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# RE: st: Looking for courses in non-linear modelling and imputation techniques

 From Nick Cox To "'statalist@hsphsun2.harvard.edu'" Subject RE: st: Looking for courses in non-linear modelling and imputation techniques Date Mon, 10 Oct 2011 18:00:46 +0100

```To echo Maarten: this depends on what field you are in. Some disciplines (biochemistry, pharmacology, ecology, etc.) might well interpret nonlinear modelling in this question as being about non-linear least squares (-nl-, in Stata terms).

Nick
n.j.cox@durham.ac.uk

Maarten Buis

On Mon, Oct 10, 2011 at 6:02 PM, Sofia Ramiro wrote:
> I want to explore non-linear relationships between outcomes that have so far
> been analyzed as if their relationship was linear. For this, I need to learn
> some statistical techniques to explore these relationships (besides normal
> regression, or even generalized estimation equations), if I am not wrong.
> This is more difficult to find in normal courses, at least the ones I have
> been finding, as they focus on linear relationships between variables...

This is actually routinely discussed in introductory regression
courses. The standard remedies depends on the discipline: either one
splits the linear variable up in categories and adds dummies/indicator
variables for those categories or one adds a square term. These
standard remedies tend to either trow away too much information
(dummies) or impose too much structure and thus not fit very well
(square term). Personally, I like adding linear splines (see: -help
mkspline-) as a nice compromise between adding a non-linear effect and
interpretable coefficients. Another option is fractional polynomials
(see: -help fracpoly- and Royston and Sauerbrei 2008).

Hope this helps,
Maarten

Royston, P., and W. Sauerbrei. 2008. Multivariable Model-building: A
Pragmatic Approach to Regression Analysis
Based on Fractional Polynomials for Modelling Continuous Variables.
Chichester, UK: Wiley.

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