Bookmark and Share

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


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

st: RE: Poisson Regression


From   "Visintainer, Paul" <Paul.Visintainer@baystatehealth.org>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: Poisson Regression
Date   Mon, 14 Feb 2011 13:08:58 -0500

Alexandra,

There is a growing literature on alternatives to logistic regression if the outcome is common. I've attached some of the literature below. Just a quick overview: 

In general, two approaches are suggested: log-binomial and Poisson regression with robust standard errors. The log-binomial approach is preferred, unless the model fails to converge (which if frequently does) (see Petersen & Deddens 2008; Deddens & Petersen 2008).  Stata provides two approaches to log-binomial: -glm- with the family and link specified, and -binreg-, with the rr option. 

I think that Poisson regression with robust standard errors (the robust option) will be used more often in practice because it seldom has problems converging.  Zou (2004) suggests its use (as do Barros & Hirakata 2003) for cohort studies where the relative risk is of interest and the base incidence is common.

Spiegelman & Hertzmark (2005) in a commentary go as far as to recommend that logistic regression not be used for risk or prevalence ratios when the outcome is common.


*************

Wacholder S. Binomial regression in GLIM: estimating risk ratios and risk differences. Am J Epidemiol. Jan 1986;123(1):174-184.

Skov T, Deddens J, Petersen MR, Endahl L. Prevalence proportion ratios: estimation and hypothesis testing. Int J Epidemiol. Feb 1998;27(1):91-95.

McNutt LA, Wu C, Xue X, Hafner JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol. May 15 2003;157(10):940-943.

McNutt LA, Hafner JP, Xue X. Correcting the odds ratio in cohort studies of common outcomes. JAMA. Aug 11 1999;282(6):529.

Zou G. A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology. 2004;159:702-706.

Barros AJ, Hirakata VN. Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Med Res Methodol. Oct 20 2003;3:21.

Deddens JA, Petersen MR. Approaches for estimating prevalence ratios. Occup Environ Med. Jul 2008;65(7):481, 501-486.

Petersen MR, Deddens JA. A comparison of two methods for estimating prevalence ratios. BMC Med Res Methodol. 2008;8:9.

Spiegelman D, Hertzmark E. Easy SAS calculateons for risk or prevalence ratios and differences. Am J Epidemiol. Aug 1 2005;162(3):199-200.


Regards,

Paul F. Visintainer, PhD
Baystate Medical Center
Springfield, MA 01199


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Alexandra Boing
Sent: Sunday, February 13, 2011 3:09 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: Poisson Regression

I would like to know how to proceed and the justication Mathematical and Statistical. My dependent variable is spent on health (0=No   1=Yes). The prevalence was higher than 10 percent. Can I do Poisson regression? According to this paper published in BMC on line in 2003, registred   PMC521200 I can do Poisson regression with variable (0=No  1=Yes) and with prevalence higher than 10 percent, but other authors report that only I can do Poisson regression with the dependent variable= discrete variable and prevalence under 10 percent. 
Which is correct? And what is the explanation Mathematical and Statistical?Thanks, Alexandra


      

*
*   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/

----------------------------------------------------------------------
Please view our annual report at http://baystatehealth.org/annualreport
 

CONFIDENTIALITY NOTICE: This e-mail communication and any attachments may contain confidential and privileged information for the use of the designated recipients named above. If you are not the intended recipient, you are hereby notified that you have received this communication in error and that any review, disclosure, dissemination, distribution or copying of it or its contents is prohibited. If you have received this communication in error, please reply to the sender immediately or by telephone at 413-794-0000 and destroy all copies of this communication and any attachments. For further information regarding Baystate Health's privacy policy, please visit our Internet site at http://baystatehealth.org.

*
*   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/


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