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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Common regression models in epidemiology assume linear relationships for continuous covariates.
  • Categorizing continuous variables is a frequent but limited approach to handle non-linearities.
  • Restricted cubic splines (RCS) provide a flexible alternative for modeling non-linear associations.

Purpose of the Study:

  • To introduce the application of restricted cubic splines (RCS) in regression modeling for epidemiological studies.
  • To present RCS as a flexible extension of covariate categorization.
  • To guide the integration, interpretation, and graphical presentation of RCS in regression analysis.

Main Methods:

  • Application of restricted cubic splines (RCS) for modeling continuous covariates in regression.
  • Focus on model fitting and graphical presentation of exposure-outcome associations.
  • Development of accompanying functions and examples in R, Stata, and SAS.

Main Results:

  • RCS improves model fit for non-linear exposure-outcome relationships.
  • RCS overcomes limitations of covariate categorization in interpretation.
  • Provides tools for robust incorporation of continuous covariates in regression modeling.

Conclusions:

  • Restricted cubic splines (RCS) are a valuable tool for assessing non-linear associations in epidemiological research.
  • The paper provides practical guidance and software for implementing RCS.
  • Facilitates more flexible and robust regression modeling with continuous covariates.