st: transformation of a continuous variable for a logistic regressionmodel

[Suzy <scott_788@wowway.com>]

I am trying to transform one final continuous independent variable (age) 
in a logistic regression model. I've tried what I know that's available 
via Stata. For example, I used the fracpoly command and the best 
transformation was a second order polynomial with powers 3 3.

Fractional polynomial model comparisons:
---------------------------------------------------------------
age              df       Deviance      Gain   P(term) Powers
---------------------------------------------------------------
Not in model      0       2098.129        --     --
Linear            1       1834.224     0.000    0.000  1
m = 1             2       1805.957    28.267    0.000  -1
m = 2             4       1791.327    42.897    0.001  3 3
m = 3             6       1790.526    43.699    0.670  -2 3 3
m = 4             8       1788.431    45.793    0.351  -2 -2 3 3
---------------------------------------------------------------


I then used fracgen to generate the new age variables - age_1 and age_2.

fracgen age 3 3
-> gen double age_1 = X^3 
-> gen double age_2 = X^3*ln(X) 
  (where: X = (age+1)/10)





The coefficients for age_1 and age_2 from the full logistic regression 
model:
------------------------------------------------------------------------------
      Y var | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. 
Interval]
-------------+----------------------------------------------------------------
      age_1 |   1.087994   .0093302     9.83   0.000      1.06986    
1.106436
      age_2 |   .9644247   .0037538    -9.31   0.000     .9570955    
.9718101


However the boxtid command rejected the null for both age_1 and age_2....

 age_1    |   .0100805   .0007172     14.055   Nonlin. dev. 24.646  (P 
= 0.000)
       p1 |   .0535714   .2122906      0.252
------------------------------------------------------------------------------
 age_2    |  -.0021756   .0004885     -4.453   Nonlin. dev. 7.894   (P 
= 0.005)
       p1 |   3.864227   2.133377      1.811


In all other respects, the preliminary diagnostics look good...

Linktest:
------------------------------------------------------------------------------
      dmcat |      Coef.   Std. Err.      z    P>|z|     [95% Conf. 
Interval]
-------------+----------------------------------------------------------------
       _hat |   .8900851   .1153855     7.71   0.000     .6639337    
1.116236
     _hatsq |  -.0319886   .0307101    -1.04   0.298    -.0921793    
.0282022
      _cons |  -.0450195   .1069617    -0.42   0.674    -.2546606    
.1646215
------------------------------------------------------------------------------
lroc

Logistic model for dmcat

number of observations =     3354
area under ROC curve   =   0.8647

etc...etc...etc...

My question is should I be concerned with the results of the Boxtid 
command? Is there something I've done incorrectly or something else I 
can do/should do?

Thanks for any help or insight on this.

Suzy



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