# lect06 - Categorical Data Analysis Lei Sun 1 CHL 5210...

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Unformatted text preview: Categorical Data Analysis - Lei Sun 1 CHL 5210 - Statistical Analysis of Qualitative Data Topic: Logistic Regression ctd. and Poisson Regression Outline • Logistic regression ctd. • Poisson regression. – Log-linear model. – An example of Poisson regression. – An example of Poisson regression for rates. • Homework 3. Categorical Data Analysis - Lei Sun 2 Multiple regression analysis - model building. Model building (selection of variables, diagnostics and validation) for linear multiple regression models generally applies to logistic regression models. • To obtain valid estimates of associations: – Specify variables. – Univariate exploratory analyses. – Construct full model. How many covariates should we include in our model given that we will be limiting attention to large sample procedures? – Assess interactions. The Challenge of examining for interaction (too many). Hierarchically well formulated models (all lower order components of any term are included ). – Assess confounding. – Examine the fit of your final model(s). Categorical Data Analysis - Lei Sun 3 • The LBW example. – Risk factors of primary interest (3 variables). * Mother’s smoking status (SMOKE). * Mother’s age (AGE). * Number of physician visits during the first trimester (FTV). – Potential confounders and effect modifiers (5 variables). * Mother’s race (RACE). * Mother’s weight (LWT). * Mother’s history of premature labor (PTL). * Mother’s history of hypertension (HT). * Presence of uterine irritability (UI). Categorical Data Analysis - Lei Sun 4 – How should we model mother’s weight (LWT)? (Univariate exploratory analyses.) data x1;infile "bwt.dat"; input id low age lwt race smoke ptl ht ui ftv bwt; lwtq= (lwt le 109) + 2*(lwt ge 110 and lwt le 120) + 3*(lwt ge 121 and lwt le 138) + 4*(lwt ge 139); lwt2=0; lwt3=0; lwt4=0; if (lwtq=2) then lwt2=1; if (lwtq=3) then lwt3=1; if (lwtq=4) then lwt4=1; proc freq;table low*lwtq; proc logistic descending; model low=lwt2 lwt3 lwt4; run; Table of low by lwtq low lwtq | 1| 2| 3| 4| Total---------+--------+--------+--------+--------+ 0 | 21 | 37 | 34 | 38 | 130---------+--------+--------+--------+--------+ 1 | 21 | 13 | 13 | 12 | 59---------+--------+--------+--------+--------+ Total 42 50 47 50 189 Model Fit Statistics Intercept Intercept and Criterion Only Covariates-2 Log L 234.672 226.071 Categorical Data Analysis - Lei Sun 5 Testing Global Null Hypothesis: BETA=0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio 8.6013 3 0.0351 Score 9.0246 3 0.0290 Wald 8.6347 3 0.0346 Analysis of Maximum Likelihood Estimates Standard Wald Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept 1-0.00010 0.3086 0.0000 0.9997 lwt2 1-1.0458 0.4463 5.4911 0.0191 lwt3 1-0.9613 0.4490 4.5845 0.0323 lwt4 1-1.1524 0.4526 6.4820 0.0109 Odds Ratio Estimates Point 95% Wald Effect Estimate Confidence Limits lwt2 0.351 0.147 0.843 lwt3 0.382 0.159 0.922 lwt4 0.316 0.130 0.767 What is the reference group?...
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## This note was uploaded on 02/23/2012 for the course CHL 5210H taught by Professor Leisun during the Fall '11 term at University of Toronto.

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lect06 - Categorical Data Analysis Lei Sun 1 CHL 5210...

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