Epidemiology: 10) Cohort Analysis: Multivariate analysis
Terms
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- Regression models
- control for confounding using math models
- Choice of Regression
- (10-5)
- 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
-colinearity
(consider including demographics and known risk factors) - Pitfalls for Additivity
- -effect modification
- Pitfalls for unknowns
- failure to consider