Education 231C

Applied Categorical & Nonnormal Data Analysis

Conditional Logistic Models

In all of the examples so far, the observations have been independent. But what if the observations were matched or even repeated? You might think that it would possible to include dummy coded variables to indicate the matching. For example, if you had 56 matched pairs you could include 55 dummy variables to account for non-independence along with whatever covariates you wanted to have in the model. Logistic regression has problems when the number of degrees of freedom is close to the total degrees of freedom available. In a situation, such as this, the conditional logistic model is recommended.

Conditional logistic regression, also known as fixed effects logistic regression, is designed to work with matched subjects or repeated measures. Stata's clogit command will work with 1:1 matching, 1:k matching and repeated measures models. The repeated measures models are also called panel models or cross-sectional time-series models.

Example 1: 1-1 Matching

This example is adapted from Hosmer & Lemeshow (2000). Mothers of low birth weight babies were matched by age with mothers of normal weight babies. Low birth weight is defined as less than 2500 grams. The variable, pairid, indicates with mother were matched.

Example 2: 1-M Matching

Here is another example from Hosmer and Lemeshow which involves a 1-M matching. In this case, there are three controls matched with each diagnosed case of breast cancer.

Categorical Data Analysis Course

Phil Ender