Generalized Mixed Models for Ordinal Longitudinal Outcomes using PROC GLIMMIX
|
|
|
- Bertina Carson
- 7 years ago
- Views:
Transcription
1 Generalized Mixed Models for Ordinal Longitudinal Outcomes using PROC GLIMMIX SAS Data Manipulation: * Reading in all data; DATA alldata; SET annk.annknewfinal; WHERE NMISS(age80, mmse16)=0; cam012=cam; LABEL cam012= " Some, Full Delirium"; run; * Observed frequencies; PROC FREQ DATA= alldata; TABLE cam012; run; Some, Full Delirium Cumulative Cumulative cam012 Frequency Percent Frequency Percent ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ none some full a) Ordinal Single-Level Empty Means Model for None vs. Some vs. Full Delirium via GLIMMIX (Composite Model Form) Note: it appears ILINK does not work correctly for CLOGIT models. Logit y 0 Logit y 1 ti 001 ti 002 TITLE1 "GLIMMIX Ordinal Single-Level Empty Means Model"; TITLE2 "Cumulative Logit Link, Multinomial Distribution"; PROC GLIMMIX DATA=alldata METHOD=QUAD; MODEL cam012 (DESCENDING) = / SOLUTION LINK=CLOGIT DIST=MULT; run; Response Profile Ordered Total Value cam012 Frequency The GLIMMIX procedure is modeling the probabilities of levels of cam012 having lower Ordered Values in the Response Profile table. This means we are predicting up, not down. Convergence criterion (ABSGCONV= ) satisfied. -2 Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) CAIC (smaller is better) HQIC (smaller is better) Parameter Estimates Intercept < E-14 Intercept < E-15 Intercept2 = logit of probability of y > 1 = exp( 1.667) / (1+exp( 1.667)) =.1588 Intercept1 = logit of probability of y > 0 = exp( 0.521) / (1+exp( 0.521)) =.6274 (= ) Probability(y=0) = 1 intercept1 = =.3725 Probability(y=1) = intercept1 intercept2 = =.4686 Probability(y=2) = intercept2 0 = =.1588 Ordinal Longitudinal Example 1 of 7
2 1b) Ordinal Empty Means, Random Intercept Only Model (same random intercept both sub-models) Logit yti U0i Logit y 1 U ti 002 0i TITLE1 "GLIMMIX Ordinal Empty Means, Random Intercept Model"; TITLE2 "Cumulative Logit Link, Multinomial Response Distribution"; MODEL cam012 (DESCENDING) = / SOLUTION LINK=CLOGIT DIST=MULT; RANDOM INTERCEPT / TYPE=UN SUBJECT=patient_ID; COVTEST "Need Random Intercept?" 0; * Test if G matrix (1,1)=0; RUN; run; -2 Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) CAIC (smaller is better) HQIC (smaller is better) ariance Parameter Estimates Parm Subject Estimate Error Gradient UN(1,1) PATIENT_ID Solutions for Fixed Effects Intercept < Intercept < Tests of ariance Parameters Based on the Likelihood Label DF -2 Log Like ChiSq Pr > ChiSq Note Need Random Intercept? <.0001 MI This matches the 2ΔLL MI: P-value based on a mixture of chi-squares. The fixed effects for the intercepts are not the same as in the previous model. This is for two reasons: (1) They are conditional on the random intercept (i.e., expected proportions for someone with U 0i = 0). So for that kind of person, the probability of y > 1 = exp( ) / (1+exp( )) =.115 (not.159), and the probability of y > 0 = exp( ) / (1+exp( )) =.665 (not.627). (2) They are scaled differently because there is now more total variance in the outcome: Total variance = Var(U 0i ) + Var(e ti ) = π 2 /3 = = = only 3.29 in previous single-level model Ordinal Longitudinal Example 2 of 7
3 1c) Ordinal Mixed Model: Adding a Fixed Slope and a Random Slope for Days Since Hospital Admission (0=Day Hospitalized) Logit y 0 U Day U Logit y 1 U Day U ti i ti 0i ti i ti 0i TITLE1 "GLIMMIX Ordinal Mixed Model -- Adding a Fixed and Random Slope for Day"; TITLE2 "Cumulative Logit Link, Multinomial Response Distribution"; MODEL cam012 (DESCENDING) = day / SOLUTION LINK=CLOGIT DIST=MULT; RANDOM INTERCEPT day / TYPE=UN SUBJECT=patient_ID; COVTEST "Need Random Slope?". 0 0; * Leave (1,1), test if (2,1) and (2,2) =0; run; -2 Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) CAIC (smaller is better) HQIC (smaller is better) Fit statistics from Fixed Linear Day, Random Intercept Model: -2 Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) CAIC (smaller is better) HQIC (smaller is better) ariance Parameter Estimates Parm Subject Estimate Error Gradient UN(1,1) PATIENT_ID UN(2,1) PATIENT_ID UN(2,2) PATIENT_ID Solutions for Fixed Effects Intercept < Intercept < day Tests of ariance Parameters Based on the Likelihood Label DF -2 Log Like ChiSq Pr > ChiSq Note Need Random Slope? MI This matches the 2ΔLL MI: P-value based on a mixture of chi-squares. Interpret the fixed intercept 2: Interpret the fixed intercept 1: Interpret the fixed linear effect of day: Ordinal Longitudinal Example 3 of 7
