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From |
"Jan Brogger" <[email protected]> |

To |
"Statalist" <[email protected]> |

Subject |
st: adjust prevalence (explained) |

Date |
Wed, 28 Aug 2002 10:57:08 +0200 |

I recently posted on difficulties with adjust. After some experimentation, I've come to the conclusion that -adjust- can't be used. It is not a Stata problem, but a conceptual problem. This applies to the common practice of "adjustment" of binary data with logistic regression. Often, one wants to adjust prevalences to one particular population (the target population). In this post, I'll explain why adjust can't be used. In the next post, I'll explain how I think it should be done. Comments welcome. -adjust <covar1> , by(<covar2>) pr - After logistic regression, this will make the prevalences of the outcome by <covar2> comparable. They will not, however, refer to any conceptually simple population. It should be possible to make adjust refer to one particular population, by using -Adjust <covar1>=<covar1_mean> <covar2=covar2_mean>, by(<covar3>) pr - It seems intuitively correct that you should get the correct answers if you adjust to the covariate means of the target population. However, it is not. If the population you want to adjust to has 50% women, then using -adjust sex=0.5 , by(time) - will not give the correct answers. If you have a single binary covariate to adjust for, the required value to adjust to is: <covar1_mean> = (ln(target_prev/(1-target_prev))-cons)/covar1_coeff where you have to supply the target prevalence (the original prevalence in the target population), and terms from the logistic regression. AFAIK, this solution does not generalize to more than one predictor, and thus has very limited scope. The following code is an example. In code not shown here, I've tried avoiding adjust, using predict, and even avoiding predict by computing the logit's directly from the coefficients. It doesn't matter. The code sets up a population with just a single time point. The original prevalence is 25%. We will try to get this original prevalence back after a logistic regression. clear input sex asthma freq 0 0 45 0 1 5 1 0 30 1 1 20 end expand freq logit asthma sex , or gen all=1 * This next -adjust- adjust to some strange population adjust sex , by(all) pr * This next -adjust- seems intuitive but doesn't work adjust sex=0.5, by(all) pr * This gives the original prevalence, from the formula above adjust sex=0.6131472, by(all) pr Yours sincerely, Jan Brogger, Institute of Medicine, University of Bergen, Norway * * 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/

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