Study your flashcards anywhere!

Download the official Cram app for free >

  • Shuffle
    Toggle On
    Toggle Off
  • Alphabetize
    Toggle On
    Toggle Off
  • Front First
    Toggle On
    Toggle Off
  • Both Sides
    Toggle On
    Toggle Off
  • Read
    Toggle On
    Toggle Off

How to study your flashcards.

Right/Left arrow keys: Navigate between flashcards.right arrow keyleft arrow key

Up/Down arrow keys: Flip the card between the front and back.down keyup key

H key: Show hint (3rd side).h key

A key: Read text to speech.a key


Play button


Play button




Click to flip

21 Cards in this Set

  • Front
  • Back
Regression models
control for confounding using math models
Choice of Regression
4 chars of Proportional Hazards (Cox's)
1) Binary outcome
2) Variable follow-up
3) Start and end time knows for individuals
4) Assumptions are satisfied
Effect estimate of Cox's
exp(b) estimates the incidence rate ratio.
3 chars of Logistic Regression
1) Binary outcome
2) Fixed/defined follow-up
3) Binomial assumptions are satisfied
Effect estimation of logistic regression
exp(b) = odds ratio (risk ratio if disease is rare)
4 chars of Poisson Regression
1) Binary outcome
2) variable follow-up
3a) start and end times known for individuals or
3b) data stratified into mututally exclusive groups according to E and confounder, number of disease cases and follow-up time are knows for these strata
4) Rare disease (and other Poisson assumptions)
Effect estimate of Poisson Regression
exp(b) = incidence rate ratio
3 chars of Linear Regression
1) Continuous outcome (BP, LDL)
2) Followup is fixed/defined
3) Linear regression assumptions satisfied
Effect estimation of Linear regression
b, the estimated regression coefficient is a difference in mean values
7 Issues to consider for model construction
1) Model Type
2) Independence observations
3) Disease variable scale (continuous)
4) Covariate Definitions
5) Building model (selection)
6) Additivity
7) Unknowns
Pitfalls for Model Type
-logistic regression for variable follow-up
-Assuming OR is RR when disease NOT rare
Pitfalls for Independence observations
-wrong unit analysis (BP rather than people_
-Ignores tight matching
-time series/growth curve data
Pitfalls for Disease variable scale (continuous)
Need to log transform or other transform
3 issues of covariate definitions
1) Categorical vs. continuous
2) Categories
3) Scale (if continuous)
Pitfalls for Categorical vs. Continuous
Continuous covars when relationship nonlinear (should inspect data)
Pitfalls for Categories
Torturing the data (pick quintiles)
Pitfalls for scale (if continuous)
covariates w/ extreme variability (consider log-transform)
Pitfalls for building models
-automatic selection algorithm
(consider including demographics and known risk factors)
Pitfalls for Additivity
-effect modification
Pitfalls for unknowns
failure to consider