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*** M-H relative risk+ P) V. O* d; X2 P6 Q
- cs ill exposed [fweight = freq], by(confounder)
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: f. ^$ y) M0 Q# h9 I*** Poisson regression unadjusted
& V% Q, z/ p0 r: e. T- poisson ill exposed confounder [fweight = freq], irr
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*** Poisson regression adjusted by chi-squared
1 q5 H: P% W$ g0 }- glm ill exposed confounder [fweight = freq], family(poisson) scale(x2) eform
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*** Poisson regression adjusted by deviance6 b, o- ~' ~1 E
- glm ill exposed confounder [fweight = freq], family(poisson) scale(dev) eform irls
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*** Poisson regression with robust variance
- X3 m# A2 z/ g) T# X u- poisson ill exposed confounder [fweight = freq], irr r
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" {6 A: a: d6 F" G9 M7 J+ I*** Log-binomial regression+ A/ f) |, D$ g" j, V6 B( i
- glm ill exposed confounder [fweight = freq], family(binomial) link(log) eform
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3 k0 ?6 c8 z F1 R*** Odds ratio from logistic regression) R7 o" @" m* Y) G" w
- logistic ill exposed confounder [fweight = freq]
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) Z. x0 {+ l, P$ ]( ^& U; X, F" L, m
A comparison of two methods for estimating prevalence ratios.
: K# F. z/ M3 M/ H- Ohttps://www.ncbi.nlm.nih.gov/m/p ... m=/14567763/related
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Poisson regression with or without robust
5 s% b; r7 N& u9 r% Zhttps://www.statalist.org/forums ... ussion/general/5912& z5 t& J4 q& f$ _8 Z, K
% j, Y. \" e( l0 R' i% c, U- ~Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio
# E, h* u2 G5 \. e4 i! g5 X5 m# jhttps://www.statalist.org/forums/filedata/fetch?id=5927
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