4 2a) Ordinal Mixed Model: Effects of MMSE on Intercept and Slope Logit y 0 U Day MMSE 16 MMSE 16Day U Logit y 1 U Day MMSE 16 MMSE 16 Day U ti i ti 01 i 11 i ti 0i ti i ti 01 i 11 i ti 0i Hoffman Psyc 945 Example 6b TITLE1 "GLIMMIX Ordinal Mixed Model -- Effects of MMSE on Intercept and Slope"; TITLE2 "Cumulative Logit Link, Multinomial Response Distribution"; MODEL cam012 (DESCENDING) = day mmse16 day*mmse16 / SOLUTION LINK=CLOGIT DIST=MULT; RANDOM INTERCEPT day / TYPE=UN SUBJECT=patient_ID; COVTEST "Still Need Random Intercept?" 0 0 0; COVTEST "Still Need Random Slope?". 0 0; run; NOTE: GCONV convergence criterion satisfied. FROM THE LOG: NOTE: At least one element of the (projected) gradient is greater than 1e Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) CAIC (smaller is better) HQIC (smaller is better) ariance Parameter Estimates Parm Subject Estimate Error Gradient UN(1,1) PATIENT_ID UN(2,1) PATIENT_ID UN(2,2) PATIENT_ID Solutions for Fixed Effects Intercept < Intercept day mmse day*mmse Tests of ariance Parameters Based on the Likelihood Label DF -2 Log Like ChiSq Pr > ChiSq Note Still Need Random Intercept? < Still Need Random Slope? MI MI: P-value based on a mixture of chi-squares. --: test with unadjusted p-values. Interpret the fixed main effect of MMSE: Interpret the fixed interaction of MMSE by Day: This model makes what is referred as the proportional odds assumption. This means that although we have estimated separate intercepts for the two sub-models, we have constrained the fixed slopes for the predictor effects to be the same for each submodel (as well as the random effects). One alternative is the nominal model, which estimates separate intercepts, fixed effects, and random effects variances for each sub-model (specified as a baseline category vs. each of the other alternatives). Ordinal Longitudinal Example 4 of 7
5 2b) Attempting the Nominal Model: Sub-models Prob(y=1 if 0 or 1, y=2 if 0 or 2) Logit y 0 U Day MMSE 16 MMSE 16Day U Logit y 1 U Day MMSE 16 MMSE 16 Day U ti i1 ti 011 i 111 i ti 0i1 ti i2 ti 012 i 112 i ti 0i2 TITLE1 "GLIMMIX Nominal Mixed Model -- Effects of MMSE on Intercept and Slope"; TITLE2 "Baseline Logit Link, Multinomial Response Distribution"; CLASS patient_id cam012; MODEL cam012 (REF=FIRST) = day mmse16 day*mmse16 / SOLUTION LINK=GLOGIT DIST=MULT; RANDOM INTERCEPT day / TYPE=UN SUBJECT=patient_ID GROUP=cam012; run; The initial estimates did not yield a valid objective function. ariance Parameter Estimates Parm Subject Group Estimate Error Gradient UN(1,1) PATIENT_ID cam UN(2,1) PATIENT_ID cam UN(2,2) PATIENT_ID cam UN(1,1) PATIENT_ID cam UN(2,1) PATIENT_ID cam UN(2,2) PATIENT_ID cam We have another alternative, in which the fixed effects of predictors are allowed to vary across submodels, but the random effects variances do not. 2c) Ordinal Mixed Model: NON-PROPORTIONAL Effects of MMSE on Intercept and Slope Logit yti U1i Dayti 011 MMSEi MMSEi 16Dayti U0i Logit y 1 U Day MMSE 16 MMSE 16 Day U ti i ti 012 i 112 i ti 0i TITLE1 "Ordinal Mixed Non-Proportional Model for MMSE"; PROC NLMIXED DATA=alldata METHOD=GAUSS TECH=QUANEW GCONV=1e-12; * Must list all parms to be estimated here with start values; * B01 and B02 = intercepts for each equation; * B's = fixed effects, now separate per equation; * V's = variance components in order of G matrix; PARMS B01=.6 B02=-1.6 B11day=0 B21mmse=0 B31mmseday=0 B12day=0 B22mmse=0 B32mmseday=0 V11=1 V21=-.2 V22=.05; * Linear predictive model; Y1 = B01 + B11day*day + U1*day + B21mmse*mmse16 + B31mmseday*mmse16*day + U0; Y2 = B02 + B12day*day + U1*day + B22mmse*mmse16 + B32mmseday*mmse16*day + U0; * Model for probability of response - writing it the shorter way; IF (cam=0) THEN P = 1 - (1/(1 + EXP(-Y1))); ELSE IF (cam=1) THEN P = (1/(1 + EXP(-Y1))) - (1/(1 + EXP(-Y2))); ELSE IF (cam=2) THEN P = (1/(1 + EXP(-Y2))); LL = LOG(P); MODEL cam012 ~ GENERAL(LL); * Random intercept and linear slope; RANDOM U0 U1 ~ NORMAL([0,0],[V11,V21,V22]) SUBJECT=patient_ID; RUN; Ordinal Longitudinal Example 5 of 7
