## Glossary of Epidemiology: 10) Cohort Analysis: Multivariate analysis

### Deck Info

#### Description

#### Tags

#### Recent Users

#### Other Decks By This User

- 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