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Multilevel and Longitudinal Modeling Using Stata, Volume II: Categorical Responses, Counts, and Survival, Third Edition 3rd New edition [Pehme köide]

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(London School of Economics, UK), (University of California, Berkeley, USA)
  • Formaat: Paperback / softback, 484 pages, kaal: 998 g
  • Ilmumisaeg: 02-Apr-2012
  • Kirjastus: Stata Press
  • ISBN-10: 1597181048
  • ISBN-13: 9781597181044
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Multilevel and Longitudinal Modeling Using Stata, Volume II: Categorical Responses, Counts, and Survival, Third Edition 3rd New editionSuurem pilt
  • Formaat: Paperback / softback, 484 pages, kaal: 998 g
  • Ilmumisaeg: 02-Apr-2012
  • Kirjastus: Stata Press
  • ISBN-10: 1597181048
  • ISBN-13: 9781597181044
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781597181044.html
Märksõnad:

Volume II is devoted to generalized linear mixed models for binary, categorical, count, and survival outcomes. The second volume has seven chapters also organized in four parts. The first three parts in volume II cover models for categorical responses, including binary, ordinal, and nominal (a new chapter); models for count data; and models for survival data, including discrete-time and continuous-time (a new chapter) survival responses. The final part in volume II describes models with nested and crossed-random effects with an emphasis on binary outcomes.

List of Tables
xvii
List of Figures
xix
V Models for categorical responses
499(186)
10 Dichotomous or binary responses
501(74)
10.1 Introduction
501(1)
10.2 Single-level logit and probit regression models for dichotomous responses
501(14)
10.2.1 Generalized linear model formulation
502(8)
10.2.2 Latent-response formulation
510(2)
Logistic regression
512(1)
Probit regression
512(3)
10.3 Which treatment is best for toenail infection?
515(1)
10.4 Longitudinal data structure
515(2)
10.5 Proportions and fitted population-averaged or marginal probabilities
517(3)
10.6 Random-intercept logistic regression
520(3)
10.6.1 Model specification
520(1)
Reduced-form specification
520(2)
Two-stage formulation
522(1)
10.7 Estimation of random-intercept logistic models
523(6)
10.7.1 Using xtlogit
523(4)
10.7.2 Using xtmelogit
527(1)
10.7.3 Using gllamm
527(2)
10.8 Subject-specific or conditional vs. population-averaged or marginal relationships
529(3)
10.9 Measures of dependence and heterogeneity
532(3)
10.9.1 Conditional or residual intraclass correlation of the latent responses
532(1)
10.9.2 Median odds ratio
533(1)
10.9.3 Measures of association for observed responses at median fixed part of the model
533(2)
10.10 Inference for random-intercept logistic models
535(2)
10.10.1 Tests and confidence intervals for odds ratios
535(1)
10.10.2 Tests of variance components
536(1)
10.11 Maximum likelihood estimation
537(6)
10.11.1 Adaptive quadrature
537(3)
10.11.2 Some speed and accuracy considerations
540(2)
Advice for speeding up estimation in gllamm
542(1)
10.12 Assigning values to random effects
543(5)
10.12.1 Maximum "likelihood" estimation
544(1)
10.12.2 Empirical Bayes prediction
545(1)
10.12.3 Empirical Bayes modal prediction
546(2)
10.13 Different kinds of predicted probabilities
548(9)
10.13.1 Predicted population-averaged or marginal probabilities
548(1)
10.13.2 Predicted subject-specific probabilities
549(1)
Predictions for hypothetical subjects: Conditional probabilities
549(2)
Predictions for the subjects in the sample: Posterior mean probabilities
551(6)
10.14 Other approaches to clustered dichotomous data
557(5)
10.14.1 Conditional logistic regression
557(2)
10.14.2 Generalized estimating equations (GEE)
559(3)
10.15 Summary and further reading
562(1)
10.16 Exercises
563(12)
11 Ordinal responses
575(54)
11.1 Introduction
575(1)
11.2 Single-level cumulative models for ordinal responses
575(10)
11.2.1 Generalized linear model formulation
575(1)
11.2.2 Latent-response formulation
576(4)
11.2.3 Proportional odds
580(2)
11.2.4 Identification
582(3)