6 NOTE: GCONV convergence criterion satisfied (and no error messages in the log!) -2 Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) Parameter Estimates Parameter Estimate Error DF t Value Pr > t Alpha Lower Upper Gradient B E-7 B < E-8 B11day E-7 B21mmse E-6 B31mmseday Hoffman Psyc 945 Example 6b Fit statistics from Proportional version of same model: -2 Log Likelihood ΔLL(3) = 9.05 AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) B12day E-7 B22mmse E-6 B32mmseday E-6 V E-7 V E-6 V E-7 2ΔLL(3) = 9.05, p <.05, suggesting that each submodel needs its own set of fixed effects. However, the interaction of MMSE*day is not significant for either submodel, and could be removed. 2d) Ordinal Mixed Non-Proportional Model: Removing MMSE*Day Interactions Logit yti U1i Dayti 011 MMSEi 16 U0i Logit y 1 U Day MMSE 16 U ti i ti 012 i 0i TITLE1 "Ordinal Mixed Non-Proportional Model for MMSE - No MMSE*Day"; PROC NLMIXED DATA=alldata METHOD=GAUSS TECH=QUANEW GCONV=1e-12; * Must list all parms to be estimated here with start values; * B01 and B02 = intercepts for each equation; * B's = fixed effects; * V's = variance components in order of G matrix; PARMS B01=.6 B02=-1.6 B11day=0 B21mmse=0 B12day=0 B22mmse=0 V11=1 V21=-.2 V22=.05; * Linear predictive model - written as single-level equation; Y1 = B01 + U0 + B11day*day + B21mmse*mmse16 + U1*day; Y2 = B02 + U0 + B12day*day + B22mmse*mmse16 + U1*day; * Model for probability of response - writing it the shorter way; IF (cam=0) THEN P = 1 - (1/(1 + EXP(-Y1))); ELSE IF (cam=1) THEN P = (1/(1 + EXP(-Y1))) - (1/(1 + EXP(-Y2))); ELSE IF (cam=2) THEN P = (1/(1 + EXP(-Y2))); LL = LOG(P); MODEL cam012 ~ GENERAL(LL); * Random intercept and linear slope; RANDOM U0 U1 ~ NORMAL([0,0],[V11,V21,V22]) SUBJECT=patient_ID; RUN; NOTE: GCONV convergence criterion satisfied (and no error messages in the log!) -2 Log Likelihood AIC (smaller is better) AICC (smaller is better) BIC (smaller is better) Ordinal Longitudinal Example 6 of 7
7 Parameter Estimates Parameter Estimate Error DF t Value Pr > t Alpha Lower Upper Gradient B E-6 B < E-6 B11day B21mmse < E-6 B12day E-6 B22mmse V E-6 V E-6 V Hoffman Psyc 945 Example 6b Change in Logit across Days by Sub-Model and MMSE 5.00 Sub-Model 0 vs 12: MMSE = 9 Sub-Model 0 vs 12: MMSE = 23 Sub-Model 01 vs 2: MMSE = 9 Sub-Model 01 vs 2: MMSE = There is a linear relationship between day and the logits Logit (Y) Day Since Hospital Admission Change in Probability across Days by Sub-Model and MMSE Sub-Model 0 vs 12: MMSE = 9 Sub-Model 0 vs 12: MMSE = 23 Sub-Model 01 vs 2: MMSE = 9 Sub-Model 01 vs 2: MMSE = There is a NON-linear relationship between day and the probabilities Probability (Y) Days Since Hospital Admission Ordinal Longitudinal Example 7 of 7
Generalized Mixed Models for Binomial Longitudinal Outcomes (% Correct) using PROC GLIMMIX
Hoffman Psyc 945 Example 6c Generalized Mixed Models for Binomial Longitudinal Outcomes (% Correct) using PROC GLIMMIX The data for this example are based on the publication below, which examined annual
SAS Syntax and Output for Data Manipulation:
Psyc 944 Example 5 page 1 Practice with Fixed and Random Effects of Time in Modeling Within-Person Change The models for this example come from Hoffman (in preparation) chapter 5. We will be examining
I L L I N O I S UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
Beckman HLM Reading Group: Questions, Answers and Examples Carolyn J. Anderson Department of Educational Psychology I L L I N O I S UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN Linear Algebra Slide 1 of
861 Example SPLH. 5 page 1. prefer to have. New data in. SPSS Syntax FILE HANDLE. VARSTOCASESS /MAKE rt. COMPUTE mean=2. COMPUTE sal=2. END IF.
