Multilevel and longitudinal modeling using Stata: Volume 2 Categorical responses, counts, and survival
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
College Station, Tex.
Stata Press
2012
|
| Ausgabe: | Third edition |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxiii Seiten, Seite 501 - 974 Diagramme, Karten |
| ISBN: | 9781597181044 1597181048 |
Internformat
MARC
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| 020 | |a 9781597181044 |9 978-1-59718-104-4 | ||
| 020 | |a 1597181048 |9 1-59718-104-8 | ||
| 035 | |a (OCoLC)796196816 | ||
| 035 | |a (DE-599)BVBBV040035509 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
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| 084 | |a ST 601 |0 (DE-625)143682: |2 rvk | ||
| 084 | |a QH 234 |0 (DE-625)141549: |2 rvk | ||
| 100 | 1 | |a Rabe-Hesketh, Sophia |e Verfasser |0 (DE-588)13573116X |4 aut | |
| 245 | 1 | 0 | |a Multilevel and longitudinal modeling using Stata |n Volume 2 |p Categorical responses, counts, and survival |c Sophia Rabe-Hesketh ; Anders Skrondal |
| 250 | |a Third edition | ||
| 264 | 1 | |a College Station, Tex. |b Stata Press |c 2012 | |
| 300 | |a xxiii Seiten, Seite 501 - 974 |b Diagramme, Karten | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 700 | 1 | |a Skrondal, Anders |d 1961- |e Verfasser |0 (DE-588)14306097X |4 aut | |
| 773 | 0 | 8 | |w (DE-604)BV040035494 |g 2 |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024892317&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-024892317 | ||
Datensatz im Suchindex
| _version_ | 1804149034391175168 |
|---|---|
| adam_text | Contents
List of Tables
xvii
List of Figures
xix
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
................................. 501
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
spécification
.................. 520
Two-stage formulation
.................... 522
10.7
Estimation of random-intercept logistic models
............ 523
10.7.1
Using xtiogit
.......................... 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
viii Contents
10.9
Measures of dependence and heterogeneity
.............. 532
10.9.1
Conditional or residual intraclass correlation of the la¬
tent responses
......................... 532
10.9.2
Median odds ratio
....................... 533
10.9.3 *♦*
Measures of association for observed responses at me¬
dian 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
<î*
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 1 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 prob¬
abilities
........................ 549
Predictions for the subjects in the sample: Posterior
mean probabilities
.................. 551
10.14
Other approaches to clustered diehotomou.s data
........... 557
10.14.1
Conditional logistic regression
................ 557
10.14.2
Generalized estimating equations (GEE)
.......... 55!)
10.15
Summary and further reading
..................... 562
10.16
Exercises
................................. 563
11
Ordinal responses
575
11.1
Introduction
............................... 575
Contents ix
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
responsos
..... 595
Median odds ratio
....................... 596
11.7
A random-coefficient
proportional
odds model
............
r>9(>
11.7.1
Motk4!
specificat
ion
...................... >!)(>
11.7.2
Estimation using glbunm
................... . >·)(>
11.8
Different kinds of predicted
probabilit
ies
...............
ľ>99
11.8.1
Predicted population-averaged or marginal probabilities
. . 599
11.8.2
Predicted .subjeet-specifie
probabilit
ies:
Posterior mean
. .
(І02
11.9
Do experts differ in their grading of student essays?
.........
(>0t>
11.10
A random-intercept
probit
model with grader bias
.......... 60(5
11.10.1
Model specification
...................... (¡00
11.10.2
Estimation using gllamm
...................
(JU7
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 ♦♦♦
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 model«
.................. 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 ran¬
dom coefficients
........................ 663
12.7.3
V Preference and response heterogeneity
.......... 666
Estimation using gllainm
................... 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
.....................
(j!)2
13.6.1
Model specification
...................... 692
13.6.2
Estimation using
Stata
....................
(НІЗ
13.7
Random-intercept
Poisson
regression
................. 690
13.7.1
Model specification
...................... 606
13.7.2
Measures of dependence and heterogeneity
.........
f!97
13.7.3
Estimation using
Stata
.................... (¡97
Using xt
poisson
........................
Ii97
Using xtmepoisson
....................... 699
Using gllanmi
......................... 700
13.8
Random-coefficient
Poisson
regression
................. 701
13.8.1
Model specification
...................... 01
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
V 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
.spécification
...................... 723
13.15.2
Estimation using gllanim
................... 724
13.15.3
Prediction of standardized mortality ratios
......... 725
13.10
Φ
Nonparametric maximum likelihood estimation
.......... 727
13.16.1
Specification
.......................... 727
13.16.2
Est
imat
ion using
głlamin
................... 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
.................... 702
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
V Population-averaged or marginal vs. subjeef-spoeific or condi¬
tional survival probabilities
....................... 784
14.9
Summary and further reading
..................... 788
14.10
Exercises
................................. 78!)
