Transcription of 11 Logistic Regression - Interpreting Parameters
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11 Logistic Regression - Interpreting Parameters
11 Logistic Regression - Interpreting PARAMETERS11 Logistic Regression - Interpreting ParametersLet us expand on the material in the last section, trying to make sure we understand the logisticregression model and can interpretStataoutput. Consider first the case of a single binary predictor,wherex={1 if exposed to factor0 if not,andy={1 if develops disease0 does can be summarized in a simple 2 X 2 contingency table asExposureDisease101 (+)ab0 ( )cdwhere OR=adbc(why?) and we interpret OR >1 as indicating a risk factor, and OR <1 asindicating a protective the Logistic model :p(x) is the probability of disease for a given value of x, andlogit(p(x)) = log(p(x)1 p(x))= + for x = 0 (unexposed), logit(p(x)) = logit(p(0)) = + (0) = x = 1 (exposed),logit(p(x)) = logit(p(1)) = + (1) = + Also,odds of disease among unexposed:p(0)/(1 p(0))exposed:p(1)/(1 p(1))NowOR=odds of disease among exposedodds of disease among unexposed=p(1)/(1 p(1))p(0)/(1 p(0))and = logit(p(1)) logit(p(0))= log(p(1)(1 p(1))) log(p(0)(1 p(0)))= log(p(1)/(1 p(1))p(0)/(1 p(0)))= log(OR)The Regression coefficient in the population model is the log(OR), hence theORis obtained byexponentiating ,e =elog(OR)=ORRemark:If we fit this simple Logistic model to a 2 X 2 table, the estimated unadjustedOR(above)and the Regression coefficient for x have the same :Leukemia Survival Data (Section 10 p.)}}
A More Complex Model log ‡ p 1¡p · = fi+fl1x1 +fl2x2, where x1 is binary (as before) and x2 is a continuous predictor. The regression coefficients are adjusted log-odds ratios. To interpret fl1, fix the value of x2: For x1 = 0 log odds of disease = fi +fl1(0)+fl2x2 = fi +fl2x2 odds of disease = efi+fl2x2 For x1 = 1
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