SPLH 861 Example 5 page 1 Multivariate Models for Repeated Measures Response Times in Older and Younger Adults These data were collected as part of my masters thesis, and are unpublished in this form (to
SUGI 29 Statistics and Data Analysis
Paper 194-29 Head of the CLASS: Impress your colleagues with a superior understanding of the CLASS statement in PROC LOGISTIC Michelle L. Pritchard and David J. Pasta Ovation Research Group, San Francisco,
Electronic Thesis and Dissertations UCLA
Electronic Thesis and Dissertations UCLA Peer Reviewed Title: A Multilevel Longitudinal Analysis of Teaching Effectiveness Across Five Years Author: Wang, Kairong Acceptance Date: 2013 Series: UCLA Electronic
Basic Statistical and Modeling Procedures Using SAS
Basic Statistical and Modeling Procedures Using SAS One-Sample Tests The statistical procedures illustrated in this handout use two datasets. The first, Pulse, has information collected in a classroom
This can dilute the significance of a departure from the null hypothesis. We can focus the test on departures of a particular form.
One-Degree-of-Freedom Tests Test for group occasion interactions has (number of groups 1) number of occasions 1) degrees of freedom. This can dilute the significance of a departure from the null hypothesis.
Mihaela Ene, Elizabeth A. Leighton, Genine L. Blue, Bethany A. Bell University of South Carolina
Paper 134-2014 Multilevel Models for Categorical Data using SAS PROC GLIMMIX: The Basics Mihaela Ene, Elizabeth A. Leighton, Genine L. Blue, Bethany A. Bell University of South Carolina ABSTRACT Multilevel
Random effects and nested models with SAS
Random effects and nested models with SAS /************* classical2.sas ********************* Three levels of factor A, four levels of B Both fixed Both random A fixed, B random B nested within A ***************************************************/
VI. Introduction to Logistic Regression
VI. Introduction to Logistic Regression We turn our attention now to the topic of modeling a categorical outcome as a function of (possibly) several factors. The framework of generalized linear models
Ordinal Regression. Chapter
Ordinal Regression Chapter 4 Many variables of interest are ordinal. That is, you can rank the values, but the real distance between categories is unknown. Diseases are graded on scales from least severe
SAS Software to Fit the Generalized Linear Model
SAS Software to Fit the Generalized Linear Model Gordon Johnston, SAS Institute Inc., Cary, NC Abstract In recent years, the class of generalized linear models has gained popularity as a statistical modeling
Lecture 19: Conditional Logistic Regression
Lecture 19: Conditional Logistic Regression Dipankar Bandyopadhyay, Ph.D. BMTRY 711: Analysis of Categorical Data Spring 2011 Division of Biostatistics and Epidemiology Medical University of South Carolina
Individual Growth Analysis Using PROC MIXED Maribeth Johnson, Medical College of Georgia, Augusta, GA
Paper P-702 Individual Growth Analysis Using PROC MIXED Maribeth Johnson, Medical College of Georgia, Augusta, GA ABSTRACT Individual growth models are designed for exploring longitudinal data on individuals
HLM software has been one of the leading statistical packages for hierarchical
Introductory Guide to HLM With HLM 7 Software 3 G. David Garson HLM software has been one of the leading statistical packages for hierarchical linear modeling due to the pioneering work of Stephen Raudenbush
Overview Classes. 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7)
Overview Classes 12-3 Logistic regression (5) 19-3 Building and applying logistic regression (6) 26-3 Generalizations of logistic regression (7) 2-4 Loglinear models (8) 5-4 15-17 hrs; 5B02 Building and
Family economics data: total family income, expenditures, debt status for 50 families in two cohorts (A and B), annual records from 1990 1995.
Lecture 18 1. Random intercepts and slopes 2. Notation for mixed effects models 3. Comparing nested models 4. Multilevel/Hierarchical models 5. SAS versions of R models in Gelman and Hill, chapter 12 1
Nominal and ordinal logistic regression
Nominal and ordinal logistic regression April 26 Nominal and ordinal logistic regression Our goal for today is to briefly go over ways to extend the logistic regression model to the case where the outcome
Missing Data in Longitudinal Studies
Missing Data in Longitudinal Studies Hedeker D & Gibbons RD (1997). Application of random-effects pattern-mixture models for missing data in longitudinal studies. Psychological Methods, 2, 64-78. Chapter
Lecture 14: GLM Estimation and Logistic Regression
Lecture 14: GLM Estimation and Logistic Regression Dipankar Bandyopadhyay, Ph.D. BMTRY 711: Analysis of Categorical Data Spring 2011 Division of Biostatistics and Epidemiology Medical University of South
Using An Ordered Logistic Regression Model with SAS Vartanian: SW 541
Using An Ordered Logistic Regression Model with SAS Vartanian: SW 541 libname in1 >c:\=; Data first; Set in1.extract; A=1; PROC LOGIST OUTEST=DD MAXITER=100 ORDER=DATA; OUTPUT OUT=CC XBETA=XB P=PROB; MODEL
