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From |
"Silcocks, Paul" <Paul.Silcocks@liverpool.ac.uk> |

To |
"'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Cox proportional hazard model and the number of parameters for AICc |

Date |
Tue, 6 Dec 2011 09:00:13 +0000 |

Firstly, AICc was derived for Gaussian linear models and doesn't directly apply to generalised linear models or Cox models. Provided you have a "reasonable" events/parameter ratio to begin with, AIC should be ok for model selection. What is "reasonable" is often cited as an events/parameter ratio of 10:1 if you are developing a prediction model from scratch, this ratio can be surprising high (50:1) see Steyerberg EW "Clinical Prediction models" Springer 2009, p198. If the stratification variables are the same from model to model then the strata won't count when calculating AIC, which will just be based on the number of parameters fitted to the (stratified) Cox model. Paul Silcocks BM BCh, MSc , FRCPath, FFPH, CStat Senior statistician, Cancer Research UK Liverpool Cancer Trials Unit University of Liverpool Block C Waterhouse Building 1-3 Brownlow Street L69 3GL email: paul.silcocks@liverpool.ac.uk tel: 0151 7948802 mob: 0794 983 2775 -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Brigham Whitman Sent: 05 December 2011 21:40 To: statalist@hsphsun2.harvard.edu Subject: st: Cox proportional hazard model and the number of parameters for AICc I am using the stcox command to perform a stratified Cox proportional hazard model in Stata. I want to use AICc to determine the best model of a set of candidate models and I cannot figure out how to determine the number of parameters ("K") to use for each model. The models are stratified by sex (male or female) and use 1, 2, or 3 variables. I don't know how to consider a dichotomous variable that the model is being stratified by when determining "K" for the AICc calculation. I would appreciate any help on this subject. I am using Stata/ MP 9.2 and there is an example below of one model (which has 2 variables ("velo_log10" and "dst_to_cover_m")) and its output. Thank you, Brigham Whitman SUNY ESF . stcox velo_log10 dst_to_cover_m, nohr robust strata(sex) failure _d: event == 3 analysis time _t: (mydate-origin) origin: time d(01Jan2006) enter on or after: event==1 exit on or before: event==3 4 id: id Iteration 0: log pseudolikelihood = -24.512423 Iteration 1: log pseudolikelihood = -20.631007 Iteration 2: log pseudolikelihood = -20.563058 Iteration 3: log pseudolikelihood = -20.562172 Iteration 4: log pseudolikelihood = -20.562171 Refining estimates: Iteration 0: log pseudolikelihood = -20.562171 Stratified Cox regr. -- no ties No. of subjects = 34 Number of obs = 2304 No. of failures = 13 Time at risk = 2597 Wald chi2(2) = 9.26 Log pseudolikelihood = -20.562171 Prob > chi2 = 0.0097 (Std. Err. adjusted for 34 clusters in id) ------------------------------------------------------------------------------ | Robust _t | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- velo_log10 | -1.608069 .7634565 -2.11 0.035 -3.104417 -.1117222 dst_to_cov~m | -.0123516 .0126958 -0.97 0.331 -.037235 .0125318 ------------------------------------------------------------------------------ Stratified by sex * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/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/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: Cox proportional hazard model and the number of parameters for AICc***From:*Brigham Whitman <brighamj@gmail.com>

**References**:**st: Cox proportional hazard model and the number of parameters for AICc***From:*Brigham Whitman <brighamj@gmail.com>

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