Flexible regression models with cubic splines
Statistics in Medicine1989Vol. 8(5), pp. 551–561
Citations Over TimeTop 10% of 1989 papers
Abstract
We describe the use of cubic splines in regression models to represent the relationship between the response variable and a vector of covariates. This simple method can help prevent the problems that result from inappropriate linearity assumptions. We compare restricted cubic spline regression to non-parametric procedures for characterizing the relationship between age and survival in the Stanford Heart Transplant data. We also provide an illustrative example in cancer therapeutics.
Related Papers
- → Generalized Cross Validation (GCV) in Smoothing Spline Nonparametric Regression Models(2021)20 cited
- → An algorithm for experimental data deconvolution using spline functions(1983)16 cited
- → Multivariate Adaptive Regression Splines(2006)128 cited
- → Multivariate Adaptive Regression Splines(2014)48 cited
- ESTIMATION OF CLEARNESS INDEX MODEL VIA CRS, TPRS AND MARS(2011)