The cumulative logit or the proportional odds regression model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds regression model. The methods focus on evaluating...

A standard approach to analyzing n binary matched pairs usually represented in n 2×2 tables is to apply a subject-specific model; for the simplest situation it is the so-called Rasch model. An alternative population-averaged approach is to apply a marginal model to the single 2×2 table formed...

Curved Exponential random graph models (CERGMs) are a popular tool for modeling social networks representing relational data, such as working relations or friend- ships. Additionally to the network itself some exogenous variables are often collected, such as gender, age, etc. CERGMs allow...

Classical methods for fitting a varying intercept logistic regression model to stratified data are based on the conditional likelihood principle to eliminate the stratum-specific nuisance parameters. When the outcome variable has multiple ordered categories, a natural choice for the outcome...

In an individually matched case–control study, effects of potential risk factors are ascertained through conditional logistic regression (CLR). Extension of CLR to situations with multiple disease or reference categories has been made through polychotomous CLR and is shown to be more efficient...