Multilevel and longitudinal modeling using Stata 第三版 Volume 2 下载，请悬赏的同学先购买，然后再降价吧。感谢voodoo帮忙制作图书。第一卷论坛上已经有，可以自行下载。
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http://www.stata.com/bookstore/multilevel-longitudinal-modeling-stata/index.html
V Models for categorical responses
10 Dichotomous or binary responses (pdf)
10.1 Introduction
10.2 Single-level logit and probit regression models for dichotomous responses
10.2.1 Generalized linear model formulation
10.2.2 Latent-response formulation
Logistic regression
Probit regression
10.3 Which treatment is best for toenail infection?
10.4 Longitudinal data structure
10.5 Proportions and fitted population-averaged or marginal probabilities
10.6 Random-intercept logistic regression
10.6.1 Model specification
Reduced-form specification
Two-stage formulation
10.7 Estimation of random-intercept logistic models
10.7.1 Using xtlogit
10.7.2 Using xtmelogit
10.7.3 Using gllamm
10.8 Subject-specific or conditional vs. population-averaged or marginal relationships
10.9 Measures of dependence and heterogeneity
10.9.1 Conditional or residual intraclass correlation of the latent responses
10.9.2 Median odds ratio
10.9.3 Measures of association for observed responses at median fixed part of the model
10.10 Inference for random-intercept logistic models
10.10.1 Tests and confidence intervals for odds ratios
10.10.2 Tests of variance components
10.11 Maximum likelihood estimation
10.11.1 Adaptive quadrature
10.11.2 Some speed and accuracy considerations
Advice for speeding up estimation in gllamm
10.12 Assigning values to random effects
10.12.1 Maximum “likelihood” estimation
10.12.2 Empirical Bayes prediction
10.12.3 Empirical Bayes modal prediction
10.13 Different kinds of predicted probabilities
10.13.1 Predicted population-averaged or marginal probabilities
10.13.2 Predicted subject-specific probabilities
Predictions for hypothetical subjects: Conditional probabilities
Predictions for the subjects in the sample: Posterior mean probabilities
10.14 Other approaches to clustered dichotomous data
10.14.1 Conditional logistic regression
10.14.2 Generalized estimating equations (GEE)
10.15 Summary and further reading
10.16 Exercises
11 Ordinal responses
11.1 Introduction
11.2 Single-level cumulative models for ordinal responses
11.2.1 Generalized linear model formulation
11.2.2 Latent-response formulation
11.2.3 Proportional odds
11.2.4 Identification
11.3 Are antipsychotic drugs effective for patients with schizophrenia?
11.4 Longitudinal data structure and graphs
11.4.1 Longitudinal data structure
11.4.2 Plotting cumulative proportions
11.4.3 Plotting cumulative sample logits and transforming the time scale
11.5 A single-level proportional odds model
11.5.1 Model specification
11.5.2 Estimation using Stata
11.6 A random-intercept proportional odds model
11.6.1 Model specification
11.6.2 Estimation using Stata
11.6.3 Measures of dependence and heterogeneity
Residual intraclass correlation of latent responses
Median odds ratio
11.7 A random-coefficient proportional odds model
11.7.1 Model specification
11.7.2 Estimation using gllamm
11.8 Different kinds of predicted probabilities
11.8.1 Predicted population-averaged or marginal probabilities
11.8.2 Predicted subject-specific probabilities: Posterior mean
11.9 Do experts differ in their grading of student essays?
11.10 A random-intercept probit model with grader bias
11.10.1 Model specification
11.10.2 Estimation using gllamm
11.11 Including grader-specific measurement error variances
11.11.1 Model specification
11.11.2 Estimation using gllamm
11.12 Including grader-specific thresholds
11.12.1 Model specification
11.12.2 Estimation using gllamm
11.13 Other link functions
Cumulative complementary log-log model
Continuation-ratio logit model
Adjacent-category logit model
Baseline-category logit and stereotype models
11.14 Summary and further reading
11.15 Exercises
12 Nominal responses and discrete choice
12.1 Introduction
12.2 Single-level models for nominal responses
12.2.1 Multinomial logit models
12.2.2 Conditional logit models
Classical conditional logit models
Conditional logit models also including covariates that vary only over units
12.3 Independence from irrelevant alternatives
12.4 Utility-maximization formulation
12.5 Does marketing affect choice of yogurt?
12.6 Single-level conditional logit models
12.6.1 Conditional logit models with alternative-specific intercepts
12.7 Multilevel conditional logit models
12.7.1 Preference heterogeneity: Brand-specific random intercepts
12.7.2 Response heterogeneity: Marketing variables with random coefficients
12.7.3 Preference and response heterogeneity
Estimation using gllamm
Estimation using mixlogit
12.8 Prediction of random effects and response probabilities
12.9 Summary and further reading
12.10 Exercises
VI Models for counts
13 Counts
13.1 Introduction
13.2 What are counts?
13.2.1 Counts versus proportions
