## HAP 819: Advanced Statistics## Lecture: Multiplicative Regression
## Assigned Reading- Read Chapter 18 in Statistical Analysis of Electronic Health Records by Farrokh Alemi, 2020 Slides►
- Cursor and do-while SQL commands Slides► YouTube►
## AssignmentSubmit one file for all questions. Include all charts, code, and output in the same file. Start each question in a separate page or sheet. 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."
- Use LASSO Logistic Regression to identify comorbidities that are predictive of survival in 6 months.
- For comorbidities that make prognosis of lung cancer worse (i.e., non-zero comorbidities in the regression), identify impact of the comorbidity alone by itself, without any other comorbidities. Report the number of such cases and the probability of mortality within 6 months.
- Fit a multiplicative model to the data and estimate the parameters of the model.
- Estimate the overall k parameter for the multiplicative model.
Estimate k by trying -1, 0, and +1. Use the following relationship
among the parameters to estimate k:
Resources for Question 1: - Data Download►
- Estimate parameters SQL►
- Survival from lung cancer SQL & R Code►
- Vladimir Cardenas's Answer► R-code► (Requires passcode)
- Fateme Dehrouye's Teach One YouTube►
- Clean the data using the following steps:
- For each assessment calculate if the patient dies in 6 months from the assessment. If the patient never dies assume not dead in 6 months.
- At death assume that the patient has all disabilities.
- Drop last assessment as no outcomes can be calculated from the last assessment.
- Assume age of assessment is age at first assessment plus days to assessment/365.
- Residents with negative age should be dropped because of date of birth errors.
- Residents 100 or more years should be dropped because of small sample.
- Adjust all variables to be binary, either 0 or 1; and 1 is assigned to the level that increases the odds of mortality.
- Predict from the patient's assessments (i.e. their age, gender, and disabilities at time of assessment) if the patient is likely to die in the next 6 months and may be a candidate for hospice care.
- Calculate the fit in the data for possible k values of 1.0, 0.0, and -1.0. Calculate the k constant for the multiplicative model that fits
the following formula:
- Use the model you have developed to predict the probability of mortality for a 75 year old resident with only urine, bowel, and toilet disabilities.
Following resources are available for Question 2: - Data Download►
- Cleaned data Download►
- Calculate k constant SQL►
- Predict from a multiplicative model SQL► Answer►
- Clean and organize the data for analysis of bupropion
- Identify the baseline characteristics that are non-zero LASSO predictors of receiving bupropion
- Create a multiplicative model of the impact of baseline variables and bupropion on remission.
Resources for Question 3: - Data Download►
- Study protocol Journal Article►
## MoreFor additional information (not part of the required reading), please see the following links: - Multi-attribute preference functions. Health Utilities Index. PubMed►
- Utility functions for health profiles PubMed►
- How decisions reveal our preferences PubMed►
This page is part of the course on Comparative Effectiveness by Farrokh Alemi PhD Home► Email► |
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