Semiparametric regression splines in matched case-control studies. Academic Article

abstract

• We develop semiparametric methods for matched case-control studies using regression splines. Three methods are developed: 1) an approximate cross-validation scheme to estimate the smoothing parameter inherent in regression splines, as well as 2) Monte Carlo expectation maximization (MCEM) and 3) Bayesian methods to fit the regression spline model. We compare the approximate cross-validation approach, MCEM, and Bayesian approaches using simulation, showing that they appear approximately equally efficient; the approximate cross-validation method is computationally the most convenient. An example from equine epidemiology that motivated the work is used to demonstrate our approaches.

authors

• Carroll, Raymond
• Cohen, Noah

published proceedings

• Biometrics

author list (cited authors)

• Kim, I., Cohen, N. D., & Carroll, R. J.

citation count

• 11

complete list of authors

• Kim, Inyoung||Cohen, Noah D||Carroll, Raymond J

publication date

• December 2003

publisher

• Wiley  Publisher

published in

• Biometrics  Journal

keywords

• Animals
• Biometry
• Case-Control Studies
• Colic
• Computer Simulation
• Horse Diseases
• Horses
• Monte Carlo Method
• Regression Analysis

PubMed Central ID

• 14969497

Digital Object Identifier (DOI)

• 10.1111/j.0006-341x.2003.00133.x

start page

• 1158

end page

• 1169

volume

• 59

issue

• 4

URL

• http://dx.doi.org/10.1111/j.0006-341x.2003.00133.x