11.3 Are antipsychotic drugs effective for patients with schizophrenia?
585(1)
11.4 Longitudinal data structure and graphs
585(5)
11.4.1 Longitudinal data structure
586(1)
11.4.2 Plotting cumulative proportions
587(1)
11.4.3 Plotting cumulative sample logits and transforming the time scale
588(2)
11.5 A single-level proportional odds model
590(4)
11.5.1 Model specification
590(1)
11.5.2 Estimation using Stata
591(3)
11.6 A random-intercept proportional odds model
594(2)
11.6.1 Model specification
594(1)
11.6.2 Estimation using Stata
594(1)
11.6.3 Measures of dependence and heterogeneity
595(1)
Residual intraclass correlation of latent responses
595(1)
Median odds ratio
596(1)
11.7 A random-coefficient proportional odds model
596(3)
11.7.1 Model specification
596(1)
11.7.2 Estimation using gllamm
596(3)
11.8 Different kinds of predicted probabilities
599(7)
11.8.1 Predicted population-averaged or marginal probabilities
599(3)
11.8.2 Predicted subject-specific probabilities: Posterior mean
602(4)
11.9 Do experts differ in their grading of student essays?
606(1)
11.10 A random-intercept probit model with grader bias
606(2)
11.10.1 Model specification
606(1)
11.10.2 Estimation using gllamm
607(1)
11.11 Including grader-specific measurement error variances
608(3)
11.11.1 Model specification
608(1)
11.11.2 Estimation using gllamm
609(2)
11.12 Including grader-specific thresholds
611(5)
11.12.1 Model specification
611(1)
11.12.2 Estimation using gllamm
611(5)
11.13 Other link functions
616(3)
Cumulative complementary log-log model
616(1)
Continuation-ratio logit model
616(2)
Adjacent-category logit model
618(1)
Baseline-category logit and stereotype models
618(1)
11.14 Summary and further reading
619(1)
11.15 Exercises
620(9)
12 Nominal responses and discrete choice
629(56)
12.1 Introduction
629(1)
12.2 Single-level models for nominal responses
630(18)
12.2.1 Multinomial logit models
630(8)
12.2.2 Conditional logit models
638(1)
Classical conditional logit models
639(6)
Conditional logit models also including covariates that vary only over units
645(3)
12.3 Independence from irrelevant alternatives
648(1)
12.4 Utility-maximization formulation
649(2)
12.5 Does marketing affect choice of yogurt?
651(2)
12.6 Single-level conditional logit models
653(6)
12.6.1 Conditional logit models with alternative-specific intercepts
654(5)
12.7 Multilevel conditional logit models
659(13)
12.7.1 Preference heterogeneity: Brand-specific random intercepts
659(4)
12.7.2 Response heterogeneity: Marketing variables with random coefficients
663(3)
12.7.3 Preference and response heterogeneity
666(1)
Estimation using gllamm
667(2)
Estimation using mixlogit
669(3)
12.8 Prediction of random effects and response probabilities
672(4)
12.9 Summary and further reading
676(1)
12.10 Exercises
677(8)
VI Models for counts
685(56)
13 Counts
687(54)
13.1 Introduction
687(1)
13.2 What are counts?
687(2)
13.2.1 Counts versus proportions
687(1)
13.2.2 Counts as aggregated event-history data
688(1)
13.3 Single-level Poisson models for counts
689(2)
13.4 Did the German health-care reform reduce the number of doctor visits?
691(1)
13.5 Longitudinal data structure
691(1)
13.6 Single-level Poisson regression
692(4)
13.6.1 Model specification
692(1)
13.6.2 Estimation using Stata
693(3)
13.7 Random-intercept Poisson regression
696(5)
13.7.1 Model specification
696(1)
13.7.2 Measures of dependence and heterogeneity
697(1)
13.7.3 Estimation using Stata
697(1)
Using xtpoisson
697(2)
Using xtmepoisson
699(1)
Using gllamm
700(1)
13.8 Random-coefficient Poisson regression
701(5)
13.8.1 Model specification
701(1)
13.8.2 Estimation using Stata
702(1)
Using xtmepoisson
702(2)
Using gllamm
704(1)
13.8.3 Interpretation of estimates
705(1)
13.9 Overdispersion in single-level models
706(5)
13.9.1 Normally distributed random intercept
706(1)
13.9.2 Negative binomial models
707(1)
Mean dispersion or NB2
708(1)
Constant dispersion or NB1
709(1)
13.9.3 Quasilikelihood
709(2)
13.10 Level-1 overdispersion in two-level models
711(2)
13.11 Other approaches to two-level count data
713(3)
13.11.1 Conditional Poisson regression
713(2)
13.11.2 Conditional negative binomial regression
715(1)
13.11.3 Generalized estimating equations
715(1)
13.12 Marginal and conditional effects when responses are MAR
716(4)
Simulation
717(3)
