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From |
Leonelo Bautista <lebautista@wisc.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: RRR with CI from logit model |

Date |
Tue, 02 Nov 2004 09:05:55 -0600 |

I think Ronan Conroy's advice in this case is very sound. What you are observing may just pertain to the nature of the examples you are using. It may happen with other examples, but you need to use subject matter to judge what your results tell you. On the other hand, I fail to see why you want to calculate relative risks from a logistic model. Isn't the odds ratio from the logistic model a reasonable estimate of the relative risk? If that's the case, as Joseph Coveney suggested, you can use -glm- fam(bin) link(log), or -binreg-. That should allow you to calculate absolute and relative risks with their confidence intervals for any values of your independent variable. I'm not familiar with -nlcom-, but I'd feel inclined to compare the results from -logit- plus -nlcom- with those from -binreg- plus -lincom- and see if they differ. Leonelo Bautista -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Michael Ingre Sent: Tuesday, November 02, 2004 5:47 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: RRR with CI from logit model On 2004-11-02, at 11.35, Ronán Conroy wrote: > Take a step back here. Have you *graphed* your outcome against your > predictor variable? Thanks for your advice. Yes I have graphed it. And there is a squared component that kicks in at about 7 on the scale were probabilities starts to rise dramatically. The graphed probabilities looks fine and are according to theory. The problem is the standard errors in the predicted RRR using -nlcom-. There seem to be a paradoxical relation here: the more extreme the RRR the LESS significant they are. The paradox described above can be found in auto.dta also. Consider a logit model where the probability of a car being foreign is modelled as a function of length. Length is negatively associated with foreign (-.0797353). Using -nlcom- a significant (p<.001) ratio of 1.3 between the predicted probabilities are fond for length=1 vs length=10. When length=1 is compared to length=100 the ratio increase to 764 but is no longer significant (p=.606). Code is listed below: sysuse auto logit foreign length // RRR for length=1 vs length=10 nlcom (exp(1 * _b[length] + _cons) / (1+ exp(1 * _b[length] + _cons))) / /// (exp(10 * _b[length] + _cons) / (1+ exp(10 * _b[length] + _cons))) // // RRR for length=1 vs length=100 nlcom (exp(1 * _b[length] + _cons) / (1+ exp(1 * _b[length] + _cons))) / /// (exp(100 * _b[length] + _cons) / (1+ exp(100 * _b[length] + _cons))) // I might be doing something I shouldn't and I'm happy for any advice on how to calculate RRRs with CI from the logit model above using auto.dta. Michael On 2004-11-02, at 11.35, Ronán Conroy wrote: > Michael Ingre wrote: > >> A follow up on statistical power. >> >> I have calculated a few RRRs and an interesting pattern is emerging. >> Extreme comparisons give insignificant p-values but others don't. >> >> RRR for 9.5 vs 1, p=.669 >> RRR for 9.5 vs 9, p=.030 >> RRR for 2 vs 1, p=.049 >> >> Predicted absolute probabilities are: 9.5=.33 , 9=.14, 2=.000020 & >> 1=.000015 >> >> What is going on here? Am I doing something wrong? I appreciate any >> suggestion because this makes no sense to me. > > Take a step back here. Have you *graphed* your outcome against your > predictor variable? Use a smoother to have a look at the shape of the > relationship. I sometimes use -autosmoo-, but usually do this sort of > thing in JMP, where you can vary the smoothness of a spline > interactively. It is handy to know if there is a threshold effect > (above a critical value, risk begins to rise) or even a 'normal > region' phenomenon, whereby risk is lowest in some normal region, and > rises at the high and low extremes (weight and health is a classic > example). > You may also be the victim of small numbers in some of the categories. > > But relative risk ratios are a way of measuring a phenomenon. The > first thing to do is to inspect the phenomenon personally, using the > Mk I intra-ocular traumatic test. > > Ronan M Conroy (rconroy@rcsi.ie) Senior Lecturer in Biostatistics > Royal College of Surgeons Dublin 2, Ireland +353 1 402 2431 (fax 2764) > -------------------- Just say no to drug reps > http://www.nofreelunch.org/ > > * > * For searches and help try: > * http://www.stata.com/support/faqs/res/findit.html > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > ------------------------------------------------ Michael Ingre , PhD student & Research Associate Department of Psychology, Stockholm University & National Institute for Psychosocial Medicine IPM Box 230, 171 77 Stockholm, Sweden * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: RRR with CI from logit model***From:*jmpsouza@usp.br

**References**:**Re: st: RRR with CI from logit model***From:*Michael Ingre <Michael.Ingre@ipm.ki.se>

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