Revealing Facts and Avoiding Biases: A Review of Several Common Problems in Statistical Analyses of Epidemiological Data
This paper reviews 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,...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Book |
| Published: |
Frontiers Media S.A.,
2016-10-01T00:00:00Z.
|
| Subjects: | |
| Online Access: | Connect to this object online. |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This paper reviews 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. |
|---|---|
| Item Description: | 2296-2565 10.3389/fpubh.2016.00207 |