Developing Risk Adjustment Techniques Using the SAS@ System for Assessing Health Care Quality in the lmsystem@
Developing Risk Adjustment Techniques Using the SAS@ System for Assessing Health Care Quality in the lmsystem@ Yanchun Xu, Andrius Kubilius Joint Commission on Accreditation of Healthcare Organizations,
Applied Longitudinal Data Analysis: An Introductory Course
Applied Longitudinal Data Analysis: An Introductory Course Emilio Ferrer UC Davis The Risk and Prevention in Education Sciences (RPES) Curry School of Education - UVA August 2005 Acknowledgments Materials
Multinomial and Ordinal Logistic Regression
Multinomial and Ordinal Logistic Regression ME104: Linear Regression Analysis Kenneth Benoit August 22, 2012 Regression with categorical dependent variables When the dependent variable is categorical,
ln(p/(1-p)) = α +β*age35plus, where p is the probability or odds of drinking
Dummy Coding for Dummies Kathryn Martin, Maternal, Child and Adolescent Health Program, California Department of Public Health ABSTRACT There are a number of ways to incorporate categorical variables into
Cool Tools for PROC LOGISTIC
Cool Tools for PROC LOGISTIC Paul D. Allison Statistical Horizons LLC and the University of Pennsylvania March 2013 www.statisticalhorizons.com 1 New Features in LOGISTIC ODDSRATIO statement EFFECTPLOT
An Introduction to Modeling Longitudinal Data
An Introduction to Modeling Longitudinal Data Session I: Basic Concepts and Looking at Data Robert Weiss Department of Biostatistics UCLA School of Public Health robweiss@ucla.edu August 2010 Robert Weiss
Introduction to mixed model and missing data issues in longitudinal studies
Introduction to mixed model and missing data issues in longitudinal studies Hélène Jacqmin-Gadda INSERM, U897, Bordeaux, France Inserm workshop, St Raphael Outline of the talk I Introduction Mixed models
Comparing Multiple Proportions, Test of Independence and Goodness of Fit
Comparing Multiple Proportions, Test of Independence and Goodness of Fit Content Testing the Equality of Population Proportions for Three or More Populations Test of Independence Goodness of Fit Test 2
Module 5: Introduction to Multilevel Modelling SPSS Practicals Chris Charlton 1 Centre for Multilevel Modelling
Module 5: Introduction to Multilevel Modelling SPSS Practicals Chris Charlton 1 Centre for Multilevel Modelling Pre-requisites Modules 1-4 Contents P5.1 Comparing Groups using Multilevel Modelling... 4
CHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS
Examples: Regression And Path Analysis CHAPTER 3 EXAMPLES: REGRESSION AND PATH ANALYSIS Regression analysis with univariate or multivariate dependent variables is a standard procedure for modeling relationships
Introduction to Hierarchical Linear Modeling with R
Introduction to Hierarchical Linear Modeling with R 5 10 15 20 25 5 10 15 20 25 13 14 15 16 40 30 20 10 0 40 30 20 10 9 10 11 12-10 SCIENCE 0-10 5 6 7 8 40 30 20 10 0-10 40 1 2 3 4 30 20 10 0-10 5 10 15
STATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 Sigma-Restricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
CHAPTER 12 EXAMPLES: MONTE CARLO SIMULATION STUDIES
Examples: Monte Carlo Simulation Studies CHAPTER 12 EXAMPLES: MONTE CARLO SIMULATION STUDIES Monte Carlo simulation studies are often used for methodological investigations of the performance of statistical
Use of deviance statistics for comparing models
A likelihood-ratio test can be used under full ML. The use of such a test is a quite general principle for statistical testing. In hierarchical linear models, the deviance test is mostly used for multiparameter
Chapter 29 The GENMOD Procedure. Chapter Table of Contents
Chapter 29 The GENMOD Procedure Chapter Table of Contents OVERVIEW...1365 WhatisaGeneralizedLinearModel?...1366 ExamplesofGeneralizedLinearModels...1367 TheGENMODProcedure...1368 GETTING STARTED...1370
MISSING DATA TECHNIQUES WITH SAS. IDRE Statistical Consulting Group
MISSING DATA TECHNIQUES WITH SAS IDRE Statistical Consulting Group ROAD MAP FOR TODAY To discuss: 1. Commonly used techniques for handling missing data, focusing on multiple imputation 2. Issues that could
Outline. Topic 4 - Analysis of Variance Approach to Regression. Partitioning Sums of Squares. Total Sum of Squares. Partitioning sums of squares
Topic 4 - Analysis of Variance Approach to Regression Outline Partitioning sums of squares Degrees of freedom Expected mean squares General linear test - Fall 2013 R 2 and the coefficient of correlation
data visualization and regression
data visualization and regression Sepal.Length 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 I. setosa I. versicolor I. virginica I. setosa I. versicolor I. virginica Species Species
ASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS
DATABASE MARKETING Fall 2015, max 24 credits Dead line 15.10. ASSIGNMENT 4 PREDICTIVE MODELING AND GAINS CHARTS PART A Gains chart with excel Prepare a gains chart from the data in \\work\courses\e\27\e20100\ass4b.xls.
