Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at

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

re: Re: st: Alternatives to clogit using generalised additive models (GAM)

From   "Ariel Linden, DrPH" <[email protected]>
To   <[email protected]>
Subject   re: Re: st: Alternatives to clogit using generalised additive models (GAM)
Date   Wed, 21 Dec 2011 10:26:20 -0500

I would add regression splines to Steve's suggestion as well. In fact, the
approach used in -gam- (written by Patrick Royston and Gareth Ambler), is
more akin to splines than polynomials. Patrick has another program you
should consider for this problem - mvrs- which is the multiple variable
alternative to -uvrs- (the univariate model).  


Lambert P, Royston P (2009) Further development of flexible parametric
models for survival analysis. Stata
Journal 9: 265-290.

Royston P, Sauerbrei W (2008) Multivariable model-building. A pragmatic
approach to regression analysis based
on fractional polynomials for modelling continuous variables.
Wiley-Blackwell, Chichester.

Royston P, Sauerbrei W (2007) Multivariable modeling with cubic regression
splines: a principled approach. Stata
Journal 7: 45-70.


Date: Tue, 20 Dec 2011 18:22:27 -0500
From: Steve Samuels <[email protected]>
Subject: Re: st: Alternatives to clogit using generalised additive models

See -fracpoly- and -mfp- which will fit very flexible models in any
regression command, including -clogit-.  "search fractional polynomials,
all" will turn up several contributed commands that enhance -fracpoly- amd

[email protected]

On Dec 20, 2011, at 8:51 AM, Lucas Salas wrote:


I am performing a matched case-control analysis in which my outcome is
binomial (Cancer yes/no) and my exposure is continuous non-normal. I
have already fitted some conditional logistic regression to account
for matching using quartiles as boundaries to overcome the
non-normality. However, I am interested in explore the dose-response
curves and the fitting of my model using a GAM approach. The actual
GAM module for Stata is the Fortran app developed by Hastie and
Tibshirani, which do not allow any fixed effects adjustment
(conditional or unconditional).

I have seen these potential alternatives in different webpages:
1. Introducing a match dummy variable in my GAM model. As you could
expect my strata are rather sparse so this is not helpful.
2. Fitting a Cox model using exact partial likelihood and using the
match as the strata. The logic behind the conditional logistic
regression is ok, but I cannot figure out how this applies to a GAM
model, and how can I extrapolate this to graph the dose response.
3. Using a vectorial GAM to account for the matching. I have not found
any Stata alternative to this procedure.

I'd be very grateful for any potential ideas, or if you know any kind
of user written ado that might be useful for this data.



*   For searches and help try:

© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index