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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Appropriate modelling - testing which set of exposures are more important |

Date |
Fri, 28 Sep 2012 10:19:24 +0200 |

On Thu, Sep 27, 2012 at 7:46 PM, Amal Khanolkar wrote: > I have two main exposures; maternal ethnicity and maternal socioeconomic position (SEP). > > I want to test which of the above two exposures are more important in determining maternal pregnancy outcomes. > > 1. I plan to use linear regression, as my outcome of interest is continuous. A continuous dependent/outcome/left-hand-side/explained variable is neither a necessary nor a sufficient reason for choosing a linear regression model. > 2. Initially, the first model will test the effect of ethnicity on the outcome, controlling for potential confounders as follows: > > xi: regress outcome i.ethnicity confounder1 confounder2 i.confounder3 > > 3. In the next step, I introduce the second main exposure, maternal SEP: > > xi: regress outcome i.ethnicity confounder1 confounder2 i.confounder3 i.SEP > > 4. I test for an interaction as follows: > > xi: regress outcome i.ethnicity*i.SEP confounder1 confounder2 i.confounder3 > > Questions: If the effects of ethnicity on my outcome of interest change from step2 to step3, controlling for the same confounders in both models, is this enough evidence of one exposure being more important than the other? No, think of it this way: If you started with maternal SEP instead and than added ethnicity than that would also lead to a change in the effect of maternal SEP. So if your argument were correct you could choose which variable is more important by choosing with which variable you started your analysis... As a solution you would need to be more precise about what "more important" means. To me it refers to some form of comparison of effect sizes. Than the trick becomes to create effect sizes for categorical variables that are comparable. One approach is the sheaf coefficient: see: -ssc desc sheafcoef-, <http://www.maartenbuis.nl/wp/prop.html> and the example below. In the example below I would say that occupation and education are about equally important for wage and both are more important than marital status. If you like testing, than you can test these hypotheses, as is shown below. *----------------------- begin example ---------------------- // prepare data sysuse nlsw88, clear recode occupation (11/12=4) /// (9/10=13) gen byte marst = never_married + 2*married label define marst 0 "divorced/widowed" /// 1 "never married" /// 2 "married" label value marst marst gen byte ed = cond(grade < 12, 1, /// cond(grade == 12, 2, /// cond(grade < ., 3, .))) label define ed 1 "less than high school" /// 2 "high school" /// 3 "more than high school" label ed ed // estimate the model // notice wage is (approximately) continuous, // but linear regression is not the best choice xi: glm wage i.occupation i.marst i.ed union ttl_exp, /// link(log) vce(robust) // make the effects of occupation, marital status // and education comparable sheafcoef, latent(occ:_Iocc* ; marst:_Imarst* ; ed:_Ied*) /// eform post // test whether the comparable effects are different test occ_e = ed_e test occ_e = marst_e test ed_e = marst_e *------------------------ end example ----------------------- (For more on examples I sent to the Statalist see: http://www.maartenbuis.nl/example_faq ) -- Maarten --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Appropriate modelling - testing which set of exposures are more important***From:*Amal Khanolkar <Amal.Khanolkar@ki.se>

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