Abstract
This paper reviews several common challenges encountered in statistical analyses of epidemiological data for epidemiologists. We focus on the application of linear regression, multivariate logistic regression, and log-linear modeling to epidemiological data. Specific topics include: (a) deletion of outliers, (b) heteroscedasticity in linear regression, (c) limitations of principal component analysis in dimension reduction, (d) hazard ratio vs. odds ratio in a rate comparison analysis, (e) log-linear models with multiple response data, and (f) ordinal logistic vs. multinomial logistic models. As a general rule, a thorough examination of a model's assumptions against both current data and prior research should precede its use in estimating effects.
Original language | English |
---|---|
Article number | 207 |
Journal | Frontiers in Public Health |
Volume | 4 |
Issue number | OCT |
DOIs | |
State | Published - 7 Oct 2016 |
Externally published | Yes |
Keywords
- Epidemiology
- Hazard ratio
- Log-linear
- Logistic
- Odds ratio
- Principal component analysis
- Regression
- Relative risk