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From |
"Dr. Bill Westman" <webicky@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Logistic regression interpretation |

Date |
Wed, 22 Sep 2010 11:22:45 -0700 |

This is incredibly helpful! after the margins command, we have the four outcomes. The last line (1:1) in your example shows: 1 1 | .4279095 _b[1.married#1.collgrad] in my example 17.87322. How do you interpret the 17.87 margin? Also - those two di statements at show the exact same odds - was this intentional? . di _b[0bn.married#1.collgrad]/_b[0bn.married#0bn.collgrad] 1.6475143 . . di _b[1.married#1.collgrad]/_b[1.married#0bn.collgrad] 1.6475143 Lastly, I read the manual on margins and I am wondering whether I need to use the test command. Thanks for this very excellent email. On Wed, Sep 22, 2010 at 2:06 AM, Maarten buis <maartenbuis@yahoo.co.uk> wrote: > --- On Tue, Sep 21, Dr. Bill Westman wrote: >> > A simple logistic regression was run - outcome - Survived/Non Survived >> > (1/0), with Group(Treatmeant/Control -- 1/0) & Risk(High/Low -- 1/0) >> > >> > The results >> > >> > ---------+------------- >> > Survived | Odds Ratio >> > ---------+------------- >> > Risk | 4.790295 >> > group | 2.111181 >> > ---------+------------- >> > >> > Can I assume that odds of treatment survival were 2.1 times higher in >> > the treatment Group for LOW risk patients? >> > And for High Risk patients in the treatment group is the adds 4. 8 + >> > 2.1 (or 7 times higher)? > > There are no interactions so the odds increases with a factor 2.1 when > one receives the treatment regardless of whether one is in the high or > low risk group (the baseline odds differs between the high and low risk > group, but does not change our result). > > You can see this in the example below. The -margins- command is used in > this example to get the odds (exp(linear predictor)) for each of the > four groups. The -coefl- option is added to show how -margins stores > each of these odds. The -di- commands use those odds to calculate the > odds ratios again. Lets assume that group in your example is collgrad > in mine, and that risk in your example is married in mine. You can see > that without adding the interaction terms the odds ratio is the same > for both married and unmarried people. > > *-------------------- begin example --------------------- > sysuse nlsw88, clear > gen byte baseline = 1 > logit union i.married i.collgrad baseline, or nocons > > margins, exp(exp(xb())) over(married collgrad) post coefl > di _b[0bn.married#1.collgrad]/_b[0bn.married#0bn.collgrad] > di _b[1.married#1.collgrad]/_b[1.married#0bn.collgrad] > *-------------------- end example ------------------------ > (For more on examples I sent to the Statalist see: > http://www.maartenbuis.nl/example_faq ) > >> - Also - Is the command >> >> listcoef, help percent >> >> indicating that the odds of survival are 111% higher for >> Treatment group? > > Yes, and you could have seen that directly from the output of > your -logit- command. If something changes by a factor 2 it > increases 100%, and if something changes by a factor .7 then it > decreases 30%. The general rule to move from a ratio to a percentage > is (ratio - 1) * 100%. You found an odds ratio of 2.11, so the > odds changes (2.11 - 1)*100% = 111% > >> and if so - is that 111% at both High/Low risk Levels? > > Yes, as I explained above. > > Hope this helps, > Maarten > > -------------------------- > Maarten L. Buis > Institut fuer Soziologie > Universitaet Tuebingen > Wilhelmstrasse 36 > 72074 Tuebingen > Germany > > http://www.maartenbuis.nl > -------------------------- > > > > > * > * 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: Logistic regression interpretation***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**Re: st: Logistic regression interpretation***From:*"Dr. Bill Westman" <webicky@gmail.com>

**Re: st: Logistic regression interpretation***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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