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Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs

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  • Sauerbrei, W.
  • Meier-Hirmer, C.
  • Benner, A.
  • Royston, P.

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  • Sauerbrei, W. & Meier-Hirmer, C. & Benner, A. & Royston, P., 2006. "Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3464-3485, August.
    • Handle: RePEc:eee:csdana:v:50:y:2006:i:12:p:3464-3485
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    References listed on IDEAS

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    1. Patrick Royston & Gareth Ambler, 1999. "Multivariable fractional polynomials," Stata Technical Bulletin, StataCorp LP, vol. 8(43).
    2. Lausen, Berthold & Schumacher, Martin, 1996. "Evaluating the effect of optimized cutoff values in the assessment of prognostic factors," Computational Statistics & Data Analysis, Elsevier, vol. 21(3), pages 307-326, March.
    3. Patrick Royston, 2005. "Multivariable regression models with continuous covariates, with a practical emphasis on fractional polynomials and applications in clinical epidemiology," German Stata Users' Group Meetings 2005 01, Stata Users Group.
    4. Patrick Royston & Douglas G. Altman, 1994. "Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(3), pages 429-453, September.
    5. Willi Sauerbrei, 1999. "The Use of Resampling Methods to Simplify Regression Models in Medical Statistics," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(3), pages 313-329.
    6. W. Sauerbrei & P. Royston, 2002. "Corrigendum: Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 399-400, June.
    7. W. Sauerbrei & P. Royston, 1999. "Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 71-94.
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    2. Dunkler, Daniela & Sauerbrei, Willi & Heinze, Georg, 2016. "Global, Parameterwise and Joint Shrinkage Factor Estimation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i08).
    3. Jinwook Lee & András Prékopa, 2013. "Properties and calculation of multivariate risk measures: MVaR and MCVaR," Annals of Operations Research, Springer, vol. 211(1), pages 225-254, December.
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    6. Strasak, Alexander M. & Umlauf, Nikolaus & Pfeiffer, Ruth M. & Lang, Stefan, 2011. "Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1540-1551, April.
    7. Carsten Schmidt & Till Ittermann & Andrea Schulz & Hans Grabe & Sebastian Baumeister, 2013. "Linear, nonlinear or categorical: how to treat complex associations in regression analyses? Polynomial transformations and fractional polynomials," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 58(1), pages 157-160, February.
    8. Xinhua Mao & Changwei Yuan & Jiahua Gan & Shiqing Zhang, 2019. "Risk Factors Affecting Traffic Accidents at Urban Weaving Sections: Evidence from China," IJERPH, MDPI, vol. 16(9), pages 1-17, May.
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    10. Forshaw, Rachel & Ölkers, Tim & Sethi, Ritika & Sovani, Manali, 2023. "A Comment on Dincecco et al. (2022): Pre-Colonial Warfare and Long-Run Development in India," I4R Discussion Paper Series 35, The Institute for Replication (I4R).
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