13.2.2 Counts as aggregated event-history data
13.3 Single-level Poisson models for counts
13.4 Did the German health-care reform reduce the number of doctor visits?
13.5 Longitudinal data structure
13.6 Single-level Poisson regression
13.6.1 Model specification
13.6.2 Estimation using Stata
13.7 Random-intercept Poisson regression
13.7.1 Model specification
13.7.2 Measures of dependence and heterogeneity
13.7.3 Estimation using Stata
Using xtpoisson
Using xtmepoisson
Using gllamm
13.8 Random-coefficient Poisson regression
13.8.1 Model specification
13.8.2 Estimation using Stata
Using xtmepoisson
Using gllamm
13.8.3 Interpretation of estimates
13.9 Overdispersion in single-level models
13.9.1 Normally distributed random intercept
13.9.2 Negative binomial models
Mean dispersion or NB2
Constant dispersion or NB1
13.9.3 Quasilikelihood
13.10 Level-1 overdispersion in two-level models
13.11 Other approaches to two-level count data
13.11.1 Conditional Poisson regression
13.11.2 Conditional negative binomial regression
13.11.3 Generalized estimating equations
13.12 Marginal and conditional effects when responses are MAR
13.13 Which Scottish counties have a high risk of lip cancer?
13.14 Standardized mortality ratios
13.15 Random-intercept Poisson regression
13.15.1 Model specification
13.15.2 Estimation using gllamm
13.15.3 Prediction of standardized mortality ratios
13.16 Nonparametric maximum likelihood estimation
13.16.1 Specification
13.16.2 Estimation using gllamm
13.16.3 Prediction
13.17 Summary and further reading
13.18 Exercises
VII Models for survival or duration data
Introduction to models for survival or duration data (part VII)
14 Discrete-time survival
14.1 Introduction
14.2 Single-level models for discrete-time survival data
14.2.1 Discrete-time hazard and discrete-time survival
14.2.2 Data expansion for discrete-time survival analysis
14.2.3 Estimation via regression models for dichotomous responses
14.2.4 Including covariates
Time-constant covariates
Time-varying covariates
14.2.5 Multiple absorbing events and competing risks
14.2.6 Handling left-truncated data
14.3 How does birth history affect child mortality?
14.4 Data expansion
14.5 Proportional hazards and interval-censoring
14.6 Complementary log-log models
14.7 A random-intercept complementary log-log model
14.7.1 Model specification
14.7.2 Estimation using Stata
14.8 Population-averaged or marginal vs. subject-specific or conditional survival probabilities
14.9 Summary and further reading
14.10 Exercises
15 Continuous-time survival
15.1 Introduction
15.2 What makes marriages fail?
15.3 Hazards and survival
15.4 Proportional hazards models
15.4.1 Piecewise exponential model
15.4.2 Cox regression model
15.4.3 Poisson regression with smooth baseline hazard
15.5 Accelerated failure-time models
15.5.1 Log-normal model
15.6 Time-varying covariates
15.7 Does nitrate reduce the risk of angina pectoris?
15.8 Marginal modeling
15.8.1 Cox regression
15.8.2 Poisson regression with smooth baseline hazard
15.9 Multilevel proportional hazards models
15.9.1 Cox regression with gamma shared frailty
15.9.2 Poisson regression with normal random intercepts
15.9.3 Poisson regression with normal random intercept and random coefficient
15.10 Multilevel accelerated failure-time models
15.10.1 Log-normal model with gamma shared frailty
15.10.2 Log-normal model with log-normal shared frailty
15.11 A fixed-effects approach
15.11.1 Cox regression with subject-specific baseline hazards
15.12 Different approaches to recurrent-event data
15.12.1 Total time
15.12.2 Counting process
15.12.3 Gap time
15.13 Summary and further reading
15.14 Exercises
VIII Models with nested and crossed random effects
16 Models with nested and crossed random effects
16.1 Introduction
16.2 Did the Guatemalan immunization campaign work?
16.3 A three-level random-intercept logistic regression model
16.3.1 Model specification
16.3.2 Measures of dependence and heterogeneity
Types of residual intraclass correlations of the latent responses
Types of median odds ratios
16.3.3 Three-stage formulation
16.4 Estimation of three-level random-intercept logistic regression models
16.4.1 Using gllamm
16.4.2 Using xtmelogit
16.5 A three-level random-coefficient logistic regression model
16.6 Estimation of three-level random-coefficient logistic regression models
16.6.1 Using gllamm
16.6.2 Using xtmelogit
16.7 Prediction of random effects
16.7.1 Empirical Bayes prediction
16.7.2 Empirical Bayes modal prediction
16.8 Different kinds of predicted probabilities
16.8.1 Predicted population-averaged or marginal probabilities: New clusters
16.8.2 Predicted median or conditional probabilities
16.8.3 Predicted posterior mean probabilities: Existing clusters
16.9 Do salamanders from different populations mate successfully?
16.10 Crossed random-effects logistic regression
16.11 Summary and further reading
16.12 Exercises
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