Re: st: ln transform and box cox

[Anthony Fulginiti <fulginit@usc.edu>]

Although I did not post the initial question, I welcome the helpful book reference.  I have been using the Singer and Willett book, "Applied Longitudinal Data Analysis" but looked at Fitzmaurice website and found it to be another great source.  Related to the post:  I followed a similar modeling strategy as Tom (specifying the polynomial function of time that best fit the data first and subsequently adding variables of central interest and controls) but plan to reexamine the issue with the explanatory vars in the model.  Thanks again for the interesting correspondence.  Sincerely, Anthony


On Mar 6, 2013, at 2:31 PM, David Hoaglin wrote:

> Tom,
> 
> You should carefully consider whether a quadratic is an appropriate
> and adequate summary of the contribution of age.  Many nonlinear
> relations are not well approximated by a quadratic.  The choice of
> "overall quadratic growth" on Slide 5 in the presentation by Gutierrez
> is not very convincing.  Also, on Slide 32, part of "the problem with
> the fixed-effects approach" is the choice of 60 linear segments.
> 
> Also, you should examine the choice of functional form for age in the
> context of the model that contains all the explanatory variables that
> you plan to use.  The adjustments for those explanatory variables may
> affect the apparent pattern of the relation of the dependent variable
> to age.
> 
> Applied Longitudinal Analysis by Fitzmaurice, Laird, and Ware is a
> very useful book.  The second edition came out in 2011.  Chapter 13 in
> the first edition became Chapter 16 in the second edition.  The
> companion website has, among other resources, Stata code for many of
> the examples in the book.
> 
> David Hoaglin
> 
> On Wed, Mar 6, 2013 at 12:10 PM, Thomas Norris <T.Norris2@lboro.ac.uk> wrote:
>> Rebecca,
>> Thanks for your input and for the slides.
>> 
>> It is natural to observe increased variation between (human) fetuses as gestation progresses and this is why I logged the weight variable (and to help the models converge). The fracpoly command returned powers of 1 and 2 when weight was on the log scale. My analysis thus far has been to try and obtain the best fitting age terms for the data and then I was going to add explanatory variables to the model (ethnicity, sex, maternal height & weight).
>> 
>> Tom
> 
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