Adaptive Modeling: An Approach for Incorporating Nonlinearity in Regression Analyses. Issue 3 (1st February 2017)

• Record Type: Journal Article
• Authors: Knafl, George J.; Barakat, Lamia P.; Hanlon, Alexandra L.; Hardie, Thomas; Knafl, Kathleen A.; Li, Yimei; Deatrick, Janet A.
• Is Part Of: Research in nursing & health. Volume 40:Issue 3(2017)
• Page Start: 273
• Page End: 282
• Publication Date: 2017-02-01
• Subjects: adaptive regression -- fractional polynomials -- moderation -- non‐constant variance; Nursing -- Research -- Periodicals; Nursing -- Periodicals; 610.7305
• DOI: 10.1002/nur.21786
• Languages: English
• ISSNs: 0160-6891

Abstract

Abstract: Although regression relationships commonly are treated as linear, this often is not the case. An adaptive approach is described for identifying nonlinear relationships based on power transforms of predictor (or independent) variables and for assessing whether or not relationships are distinctly nonlinear. It is also possible to model adaptively both means and variances of continuous outcome (or dependent) variables and to adaptively power transform positive‐valued continuous outcomes, along with their predictors. Example analyses are provided of data from parents in a nursing study on emotional‐health‐related quality of life for childhood brain tumor survivors as a function of the effort to manage the survivors' condition. These analyses demonstrate that relationships, including moderation relationships, can be distinctly nonlinear, that conclusions about means can be affected by accounting for non‐constant variances, and that outcome transformation along with predictor transformation can provide distinct improvements and can resolve skewness problems.© 2017 Wiley Periodicals, Inc.