Basic data

HAP 719: Advanced Statistics I

Logistic Regression, Interpretation 

Interpretation of logistic regression findings
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Overview

In this module, you'll continue to master the art of analyzing the association of variables with binary data. You'll interpret findings from logistic regression, allowing you to draw meaningful conclusions from your analyses. You'll develop techniques to adjust for missing values in logistic regression, enhancing the accuracy and reliability of your results. All along, you will tackle real-world data challenges and make informed decisions based on your analyses.

Learning Objectives

  • Interpret findings from logistic regression analysis
  • Adjust for missing values in logistic regression

Lecture

AI assisted Indicates content, image, or video made with assistance from AI systems

  • Read Chapter 12 Logistic Regression in Statistical Analysis of Electronic Health Records by Farrokh Alemi, 2020 AI assisted Slides► AI assisted Video►
  • More on missing values Read► Slides►
  • Yili Lin's lecture on interpreting R output for Logistic regression AI assisted Slides► AI assisted Video►

Assignments

Question 1: The following data provide the length of stay of patients seen by Dr. Smith (Variable Dr Smith=1) and his peer group (variable Dr. Smith = 0).  Does Dr. Smith see a different set of patients than his peer group?  In particular, what is the probability of patients being seen by Dr. Smith.  Regress the choice of provider on the 9 diagnoses provided. 

Resources:

Question 2:  In a nursing home, data were collected on residents' survival and disabilities.  The data are listed in the following order: ID, age, gender (M for male, F for Female), number of assessments completed on the person, number of days followed, days since first assessment, days to last assessment, unable to eat, unable to transfer, unable to groom, unable to toilet, unable to bathe, unable to walk, unable to dress, unable to bowel, unable to urine, dead (1) or alive (0), and assessment number.  Predict from the patient's assessments (i.e. their age and current disabilities at time of assessment) if the patient is likely to die. Here are the steps in this analysis: 

  1. Read the data, making sure all entries are numbers. 
  2. Calculate age at each assessment not just at first assessment. 
  3. Clean the data, removing impossible situations (remove cases with date of assessment after death). 
  4. Remove irrelevant cases (all cases that have only one assessment)
  5. Organize age at current admission into a binary variable above or below the average age at current assessment.
  6. Estimate missing values
  7. Regress death in 6-months on various current disabilities, age, gender, and pairwise interactions of these variables. 

Resources:

Question 3:  In a nursing home, data were collected on residents' survival and disabilities.  The data are listed in the following order: ID, age, gender (M for male, F for Female), number of assessments completed on the person, number of days followed, days since first assessment, days to last assessment, unable to eat, unable to transfer, unable to groom, unable to toilet, unable to bathe, unable to walk, unable to dress, unable to bowel, unable to urine, dead (1) or alive (0), and assessment number.  Predict from the patient's assessments (i.e., their age and disabilities at time of assessment) if the patient is likely to die and should be admitted to the hospice program. 

Resources for Question 1:

Question 4: Regress incidence of diabetes on all other body-system variables (including pairwise, and triplet of variables) and indicator variables for missing variables. You can do the analysis first on 10% sample before you do it on the entire data that may take several hours.

  1. Create a binary variable that is 1 every time a variable is missing and 0 otherwise. Predict diabetes from patterns of missing binary variables. 
    • Create binary variables for missing values.
    • Calculate number of variables that are missing for each case
    • Create a cascaded data, where cases are arranged in order of number of variables missing. Put all missing variables last.
    • Create interaction terms for missing indicators so that the interaction term corresponds with patterns of missing variables in the cascaded data.
    • Test the statistical significant of missing indicators and interaction among missing indicators.
    • Report the percent of variation in incidence of diabetes explained by patterns of missing variables
  2. Regress diabetes on body systems, pairs of body systems, triplets of body systems, and statistically significant patterns of missing values. Report the coefficients and the percent of variation explained. One way to reduce the number of independent variables is to drop body systems that are always missing.  When a variable is always missing, then regression software automatically drop these variables. You can save computation time by dropping the variables before analyzing the data.  The plot below shows body systems and extent of missing values within them. 

Pattern of Missing 

Resources for Second Week Question 4:


This page is part of the HAP 719 course on Advanced Statistics I by Farrokh Alemi PhD Home► Email►