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]
Re: st: Odd ratio / relative risk in logistic regression
From
David Hoaglin <[email protected]>
To
[email protected]
Subject
Re: st: Odd ratio / relative risk in logistic regression
Date
Tue, 9 Apr 2013 07:27:24 -0400
Hi, Wong.
When you screen variables in Step 1, p < .25 is a better threshold for
including a variable in the logistic regression model. "Any variable
whose univariate test has a p-value < 0.25 is a candidate for the
multivariable model along with all variables of known clinical
importance." (Hosmer and Lemeshow 2000, p. 95) This strategy allows
combinations of variables to make a significant contribution to the
multivariable model when their individual relations to the outcome do
not achieve significance.
I am reluctant to comment on your output without seeing the exact
command that produced it. I have used -logit- (which reports
coefficients) and -logistic- (which reports odds ratios), but not
-binreg-.
David Hoaglin
Hosmer DW, Lemeshow S (2000). Applied Logistic Regression, 2nd ed.
John Wiley & Sons.
On Tue, Apr 9, 2013 at 12:06 AM, Ching Wong
<[email protected]> wrote:
> Hi,
>
> My analysis involves two steps:
>
> 1. Chi-square testing:
> I did a few chi-sqare testing with different variables.
> -tab grade var1, chi2
> -tab grade var2, chi2
> -tab grade var 3, ch2 etc.
> Basesd on the result of the chi-sqaure testings, the variables which
> are significant (i.e. p<0.05) will then put into the logistic
> regression.
>
> 2. logistic regression:
> I put the command as followings:
> - binreg grade var1 var3 var4 etc.
> And I have got the following output.
>
> Iteration 1: deviance = 113.0721
> Iteration 2: deviance = 92.10798
> Iteration 3: deviance = 87.45499
> Iteration 4: deviance = 86.88055
> Iteration 5: deviance = 86.86395
> Iteration 6: deviance = 86.86393
> Iteration 7: deviance = 86.86393
> Generalized linear models No. of obs = 297
> Optimization : MQL Fisher scoring Residual df = 294
> (IRLS EIM) Scale parameter = 1
> Deviance = 86.86392755 (1/df) Deviance = .2954555
> Pearson = 311.8670508 (1/df) Pearson = 1.060772
> Variance function: V(u) = u*(1-u/1) [Binomial]
> Link function : g(u) = ln(u/(1-u)) [Logit]
> BIC = -1587.093
> ------------------------------------------------------------------------------
> | EIM
> grade | Coef. Std. Err. z P>|z| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> var1 | 2.955512 1.066853 2.77 0.006 .8645186 5.046506
> var4| .4058033 1.07797 0.38 0.707 -1.70698 2.518587
> _cons | -4.464928 .6125685 -7.29 0.000 -5.665541 -3.264316
> ------------------------------------------------------------------------------
>
>
> In this case, I can tell var 1 is significant in the logistic
> regression model, since it has a p-value =0.006. However, how can I
> find out the odd ratio or the relative risk of this model? Did I use
> the wrong command?
*
* 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/