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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: CI |

Date |
Mon, 19 Dec 2011 10:25:57 +0100 |

On Mon, Dec 19, 2011 at 7:37 AM, Newton Chagoma Chagoma wrote: > I done a cox regression and one of the variables"kps" has a confidence > interval ranging from 0 to a dot(.) > > say Risk factor HR p-values 95% CI > Kps 1.49 0.999 0, . > > what do I have to say about it?or rather explain and report it. The dot stands in this case for +infinity, so it means that your estimate is completely meaningless: A confidence interval of 0 to infinity for a ratio is the same as a confidence interval from -infinity to +infinity for a difference. It is true that the ratio lies somewhere between 0 and +infinity, but we did not need to do a statistical analysis to find that out. In practice it means that there is a serious problem with your model that you must fix. You told us exactly nothing about your problem, so it is hard to diagnose the problem. I can think of two possibilities: Things like these can happen when you have covariates on wildly different scales. I would look at all variables in your model and consider its scale: e.g. don't add annual income in euros but in 1000s of euros. Also consider the origin: don't add year of birth, but year of birth - 1950. Changing the scale (in the example above, divide by 1000) or the origin (in the example above, subtract 1950), or a combination of the two (for example: year of birth in decades since 1950), are linear transformations, so they do not change the model. For example saying that someone is born 27 years after 1950 is exactly the same as saying someone is born 1975 years after 0, or saying someone earns .5 of 1000 euros more is the same as saying that someone earns 500 euros more. However, computers work better if the numbers don't get too large or too small. Another possible cause is multi-colinearity. This would mean that you need to drop variables or impose other constraints. For example, say you entered the education of the father and the mother. These tend to be highly correlated. If the correlation is too high, you can drop one of the parents, which is often done in my sub-discipline. Alternatively, you can keep both parents but constrain the effects to be the same. It depends on the problem, which solution would make most sense. 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/

**Follow-Ups**:**Re: st: CI***From:*Maarten Buis <maartenlbuis@gmail.com>

**References**:**st: CI***From:*Newton Chagoma Chagoma <nchagoma@gmail.com>

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