Abstract
A comparison is made between two different approaches to the linear logistic regression analysis of retrospective study data: the prospective model wherein the dependent variable is a dichotomous indicator of case/control status; and the retrospective model wherein the dependent variable is a binary or polychotomous classification of exposure. The two models yield increasingly similar estimates of the relative risk with increasing degrees of covariate adjustment. When the covariate effects are saturated with parameters, the relative risk estimates are identical. The prospective model is generally to be preferred for studies involving multiple quantitative risk factors.
Keywords
Related Publications
Biased Estimates of Treatment Effect in Randomized Experiments with Nonlinear Regressions and Omitted Covariates
Certain important nonlinear regression models lead to biased estimates of treatment effect, even in randomized experiments, if needed covariates are omitted. The asymptotic bias...
Variable selection – A review and recommendations for the practicing statistician
Abstract Statistical models support medical research by facilitating individualized outcome prognostication conditional on independent variables or by estimating effects of risk...
Computing Distributions for Exact Logistic Regression
Abstract Logistic regression is a commonly used technique for the analysis of retrospective and prospective epidemiological and clinical studies with binary response variables. ...
Imputing missing covariate values for the Cox model
Abstract Multiple imputation is commonly used to impute missing data, and is typically more efficient than complete cases analysis in regression analysis when covariates have mi...
The analysis of binary longitudinal data with time independent covariates
This paper considers extensions of logistic regression to the case where the binary outcome variable is observed repeatedly for each subject. We propose two working models that ...
Publication Info
- Year
- 1978
- Type
- article
- Volume
- 34
- Issue
- 1
- Pages
- 100-100
- Citations
- 93
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.2307/2529594