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BK

3rd ed.

College Station : Stata Press, 2012

2 sv. : il. ; 24 cm

ISBN 978-1-59718-108-2 (soubor ; brož.) ISBN !159718-108-0 (chyb.)

ISBN 978-1-59718-103-7 (vol 1 ; brož.) ISBN !159718-103-X (chyb.)

ISBN 978-1-59718-104-4 (vo. 2 ; brož.) ISBN !1-59718-104-8 (chyb.)

Obsahuje bibliografie a rejstříky

Vol. 1. Continuous responses -- Vol. 2. Categorical responses, counts, and survival

000246904

Contents // List of Tables xv // List of Figures x x // V Models for categorical responses 499 // 10 Dichotomous or binary responses 501 // 10.1 Introduction... 501 // 10.2 Single-level logit and probit regression models for dichotomous // responses... 1 // 10.2.1 Generalized linear model formulation... 502 // 10.2.2 Latent-response formulation... 510 // Logistic regression... 512 // Probit regression... 512 // 10.3 Which treatment is best for toenail infection?... 515 // 10.4 Longitudinal data structure ... 515 // 10.5 Proportions and fitted population-averaged or marginal // probabilities... 517 // 10.6 Random-intercept logistic regression... 520 // 10.6.1 Model specification... 520 // Reduced-form specification... 520 // Two-stage formulation ... 522 // 10.7 Estimation of random-intercept logistic models... 523 // 10.7.1 Using xtlogit... 523 // 10.7.2 Using xtmelogit... 527 // 10.7.3 Using gllamm ... 527 // 10.8 Subject-specific or conditional vs. population-averaged or // marginal relationships... 529 // Vlil // Contents // 10.9 Measures of dependence and heterogeneity... 532 // 10.9.1 Conditional or residual intraclass correlation of the latent responses... 532 // 10.9.2 Median odds ratio... 533 // 10.9.3 Measures of association for observed responses at median fixed part of the model ... 533 // 10.10 Inference for random-intercept logistic models... 535 // 10.10.1 Tests and confidence intervals for odds ratios... 535 // 10.10.2 Tests of variance components... 536 // 10.11

Maximum likelihood estimation... 537 // 10.11.1 Adaptive quadrature ... 537 // 10.11.2 Some speed and accuracy considerations ... 540 // Advice for speeding up estimation in gllamm... 542 // 10.12 Assigning values to random effects... 543 // 10.12.1 Maximum “likelihood” estimation... 544 // 10.12.2 Empirical Bayes prediction... 545 // 10.12.3 Empirical Bayes modal prediction... 546 // 10.13 Different kinds of predicted probabilities... 548 // 10.13.1 Predicted population-averaged or marginal probabilities . . 548 // 10.13.2 Predicted subject-specific probabilities ... 549 // Predictions for hypothetical subjects: Conditional probabilities ... 549 // Predictions for the subjects in the sample: Posterior // mean probabilities... 551 // 10.14 Other approaches to clustered dichotomous data... 557 // 10.14.1 Conditional logistic regression... 557 // 10.14.2 Generalized estimating equations (GEE) ... 559 // 10.15 Summary and further reading... 562 // 10.16 Exercises... 563 // 11 Ordinal responses 575 // 11.1 Introduction... 575 // Contents 1X // 11.2 Single-level cumulative models for ordinal responses... 575 // 11.2.1 Generalized linear model formulation... 575 // 11.2.2 Latent-response formulation... 576 // 11.2.3 Proportional odds... 580 // 11.2.4 ??? Identification... 582 // 11.3 Are antipsychotic drugs effective for patients with schizophrenia? . 585 // 11.4 Longitudinal data structure and graphs... 585 // 11.4.1 Longitudinal data structure... 586 // 11.4.2 Plotting cumulative

proportions... 587 // 11.4.3 Plotting cumulative sample logits and transforming the // time scale... 588 // 11.5 A single-level proportional odds model... 590 // 11.5.1 Model specification... 590 // 11.5.2 Estimation using Stata... 591 // 11.6 A random-intercept proportional odds model... 594 // 11.6.1 Model specification... 594 // 11.6.2 Estimation using Stata... 594 // 11.6.3 Measures of dependence and heterogeneity... 595 // Residual intraclass correlation of latent responses...595 // Median odds ratio... 596 // 11.7 A random-coefficient proportional odds model... 596 // 11.7.1 Model specification... 596 // 11.7.2 Estimation using gllamm...*... 596 // 11.8 Different kinds of predicted probabilities... 599 // 11.8.1 Predicted population-averaged or marginal probabilities . . 599 // 11.8.2 Predicted subject-specific probabilities: Posterior mean . . 602 // 11.9 Do experts differ in their grading of student essays?... 606 // 11.10 A random-intercept probit model with grader bias...606 // 11.10.1 Model specification... 605 // 11.10.2 Estimation using gllamm... 607 // x Contents // 11.11 Including grader-specific measurement error variances...608 // 11.11.1 Model specification...608 // 11.11.2 Estimation using gllamm...609 // 11.12 K* Including grader-specific thresholds...611 // 11.12.1 Model specification... 611 // 11.12.2 Estimation using gllamm... 611 // 11.13 ??? Other link functions...616 // Cumulative complementary log-log model...616 // Continuation-ratio logit model...616

