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From |
David Hoaglin <dchoaglin@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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 <ching.y.wong@student.adelaide.edu.au> 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/

**References**:**st: Odd ratio / relative risk in logistic regression***From:*Ching Wong <ching.y.wong@student.adelaide.edu.au>

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