ECLT5810 E-Commerce Data Mining Technique SAS Enterprise Miner -- Regression Model I. Regression Node
Enterprise Miner - Regression 1 ECLT5810 E-Commerce Data Mining Technique SAS Enterprise Miner -- Regression Model I. Regression Node 1. Some background: Linear attempts to predict the value of a continuous
An Introduction to Statistical Tests for the SAS Programmer Sara Beck, Fred Hutchinson Cancer Research Center, Seattle, WA
ABSTRACT An Introduction to Statistical Tests for the SAS Programmer Sara Beck, Fred Hutchinson Cancer Research Center, Seattle, WA Often SAS Programmers find themselves in situations where performing
Statistics, Data Analysis & Econometrics
Using the LOGISTIC Procedure to Model Responses to Financial Services Direct Marketing David Marsh, Senior Credit Risk Modeler, Canadian Tire Financial Services, Welland, Ontario ABSTRACT It is more important
Logistic Regression (1/24/13)
STA63/CBB540: Statistical methods in computational biology Logistic Regression (/24/3) Lecturer: Barbara Engelhardt Scribe: Dinesh Manandhar Introduction Logistic regression is model for regression used
Multinomial and ordinal logistic regression using PROC LOGISTIC Peter L. Flom National Development and Research Institutes, Inc
ABSTRACT Multinomial and ordinal logistic regression using PROC LOGISTIC Peter L. Flom National Development and Research Institutes, Inc Logistic regression may be useful when we are trying to model a
Introduction to Multilevel Modeling Using HLM 6. By ATS Statistical Consulting Group
Introduction to Multilevel Modeling Using HLM 6 By ATS Statistical Consulting Group Multilevel data structure Students nested within schools Children nested within families Respondents nested within interviewers
Milk Data Analysis. 1. Objective Introduction to SAS PROC MIXED Analyzing protein milk data using STATA Refit protein milk data using PROC MIXED
1. Objective Introduction to SAS PROC MIXED Analyzing protein milk data using STATA Refit protein milk data using PROC MIXED 2. Introduction to SAS PROC MIXED The MIXED procedure provides you with flexibility
How To Model The Fate Of An Animal
Models Where the Fate of Every Individual is Known This class of models is important because they provide a theory for estimation of survival probability and other parameters from radio-tagged animals.
Using PROC MIXED in Hierarchical Linear Models: Examples from two- and three-level school-effect analysis, and meta-analysis research
Using PROC MIXED in Hierarchical Linear Models: Examples from two- and three-level school-effect analysis, and meta-analysis research Sawako Suzuki, DePaul University, Chicago Ching-Fan Sheu, DePaul University,
Appendix 1: Estimation of the two-variable saturated model in SPSS, Stata and R using the Netherlands 1973 example data
Appendix 1: Estimation of the two-variable saturated model in SPSS, Stata and R using the Netherlands 1973 example data A. SPSS commands and corresponding parameter estimates Copy the 1973 data from the
A C T R esearcli R e p o rt S eries 2 0 0 5. Using ACT Assessment Scores to Set Benchmarks for College Readiness. IJeff Allen.
A C T R esearcli R e p o rt S eries 2 0 0 5 Using ACT Assessment Scores to Set Benchmarks for College Readiness IJeff Allen Jim Sconing ACT August 2005 For additional copies write: ACT Research Report
ANALYSING LIKERT SCALE/TYPE DATA, ORDINAL LOGISTIC REGRESSION EXAMPLE IN R.
ANALYSING LIKERT SCALE/TYPE DATA, ORDINAL LOGISTIC REGRESSION EXAMPLE IN R. 1. Motivation. Likert items are used to measure respondents attitudes to a particular question or statement. One must recall
Chapter 13 Introduction to Nonlinear Regression( 非 線 性 迴 歸 )
Chapter 13 Introduction to Nonlinear Regression( 非 線 性 迴 歸 ) and Neural Networks( 類 神 經 網 路 ) 許 湘 伶 Applied Linear Regression Models (Kutner, Nachtsheim, Neter, Li) hsuhl (NUK) LR Chap 10 1 / 35 13 Examples
PARALLEL LINES ASSUMPTION IN ORDINAL LOGISTIC REGRESSION AND ANALYSIS APPROACHES
International Interdisciplinary Journal of Scientific Research ISSN: 2200-9833 www.iijsr.org PARALLEL LINES ASSUMPTION IN ORDINAL LOGISTIC REGRESSION AND ANALYSIS APPROACHES Erkan ARI 1 and Zeki YILDIZ
AN ILLUSTRATION OF MULTILEVEL MODELS FOR ORDINAL RESPONSE DATA
AN ILLUSTRATION OF MULTILEVEL MODELS FOR ORDINAL RESPONSE DATA Ann A. The Ohio State University, United States of America aoconnell@ehe.osu.edu Variables measured on an ordinal scale may be meaningful
Lecture 18: Logistic Regression Continued
Lecture 18: Logistic Regression Continued Dipankar Bandyopadhyay, Ph.D. BMTRY 711: Analysis of Categorical Data Spring 2011 Division of Biostatistics and Epidemiology Medical University of South Carolina
Introduction to Data Analysis in Hierarchical Linear Models
Introduction to Data Analysis in Hierarchical Linear Models April 20, 2007 Noah Shamosh & Frank Farach Social Sciences StatLab Yale University Scope & Prerequisites Strong applied emphasis Focus on HLM
