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

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: Adjusting for age in logistic regression |

Date |
Thu, 11 Nov 2004 09:00:57 -0600 |

Including age in your model will adjust for that variable. Now, Maarten points to an issue that goes beyond what would be the reasonable age of subjects included in your study. I mean, you should check whether age is linearly related to the log odds of disease before including it in your model as a continuous variable. You can do this easily by using -lintrend-. If age is not linearly related to the log odds of disease, then one option would be to categorize age and use dummy variables. Also, depending on what you are studying and your sample size, it may make sense to adjust by age as a categorical variable. Maarten argues that after including age in your model, you can still have "problems". I reckon he is referring to residual confounding (confounding by unmeasured or poorly measured variables). Since there will always be unmeasured potential confounders and there will always be some error in our measurements (at list in the context of epidemiologic studies), residual confounding is something one can argue in practically any situation. The key point here is whether residual confounding is likely to be so large as to invalidate your conclusions. Leonelo E. Bautista University of Wisconsin Medical School Population Health Sciences -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of maartenbuis Sent: Thursday, November 11, 2004 4:16 AM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Adjusting for age in logistic regression Peter: There are two issues here: First, should age be added as a single linear variables. Lets say you are interested in whether or not a women gets a child, and the variable age has a range of 0-100 years. Adding it as a linear term would clearly be nonsense. In this case I would start with creating a series of dummies: say 0-12, 12-18, 18- 30, >30. So how you want to include a variable depends on how you think the variable effects the probability of experiencing the event (= your theory). Second, some recent posts have suggested that there are still problems after you correctly entered the controll variable: see http://www.stata.com/statalist/archive/2004-10/msg00694.html Maarten > I am doing a logistic regression to determine the effect of risk > factors to the outcome. What I want to do is adjust for age and I > am including the age variable (continuous) into the model. What I > am not sure of is if this is the right approach. * * 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/

**References**:**Re: st: Adjusting for age in logistic regression***From:*"maartenbuis" <maartenbuis@yahoo.co.uk>

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