A New Algorithm for Matched Case-Control Studies with Applications to Additive Models

Logistic models are commonly used to analyze matched case-control data. The standard analysis requires the computation of conditional maximum likelihood estimates. We propose a simple algorithm that uses a diagonal approximation for the (non-diagonal) weight matrix deriving from the Newton-Raphson method. The primary purpose of the new algorithm is to exploit iterative reweighted least-squares procedures for fitting general additive rather than simple linear structure.