The Probit Link Function in Generalized Linear Models for Data Mining Applications
Journal of Modern Applied Statistical Methods Copyright 2013 JMASM, Inc. May 2013, Vol. 12, No. 1, 164-169 1538 9472/13/$95.00 The Probit Link Function in Generalized Linear Models for Data Mining Applications
Examples of Using R for Modeling Ordinal Data
Examples of Using R for Modeling Ordinal Data Alan Agresti Department of Statistics, University of Florida Supplement for the book Analysis of Ordinal Categorical Data, 2nd ed., 2010 (Wiley), abbreviated
Logistic Regression. http://faculty.chass.ncsu.edu/garson/pa765/logistic.htm#sigtests
Logistic Regression http://faculty.chass.ncsu.edu/garson/pa765/logistic.htm#sigtests Overview Binary (or binomial) logistic regression is a form of regression which is used when the dependent is a dichotomy
Classification Problems
Classification Read Chapter 4 in the text by Bishop, except omit Sections 4.1.6, 4.1.7, 4.2.4, 4.3.3, 4.3.5, 4.3.6, 4.4, and 4.5. Also, review sections 1.5.1, 1.5.2, 1.5.3, and 1.5.4. Classification Problems
The GLIMMIX Procedure
The GLIMMIX Procedure Contents OVERVIEW................................... 5 Basic Features................................. 6 Assumptions.................................. 6 Notation for the Generalized
Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus
Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus Tihomir Asparouhov and Bengt Muthén Mplus Web Notes: No. 15 Version 8, August 5, 2014 1 Abstract This paper discusses alternatives
LCMM: a R package for the estimation of latent class mixed models for Gaussian, ordinal, curvilinear longitudinal data and/or time-to-event data
. LCMM: a R package for the estimation of latent class mixed models for Gaussian, ordinal, curvilinear longitudinal data and/or time-to-event data Cécile Proust-Lima Department of Biostatistics, INSERM
Missing data and net survival analysis Bernard Rachet
Workshop on Flexible Models for Longitudinal and Survival Data with Applications in Biostatistics Warwick, 27-29 July 2015 Missing data and net survival analysis Bernard Rachet General context Population-based,
Comparing return to work outcomes between vocational rehabilitation providers after adjusting for case mix using statistical models
Comparing return to work outcomes between vocational rehabilitation providers after adjusting for case mix using statistical models Prepared by Jim Gaetjens Presented to the Institute of Actuaries of Australia
Chapter 7: Simple linear regression Learning Objectives
Chapter 7: Simple linear regression Learning Objectives Reading: Section 7.1 of OpenIntro Statistics Video: Correlation vs. causation, YouTube (2:19) Video: Intro to Linear Regression, YouTube (5:18) -
Logistic (RLOGIST) Example #3
Logistic (RLOGIST) Example #3 SUDAAN Statements and Results Illustrated PREDMARG (predicted marginal proportion) CONDMARG (conditional marginal proportion) PRED_EFF pairwise comparison COND_EFF pairwise
CHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA
Examples: Multilevel Modeling With Complex Survey Data CHAPTER 9 EXAMPLES: MULTILEVEL MODELING WITH COMPLEX SURVEY DATA Complex survey data refers to data obtained by stratification, cluster sampling and/or
Module 4 - Multiple Logistic Regression
Module 4 - Multiple Logistic Regression Objectives Understand the principles and theory underlying logistic regression Understand proportions, probabilities, odds, odds ratios, logits and exponents Be
GEEs: SAS Syntax and Examples
GEEs: SAS Syntax and Examples The repeated statement is used in genmod to fit GLMs by GEE. The format of the repeated statement is given by: repeated subject= subject-effect/options;, where subject-effect
2. Making example missing-value datasets: MCAR, MAR, and MNAR
Lecture 20 1. Types of missing values 2. Making example missing-value datasets: MCAR, MAR, and MNAR 3. Common methods for missing data 4. Compare results on example MCAR, MAR, MNAR data 1 Missing Data
11. Analysis of Case-control Studies Logistic Regression
Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:
Statistical Models in R
Statistical Models in R Some Examples Steven Buechler Department of Mathematics 276B Hurley Hall; 1-6233 Fall, 2007 Outline Statistical Models Structure of models in R Model Assessment (Part IA) Anova
Generalized Linear Models
Generalized Linear Models We have previously worked with regression models where the response variable is quantitative and normally distributed. Now we turn our attention to two types of models where the
Applied Statistics. J. Blanchet and J. Wadsworth. Institute of Mathematics, Analysis, and Applications EPF Lausanne
Applied Statistics J. Blanchet and J. Wadsworth Institute of Mathematics, Analysis, and Applications EPF Lausanne An MSc Course for Applied Mathematicians, Fall 2012 Outline 1 Model Comparison 2 Model
Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.
Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C
5. Multiple regression
5. Multiple regression QBUS6840 Predictive Analytics https://www.otexts.org/fpp/5 QBUS6840 Predictive Analytics 5. Multiple regression 2/39 Outline Introduction to multiple linear regression Some useful
Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
How to use SAS for Logistic Regression with Correlated Data
How to use SAS for Logistic Regression with Correlated Data Oliver Kuss Institute of Medical Epidemiology, Biostatistics, and Informatics Medical Faculty, University of Halle-Wittenberg, Halle/Saale, Germany
Two Correlated Proportions (McNemar Test)
Chapter 50 Two Correlated Proportions (Mcemar Test) Introduction This procedure computes confidence intervals and hypothesis tests for the comparison of the marginal frequencies of two factors (each with
Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations
Research Article TheScientificWorldJOURNAL (2011) 11, 42 76 TSW Child Health & Human Development ISSN 1537-744X; DOI 10.1100/tsw.2011.2 Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts,
Binary Logistic Regression
Binary Logistic Regression Main Effects Model Logistic regression will accept quantitative, binary or categorical predictors and will code the latter two in various ways. Here s a simple model including
Machine Learning Logistic Regression
Machine Learning Logistic Regression Jeff Howbert Introduction to Machine Learning Winter 2012 1 Logistic regression Name is somewhat misleading. Really a technique for classification, not regression.
Lecture 3: Linear methods for classification
Lecture 3: Linear methods for classification Rafael A. Irizarry and Hector Corrada Bravo February, 2010 Today we describe four specific algorithms useful for classification problems: linear regression,
SPSS Introduction. Yi Li
SPSS Introduction Yi Li Note: The report is based on the websites below http://glimo.vub.ac.be/downloads/eng_spss_basic.pdf http://academic.udayton.edu/gregelvers/psy216/spss http://www.nursing.ucdenver.edu/pdf/factoranalysishowto.pdf
Hierarchical Logistic Regression Modeling with SAS GLIMMIX Jian Dai, Zhongmin Li, David Rocke University of California, Davis, CA
Hierarchical Logistic Regression Modeling with SAS GLIMMIX Jian Dai, Zhongmin Li, David Rocke University of California, Davis, CA ABSTRACT Data often have hierarchical or clustered structures, such as
Introduction to Quantitative Methods
Introduction to Quantitative Methods October 15, 2009 Contents 1 Definition of Key Terms 2 2 Descriptive Statistics 3 2.1 Frequency Tables......................... 4 2.2 Measures of Central Tendencies.................
Hierarchical Insurance Claims Modeling
Hierarchical Insurance Claims Modeling Edward W. (Jed) Frees, University of Wisconsin - Madison Emiliano A. Valdez, University of Connecticut 2009 Joint Statistical Meetings Session 587 - Thu 8/6/09-10:30
Calculating P-Values. Parkland College. Isela Guerra Parkland College. Recommended Citation
Parkland College A with Honors Projects Honors Program 2014 Calculating P-Values Isela Guerra Parkland College Recommended Citation Guerra, Isela, "Calculating P-Values" (2014). A with Honors Projects.
Real longitudinal data analysis for real people: Building a good enough mixed model
Tutorial in Biostatistics Received 28 July 2008, Accepted 30 September 2009 Published online 10 December 2009 in Wiley Interscience (www.interscience.wiley.com) DOI: 10.1002/sim.3775 Real longitudinal
Survey, Statistics and Psychometrics Core Research Facility University of Nebraska-Lincoln. Log-Rank Test for More Than Two Groups
Survey, Statistics and Psychometrics Core Research Facility University of Nebraska-Lincoln Log-Rank Test for More Than Two Groups Prepared by Harlan Sayles (SRAM) Revised by Julia Soulakova (Statistics)
Descriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
The Proportional Odds Model for Assessing Rater Agreement with Multiple Modalities
The Proportional Odds Model for Assessing Rater Agreement with Multiple Modalities Elizabeth Garrett-Mayer, PhD Assistant Professor Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University 1
SP10 From GLM to GLIMMIX-Which Model to Choose? Patricia B. Cerrito, University of Louisville, Louisville, KY
SP10 From GLM to GLIMMIX-Which Model to Choose? Patricia B. Cerrito, University of Louisville, Louisville, KY ABSTRACT The purpose of this paper is to investigate several SAS procedures that are used in
Analysis of Survey Data Using the SAS SURVEY Procedures: A Primer
Analysis of Survey Data Using the SAS SURVEY Procedures: A Primer Patricia A. Berglund, Institute for Social Research - University of Michigan Wisconsin and Illinois SAS User s Group June 25, 2014 1 Overview
9.2 User s Guide SAS/STAT. The MIXED Procedure. (Book Excerpt) SAS Documentation
SAS/STAT 9.2 User s Guide The MIXED Procedure (Book Excerpt) SAS Documentation This document is an individual chapter from SAS/STAT 9.2 User s Guide. The correct bibliographic citation for the complete
A Primer on Mathematical Statistics and Univariate Distributions; The Normal Distribution; The GLM with the Normal Distribution
A Primer on Mathematical Statistics and Univariate Distributions; The Normal Distribution; The GLM with the Normal Distribution PSYC 943 (930): Fundamentals of Multivariate Modeling Lecture 4: September