Predicting Freeway Crashes from Loop Detector Data by Matched Case-Control Logistic Regression

Truncated logistic regression for matched case-control studies using data from vision screening for school children

Matching is a methodology applied at the beginning of a study that compares two or more groups. The main advantage for matching over random non-matching sampling is that matched study designs can often lead to a more statistically efficient analysis. The most commonly used methods to analyse the matched pair dataset are logistic regression models. The main advantage of using logistic regression...

Unconditional or Conditional Logistic Regression Model for Age-Matched Case–Control Data?

Matching on demographic variables is commonly used in case-control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case-control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching d...

Logistic Regression: Predicting Counts

For the most part, this book concerns itself with measurement data and the corresponding analyses based on normal distributions. In this chapter and the next we consider data that consist of counts. Elementary count data were introduced in Chapter 5. Frequently data are collected on whether or not a certain event occurs. A mouse dies when exposed to a dose of chloracetic acid or it does not. In...

Penalized Logistic Regression in Case-Control Studies

Likelihood-based inference of odds ratios in logistic regression models is problematic for small samples. For example, maximum-likelihood estimators may be seriously biased or even non-existent due to separation. Firth proposed a penalized likelihood approach which avoids these problems. However, his approach is based on a prospective sampling design and its application to case-control data has...

Exact Logistic Regression for a Matched Pairs case-Control Design with Polytomous Exposure Variables