The comparison of alternative smoothing methods for fitting non-linear exposure-response relationships with Cox models in a simulation study

Usha S. Govindarajulu, Elizabeth J. Malloy, Bhaswati Ganguli, Donna Spiegelman, Ellen A. Eisen

Research output: Contribution to journalArticlepeer-review

98 Scopus citations

Abstract

We examined the behavior of alternative smoothing methods for modeling environmental epidemiology data. Model fit can only be examined when the true exposure-response curve is known and so we used simulation studies to examine the performance of penalized splines (P-splines), restricted cubic splines (RCS), natural splines (NS), and fractional polynomials (FP). Survival data were generated under six plausible exposure-response scenarios with a right skewed exposure distribution, typical of environmental exposures. Cox models with each spline or FP were fit to simulated datasets. The best models, e.g. degrees of freedom, were selected using default criteria for each method. The root mean-square error (rMSE) and area difference were computed to assess model fit and bias (difference between the observed and true curves). The test for linearity was a measure of sensitivity and the test of the null was an assessment of statistical power. No one method performed best according to all four measures of performance, however, all methods performed reasonably well. The model fit was best for P-splines for almost all true positive scenarios, although fractional polynomials and RCS were least biased, on average.

Original languageEnglish
Article number2
JournalInternational Journal of Biostatistics
Volume5
Issue number1
DOIs
StatePublished - 2009
Externally publishedYes

Keywords

  • Cox model
  • Fractional polynomial
  • Natural spline
  • Penalized spline
  • Restricted cubic spline
  • Simulation

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