube►
(C) Describe the Population. In this step you need to create Table 1 in your eventual report. This Table should
include the description of the population. For examples of Table 1 see PubMed. Provide a summary of your data that includes number of
antidepressants examined, number of individuals involved, number of antidepressants discontinued, number of days individuals followed, number
of days antidepressants continued, number of medical conditions at baseline of use of antidepressants, number of antidepressants used prior
to baseline, experience with previous antidepressants.
(D) Fit a Network Model to the Data: Use chain of LASSO regressions to create a network model of direct and indirect
predictors of remission after taking your antidepressant. Include pairwise interaction of conditions. This may result in too many
independent variables. Analyze the 2 most common medication for
bipolar depression separately. To reduce the number of independent variable
use the SAFE procedure or likelihood ratios, where strong rules are used to exclude some
variables.
(E) Report Your findings: This report should include the following section and provided at approximate times indicated by email to the instructor:
- Abstract. Include a structured abstract using objective of
the study, method, results, and main conclusion. The abstract
should be written after you complete other sections. The
abstract must not exceed 500 words and should report the number of
words used in the abstract.
- Background literature review should not exceed 1 page. Your one
page literature review should assume a reader familiar with the
literature and not exceed three paragraph. The first paragraph should
address the significance of the area you are addressing, including
prevalence of depression and importance of selection of
the medications. The second paragraph should describe failure of
clinicians in selecting the right medication, as reported in the literature. The paragraph should not exceed two or three
sentences but can have numerous references. The last paragraph should
discuss how your analysis can help selection of medication based on
patient's medical history. Background
section should be a brief synthesis of existing research findings related to the problem being addressed in the study.
Every sentence should have a reference. We are not
interested in unsupported claims.
- Method section should be a complete description of the
methods; and there is no page limit but brevity is appreciated. It should include a paragraph or a sentence on source of data.
It should describe the inclusion and exclusion criteria for the
creation of the cohort and compare these criteria to what has been
done in the literature. It should have a sentence or a paragraph, with
citations, on definition of remission. It
should have a sentence or a paragraph on number of, and definition of,
independent variables. These statements should clarify how missing values were
treated and explain what steps were taken to ensure that independent
variables occur prior to response/dependent variable. There
should be a paragraph on analytical methods used.
-
Results section should describe the findings and there is no page
limit. Table 1 should be description of the
population studied. Figures and additional tables should
summarize the statistical findings. These should include parameters of
your model and the fit between the guide and experience of African
Americans. There should not be any discussion of findings in the
result section. Result section should include a Netica model. Here is an example of a Table to report McFadden
R-square for AI predictions:
Table 2: Cross-Validated McFadden R2 in Predicting African Americans Antidepressant Response
|
|
Med 1 |
Med 2 |
|
Cases
|
1,984
|
2,658
|
|
Cases with
Remission
|
780
|
1,064
|
|
Model accuracy
|
7%
|
12%
|
Table 3: Top 5 Factors with Largest Absolute value
Added to the AI
|
|
Med 1 |
Med 2 |
|
Intercept |
xx |
vv |
|
Top |
factor,
coefficient |
factor,
coefficient |
|
1st |
factor,
coefficient |
factor,
coefficient |
|
2nd |
… |
… |
|
3rd |
… |
… |
|
4th |
… |
… |
|
5th |
… |
… |
- Discussion section should include 4 distinct sections and there is
no page limits. The first section should be a summary of the key
findings. The second section should be a review of support for the
findings in the literature. The third section should summarize study
limitations. The last section should conclude with policy
implications.
This page is part of the HAP 823 course organized by Farrokh Alemi, Ph.D.
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