Book contents
- Statistics for Laboratory Scientists and Clinicians
- Statistics for Laboratory Scientists and Clinicians
- Copyright page
- Contents
- Preface
- Acknowledgments
- I Basic Statistical Concepts
- II The Right Statistical Test for Different Types of Data
- 4 Analyzing Continuous Data
- 5 Analyzing Non-normally Distributed, Continuous Data: Non-parametric Tests
- 6 Analyses for Non-continuous Data
- 7 Analyzing a Combination of Data Types When the Outcome is Binary
- III Applied Statistics
- Glossary
- Figure Credits
- Index
7 - Analyzing a Combination of Data Types When the Outcome is Binary
from II - The Right Statistical Test for Different Types of Data
Published online by Cambridge University Press: 17 June 2021
- Statistics for Laboratory Scientists and Clinicians
- Statistics for Laboratory Scientists and Clinicians
- Copyright page
- Contents
- Preface
- Acknowledgments
- I Basic Statistical Concepts
- II The Right Statistical Test for Different Types of Data
- 4 Analyzing Continuous Data
- 5 Analyzing Non-normally Distributed, Continuous Data: Non-parametric Tests
- 6 Analyses for Non-continuous Data
- 7 Analyzing a Combination of Data Types When the Outcome is Binary
- III Applied Statistics
- Glossary
- Figure Credits
- Index
Summary
One commonly used analytic technique that examines predictors of a binary outcome (disease/no disease, test positive/test negative, etc.) is called a logistic regression. As it is for other types of regression analyses, the final set of predictors that are added to the regression equation must all be present for each case that is included in the final analysis pool. The most important point when building a model is not to enter all variables in a haphazard fashion. There are specific steps to arriving at the final set of predictors.
- Type
- Chapter
- Information
- Statistics for Laboratory Scientists and CliniciansA Practical Guide, pp. 97 - 110Publisher: Cambridge University PressPrint publication year: 2021