Linear, nonlinear or categorical: How to treat complex associations in regression analyses? Polynomial transformations and fractional polynomials

International Journal of Public Health

Volumn 58, Issue 1, 2013, Pages 157-160

  Schmidt, Carsten Oliver ^a   Ittermann, Till ^a   Schulz, Andrea ^b   Grabe, Hans J ^b   Baumeister, Sebastian E ^a  

^a UNIVERSITY MEDICINE GREIFSWALD   (Germany)

^b UNIVERSITY OF GREIFSWALD   (Germany)

Author keywords

Categorization; Fractional polynomials; Linear models; Nonlinear modelling; Polynomials; Regression analysis

Indexed keywords

HUMAN; NONLINEAR SYSTEM; REGRESSION ANALYSIS; REVIEW; STATISTICAL MODEL;

HUMANS; LINEAR MODELS; MODELS, STATISTICAL; NONLINEAR DYNAMICS; REGRESSION ANALYSIS;

MLCS; MLOWN;

EID: 84873110778     PISSN: 16618556     EISSN: 1420911X     Source Type: Journal    
DOI: 10.1007/s00038-012-0362-0     Document Type: Article

References (16)

1. Becher H (2005) General principles of data analysis: continuous covariables in epidemiological studies. In: Ahrens W, Pigeot I (eds) Handbook of epidemiology. Springer, Heidelberg, p 595-624
2. Beck N, Jackman S (1998) Beyond linearity by default: generalized additive models. Am J Political Sci 42(2):596-627
3. Burnham KP, Anderson DR (2004) Multimodel inference - understanding AIC and BIC in model selection. Sociol Methods Res 33(2):261-304 (Pubitemid 39519124)
4. Maclure M, Greenland S (1992) Tests for trend and dose response: misinterpretations and alternatives. AmJ Epidemiol 135(1):96-104
5. May S, Bigelow C (2005) Modeling nonlinear dose-response relationships in epidemiologic studies: statistical approaches and practical challenges. Dose Response 3(4):474-490
6. Rothman KJ, Greenland S, Lash T (2008) Modern epidemiology, vol 4. Lippincott Williams & Wilkins, Philadelphia
7. Royston P, Sauerbrei W (2004) A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials. Stat Med 23(16):2509-2525 (Pubitemid 39149264)
8. Royston P, Sauerbrei W (2007) Improving the robustness of fractional polynomial models by preliminary covariate transformation: a pragmatic approach. Comput Stat Data Anal 51(9):4240-4253 (Pubitemid 46584623)
9. Royston P, Sauerbrei W (2008) Multivariable model building: a pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables. John Wiley & Sons, Chichester
10. Royston P, Ambler G, Sauerbrei W (1999) The use of fractional polynomials to model continuous risk variables in epidemiology. Int J Epidemiol 28(5):964-974 (Pubitemid 29525041)
11. Royston P, Altman DG, Sauerbrei W (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 25(1):127-141 (Pubitemid 43136953)
12. Royston P, Sauerbrei W, Becher H (2010) Modelling continuous exposures with a 'spike' at zero: a new procedure based on fractional polynomials. Stat Med 29(11):1219-1227
13. 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. Comput Stat Data Anal 50(12):3464-3485 (Pubitemid 43794958)
14. Schmidt CO, Ittermann T, Schulz A, Grabe HJ, Baumeister SE (2012) Linear, nonlinear or categorical: how to treat complex associations in regression analyses? Splines and nonparametric approaches. Int J Public Health (IJPH-11-41)
15. Volzke H et al (2010) Cohort profile: the study of health in Pomerania. Int J Epidemiol 40(2):294-307
16. Wingenfeld K et al (2010) The German version of the Childhood Trauma Questionnaire (CTQ): preliminary psychometric properties]. Psychother Psychosom Med Psychol 60(11):442-450