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binary regression, RR and RD

# binary regression, RR and RD - LOGISTIC REGRESSION EXAMPLES...

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LOGISTIC REGRESSION EXAMPLES Case control design over-samples cases from the population Cases are higher proportion of sample than they are in the population For logistic model: binary outcome Y indicates case status p= P[Y=1|x’s] is probability a subject in the sample is a case In model: logit (p) = β 0 + β 1 x 1 , β 0 is the value of logit (p) in the unexposed in the sample – higher than it would be for the population β 0 does not have a population interpretation When there is no selection bias (cases and control are sampled without regard to exposure, there is same π in numerator and denominator) Example: In sample p= ½ In population, p=1/10,000 How many times the p is bigger? Sample Odds = π * Population odds Here, π = 10,0000 We have seen that logistic regression provides estimates of ORs : logit (p) = β 0 + β 1 X - Exponentiated coefficients e β are ORs and adjusted ORs - Useful for case-control studies Can also fit regression models to binary data that yield adjusted RDs and RRs - (p) = β 0 + β 1 X for RDs - Log (p) = β 0 + β 1 X for RRs Generalized in two sense 1.

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binary regression, RR and RD - LOGISTIC REGRESSION EXAMPLES...

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