Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down at the end of May, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: ln transform and box cox


From   Anthony Fulginiti <fulginit@usc.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: ln transform and box cox
Date   Wed, 6 Mar 2013 16:15:56 -0800

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
> 
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/faqs/resources/statalist-faq/
> *   http://www.ats.ucla.edu/stat/stata/


*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index