// Adjacent-category logit model... 618 // Baseline-category logit and stereotype models ...618 // 11.14 Summary and further reading... 619 // 11.15 Exercises...620 // 12 Nominal responses and discrete choice 629 // 12.1 Introduction...629 // 12.2 Single-level models for nominal responses... 630 // 12.2.1 Multinomial logit models...630 // 12.2.2 Conditional logit models...638 // Classical conditional logit models ...639 // Conditional logit models also including covariates that // vary only over units... 645 // 12.3 Independence from irrelevant alternatives... 648 // 12.4 Utility-maximization formulation ...649 // 12.5 Does marketing affect choice of yogurt?...651 // 12.6 Single-level conditional logit models... 653 // 12.6.1 Conditional logit models with alternative-specific // intercepts...654 // 12.7 Multilevel conditional logit models...659 // 12.7.1 Preference heterogeneity: Brand-specific random // intercepts...659 // Contents // XI // 12.7.2 Response heterogeneity: Marketing variables with random coefficients... 663 // 12.7.3 ??? Preference and response heterogeneity... 666 // Estimation using gllamm... 667 // Estimation using mixlogit... 669 // 12.8 Prediction of random effects and response probabilities...672 // 12.9 Summary and further reading... 676 // 12.10 Exercises...677 // VI Models for counts 685 // 13 Counts 687 // 13.1 Introduction... 687 // 13.2 What are counts?... 687 // 13.2.1 Counts versus proportions ... 687 // 13.2.2 Counts as aggregated event-history data

... 688 // 13.3 Single-level Poisson models for counts... 689 // 13.4 Did the German health-care reform reduce the number of doctor // visits?... 691 // 13.5 Longitudinal data structure ... 691 // 13.6 Single-level Poisson regression... 692 // 13.6.1 Model specification... 692 // 13.6.2 Estimation using Stata... 693 // 13.7 Random-intercept Poisson regression... 696 // 13.7.1 Model specification... 696 // 13.7.2 Measures of dependence and heterogeneity... 697 // 13.7.3 Estimation using Stata... 697 // Using xtpoisson... 697 // Using xtmepoisson... 699 // Using gllamm ... 706 // 13.8 Random-coefficient Poisson regression... 701 // 13.8.1 Model specification... 701 // xii Contents // 13.8.2 Estimation using Stata... 702 // Using xtmepoisson... 702 // Using gllamm ... 704 // 13.8.3 Interpretation of estimates... 705 // 13.9 Overdispersion in single-level models ... 706 // 13.9.1 Normally distributed random intercept... 706 // 13.9.2 Negative binomial models... 707 // Mean dispersion or NB2 ... 708 // Constant dispersion or NB1 ... 709 // 13.9.3 Quasilikelihood... 709 // 13.10 Level-1 overdispersion in two-level models ... 711 // 13.11 Other approaches to two-level count data... 713 // 13.11.1 Conditional Poisson regression... 713 // 13.11.2 Conditional negative binomial regression... 715 // 13.11.3 Generalized estimating equations... 715 // 13.12 Marginal and conditional effects when responses are MAR...716 // ??? Simulation... 717 // 13.13 Which Scottish counties have a high risk