13.13 Which Scottish counties have a high risk of lip cancer?
720(1)
13.14 Standardized mortality ratios
721(2)
13.15 Random-intercept Poisson regression
723(4)
13.15.1 Model specification
723(1)
13.15.2 Estimation using gllamm
724(1)
13.15.3 Prediction of standardized mortality ratios
725(2)
13.16 Nonparametric maximum likelihood estimation
727(5)
13.16.1 Specification
727(1)
13.16.2 Estimation using gllamm
727(5)
13.16.3 Prediction
732(1)
13.17 Summary and further reading
732(1)
13.18 Exercises
733(8)
VII Models for survival or duration data
741(130)
Introduction to models for survival or duration data (part VII)
743(6)
14 Discrete-time survival
749(48)
14.1 Introduction
749(1)
14.2 Single-level models for discrete-time survival data
749(24)
14.2.1 Discrete-time hazard and discrete-time survival
749(3)
14.2.2 Data expansion for discrete-time survival analysis
752(2)
14.2.3 Estimation via regression models for dichotomous responses
754(4)
14.2.4 Including covariates
758(1)
Time-constant covariates
758(4)
Time-varying covariates
762(5)
14.2.5 Multiple absorbing events and competing risks
767(5)
14.2.6 Handling left-truncated data
772(1)
14.3 How does birth history affect child mortality?
773(1)
14.4 Data expansion
774(2)
14.5 Proportional hazards and interval-censoring
776(1)
14.6 Complementary log-log model
777(4)
14.7 A random-intercept complementary log-log model
781(3)
14.7.1 Model specification
781(1)
14.7.2 Estimation using Stata
782(2)
14.8 Population-averaged or marginal vs. subject-specific or conditional survival probabilities
784(4)
14.9 Summary and further reading
788(1)
14.10 Exercises
789(8)
15 Continuous-time survival
797(74)
15.1 Introduction
797(1)
15.2 What makes marriages fail?
797(2)
15.3 Hazards and survival
799(6)
15.4 Proportional hazards models
805(18)
15.4.1 Piecewise exponential model
807(8)
15.4.2 Cox regression model
815(4)
15.4.3 Poisson regression with smooth baseline hazard
819(4)
15.5 Accelerated failure-time models
823(6)
15.5.1 Log-normal model
824(5)
15.6 Time-varying covariates
829(3)
15.7 Does nitrate reduce the risk of angina pectoris?
832(3)
15.8 Marginal modeling
835(6)
15.8.1 Cox regression
835(3)
15.8.2 Poisson regression with smooth baseline hazard
838(3)
15.9 Multilevel proportional hazards models
841(8)
15.9.1 Cox regression with gamma shared frailty
841(4)
15.9.2 Poisson regression with normal random intercepts
845(2)
15.9.3 Poisson regression with normal random intercept and random coefficient
847(2)
15.10 Multilevel accelerated failure-time models
849(2)
15.10.1 Log-normal model with gamma shared frailty
849(1)
15.10.2 Log-normal model with log-normal shared frailty
850(1)
15.11 A fixed-effects approach
851(2)
15.11.1 Cox regression with subject-specific baseline hazards
851(2)
15.12 Different approaches to recurrent-event data
853(8)
15.12.1 Total time
854(4)
15.12.2 Counting process
858(1)
15.12.3 Gap time
859(2)
15.13 Summary and further reading
861(1)
15.14 Exercises
862(9)
VIII Models with nested and crossed random effects
871(70)
16 Models with nested and crossed random effects
873(68)
16.1 Introduction
873(1)
16.2 Did the Guatemalan immunization campaign work?
873(2)
16.3 A three-level random-intercept logistic regression model
875(3)
16.3.1 Model specification
876(1)
16.3.2 Measures of dependence and heterogeneity
876(1)
Types of residual intraclass correlations of the latent responses
876(1)
Types of median odds ratios
877(1)
16.3.3 Three-stage formulation
877(1)
16.4 Estimation of three-level random-intercept logistic regression models
878(8)
16.4.1 Using gllamm
878(5)
16.4.2 Using xtmelogit
883(3)
16.5 A three-level random-coefficient logistic regression model
886(1)
16.6 Estimation of three-level random-coefficient logistic regression models
887(5)
16.6.1 Using gllamm
887(3)
16.6.2 Using xtmelogit
890(2)
16.7 Prediction of random effects
892(2)
16.7.1 Empirical Bayes prediction
892(1)
16.7.2 Empirical Bayes modal prediction
893(1)
16.8 Different kinds of predicted probabilities
894(3)
16.8.1 Predicted population-averaged or marginal probabilities: New clusters
894(1)
16.8.2 Predicted median or conditional probabilities
895(1)
16.8.3 Predicted posterior mean probabilities: Existing clusters
896(1)
16.9 Do salamanders from different populations mate successfully?
897(3)
16.10 Crossed random-effects logistic regression
900(7)
16.11 Summary and further reading
907(1)
16.12 Exercises
908(33)
A Syntax for gllamm, eq, and gllapred: The bare essentials
915(6)
B Syntax for gllamm
921(12)
C Syntax for gllapred
933(4)
D Syntax for gllasim
937(4)
References 941(14)
Author index 955(8)
Subject index 963