st: reporting log linked, linear, and fractional polynomial results

 From Buzz Burhans <[email protected]> To [email protected] Subject st: reporting log linked, linear, and fractional polynomial results Date Thu, 23 Oct 2003 08:36:53 -0400

I would appreciate advice on effective ways of reporting on data for similar outcome types from the same trial which have been modeled using different link functions. The outcomes (plasma metabolites in animals under two treatment regimes) are repeated measurements made over time proximate to parturition, and have variously different profiles of curvilinear increase or decrease. Simply fitting linear polynomial models failed to adequately satisfy assumptions for residuals for some (but not all) outcomes, so while some were modeled as normally distributed with the identity link, others were modeled using gllamm with family(gamma) link(log), all using adaptive quadrature. I have used logistic regression previously with categorical outcomes, but am unclear about the log link to continuous variables. My questions are as follows:

1. How to report the results from disparate model types. My initial thought is to 1) tabulate fitted values and confidence intervals at a set of representative times, with stars for significance of treatment difference , and accompany such a table with plots of fixed effect values (over the entire experimental period).

Does this make sense? I am not sure how to otherwise tabulate coefficients and se, since some refer to outcomes in the original metric, while the log linked ones refer to logged outcomes .

I also considered exponentiating the coefficients and ci, but confess to being a bit unsure about how to express their exponentiated interpretation, given that they are relative to continuous rather than categorical dependant variables. Is it appropriate to suggest that the exponentiated coefficient describes the proportionate change in the (backtransformed) outcome?

The reporting is a bit further complicated by the fact that some of the dependant variables are fractional polynomial transformations as well, which helped construct a good model, but complicate expressing the interpretation.

In the past I have modeled such data using linear or nonlinear mixed effect models in S-Plus (lme and nlme), and reported separate tables for variables modeled using each; use of nonlinear models makes interpretation of the coefficients more intuitive; however, I wanted to stay in Stata, and there are not yet random effects models in Stata that can handle mixed random and fixed slopes other than gllamm, and I am unable to fit nonlinear models in gllamm.

I am appreciative of any thoughts on these matters.

Buzz Burhans

Buzz Burhans
[email protected]

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