of // lip cancer?... 720 // 13.14 Standardized mortality ratios ... 721 // 13.15 Random-intercept Poisson regression... 723 // 13.15.1 Model specification... 723 // 13.15.2 Estimation using gllamm... 724 // 13.15.3 Prediction of standardized mortality ratios... 725 // 13.16 ??? Nonparametric maximum likelihood estimation... 727 // 13.16.1 Specification... 727 // 13.16.2 Estimation using gllamm... 727 // 13.16.3 Prediction ... 732 // 13.17 Summary and further reading... 732 // 13.18 Exercises... 733 // Contents xiii // VII Models for survival or duration data 741 // Introduction to models for survival or duration data (part VII) 743 // 14 Discrete-time survival 749 // 14.1 Introduction... 749 // 14.2 Single-level models for discrete-time survival data... 749 // 14.2.1 Discrete-time hazard and discrete-time survival ...749 // 14.2.2 Data expansion for discrete-time survival analysis...752 // 14.2.3 Estimation via regression models for dichotomous // responses... 754 // 14.2.4 Including covariates... 758 // Time-constant covariates... 758 // Time-varying covariates... 762 // 14.2.5 Multiple absorbing events and competing risks... 767 // 14.2.6 Handling left-truncated data... 772 // 14.3 How does birth history affect child mortality?... 773 // 14.4 Data expansion... 774 // 14.5 Proportional hazards and interval-censoring... 776 // 14.6 Complementary log-log models... 777 // 14.7 A random-intercept complementary log-log model... 781 // 14.7.1 Model specification... 781 // 14.7.2 Estimation

using Stata... 782 // 14.8 Population-averaged or marginal vs. subject-specific or conditional survival probabilities... 784 // 14.9 Summary and further reading... 788 // 14.10 Exercises... 789 // 15 Continuous-time survival 797 // 15.1 Introduction... 797 // 15.2 What makes marriages fail? ... 797 // 15.3 Hazards and survival... 799 // 15.4 Proportional hazards models... 805 // 15.4.1 Piecewise exponential model... 807 // XIV // Contents // 15.4.2 Cox regression model...815 // 15.4.3 Poisson regression with smooth baseline hazard ...819 // 15.5 Accelerated failure-time models...823 // 15.5.1 Log-normal model...824 // 15.6 Time-varying covariates... 829 // 15.7 Does nitrate reduce the risk of angina pectoris?...832 // 15.8 Marginal modeling ... 835 // 15.8.1 Cox regression...835 // 15.8.2 Poisson regression with smooth baseline hazard ...838 // 15.9 Multilevel proportional hazards models... 841 // 15.9.1 Cox regression with gamma shared frailty...841 // 15.9.2 Poisson regression with normal random intercepts...845 // 15.9.3 Poisson regression with normal random intercept and // random coefficient... 847 // 15.10 Multilevel accelerated failure-time models...849 // 15.10.1 Log-normal model with gamma shared frailty...849 // 15.10.2 Log-normal model with log-normal shared frailty...850 // 15.11 A fixed-effects approach... 851 // 15.11.1 Cox regression with subject-specific baseline hazards ... 851 // 15.12 Different approaches to recurrent-event data...853 // 15.12.1 Total time...

854 // 15.12.2 Counting process ...858 // 15.12.3 Gap time...859 // 15.13 Summary and further reading... 861 // 15.14 Exercises...862 // VIII Models with nested and crossed random effects 871 // 16 Models with nested and crossed random effects 873 // 16.1 Introduction...873 // 16.2 Did the Guatemalan immunization campaign work?...873 // 16.3 A three-level random-intercept logistic regression model...875 // Contents // XV // 16.3.1 Model specification... 876 // 16.3.2 Measures of dependence and heterogeneity... 876 // Types of residual intraclass correlations of the latent responses ... 876 // Types of median odds ratios... 877 // 16.3.3 Three-stage formulation... 877 // 16.4 Estimation of three-level random-intercept logistic regression // models... 878 // 16.4.1 Using gllamm ... 878 // 16.4.2 Using xtmelogit... 883 // 16.5 A three-level random-coefficient logistic regression model...886 // 16.6 Estimation of three-level random-coefficient logistic regression // models... 887 // 16.6.1 Using gllamm ... 887 // 16.6.2 Using xtmelogit... 890 // 16.7 Prediction of random effects... 892 // 16.7.1 Empirical Bayes prediction... 892 // 16.7.2 Empirical Bayes modal prediction... 893 // 16.8 Different kinds of predicted probabilities... 894 // 16.8.1 Predicted population-averaged or marginal probabilities: // New clusters... 894 // 16.8.2 Predicted median or conditional probabilities... 895 // 16.8.3 Predicted posterior mean probabilities: Existing clusters . 896 // 16.9 Do salamanders

from different populations mate successfully? . . . 897 // 16.10 Crossed random-effects logistic regression... 900 // 16.11 Summary and further reading... 907 // 16.12 Exercises... 908 // A Syntax for gllamm, eq, and gllapred: The bare essentials 915 // ? Syntax for gllamm 921 // C Syntax for gllapred 933 // D Syntax for gllasim 937 // Contents // 941 // 955 // References Author index Subject index // 963