15
Continuous-time survival
797
IS.
І
introduction
............................... 707
15.2
What makes marriages fail?
...................... 797
15.3
Hazards and survival
.......................... 790
15.4
Proportional hazards models
...................... 805
15.4.1
Piecewise exponential model
.................
SOT
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
Différent
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.il
ЕхегеЫек
................................. 802
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
spécification ......................
876
16.3.2
Measures of dependence and heterogeneity
......... 876
Types of residual intraclass correlations of the latent re¬
sponses
........................ 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
............... 891
16.8.1
Predicted population-averaged or marginal probabilities:
New clusters
.......................... 894
16.8.2
Predicted median or conditional probabilities
........
Ѕ<јГ)
16.8.3
Predicted posterior mean probabilities: Existing clusters
.
S!)tí
16.9
Do salamanders from different populations mate successfully?
. . .
8!>7
16.10
Crossed random-effects logistic regression
...............
¡НИ)
16.11
Summary and further reading
.....................
DOT
16.12
Exercises
................................. 908
A Syntax for gllamm, eq, and
głlapred:
The bare essentials
915
В
Syntax for gllamm
921
С
Syntax for
glłapred
933
D
Syntax for
głłasim
937
xvi Contents
References
941
Author index
955
Subject index
953
|
| any_adam_object | 1 |
| author | Rabe-Hesketh, Sophia Skrondal, Anders 1961- |
| author_GND | (DE-588)13573116X (DE-588)14306097X |
| author_facet | Rabe-Hesketh, Sophia Skrondal, Anders 1961- |
| author_role | aut aut |
| author_sort | Rabe-Hesketh, Sophia |
| author_variant | s r h srh a s as |
| building | Verbundindex |
| bvnumber | BV040035509 |
| classification_rvk | ST 261 ST 601 QH 234 |
| ctrlnum | (OCoLC)796196816 (DE-599)BVBBV040035509 |
| discipline | Informatik Wirtschaftswissenschaften |
| edition | Third edition |
| format | Book |
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| id | DE-604.BV040035509 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T00:16:37Z |
| institution | BVB |
| isbn | 9781597181044 1597181048 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024892317 |
| oclc_num | 796196816 |
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| owner | DE-473 DE-BY-UBG DE-11 DE-188 DE-19 DE-BY-UBM DE-522 DE-83 DE-29 DE-Re13 DE-BY-UBR DE-91S DE-BY-TUM DE-703 DE-945 DE-824 DE-20 DE-739 DE-384 DE-91 DE-BY-TUM DE-706 DE-N32 |
| owner_facet | DE-473 DE-BY-UBG DE-11 DE-188 DE-19 DE-BY-UBM DE-522 DE-83 DE-29 DE-Re13 DE-BY-UBR DE-91S DE-BY-TUM DE-703 DE-945 DE-824 DE-20 DE-739 DE-384 DE-91 DE-BY-TUM DE-706 DE-N32 |
| physical | xxiii Seiten, Seite 501 - 974 Diagramme, Karten |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Stata Press |
| record_format | marc |
| spelling | Rabe-Hesketh, Sophia Verfasser (DE-588)13573116X aut Multilevel and longitudinal modeling using Stata Volume 2 Categorical responses, counts, and survival Sophia Rabe-Hesketh ; Anders Skrondal Third edition College Station, Tex. Stata Press 2012 xxiii Seiten, Seite 501 - 974 Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Skrondal, Anders 1961- Verfasser (DE-588)14306097X aut (DE-604)BV040035494 2 Digitalisierung UB Bamberg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024892317&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rabe-Hesketh, Sophia Skrondal, Anders 1961- Multilevel and longitudinal modeling using Stata |
| title | Multilevel and longitudinal modeling using Stata |
| title_auth | Multilevel and longitudinal modeling using Stata |
| title_exact_search | Multilevel and longitudinal modeling using Stata |
| title_full | Multilevel and longitudinal modeling using Stata Volume 2 Categorical responses, counts, and survival Sophia Rabe-Hesketh ; Anders Skrondal |
| title_fullStr | Multilevel and longitudinal modeling using Stata Volume 2 Categorical responses, counts, and survival Sophia Rabe-Hesketh ; Anders Skrondal |
| title_full_unstemmed | Multilevel and longitudinal modeling using Stata Volume 2 Categorical responses, counts, and survival Sophia Rabe-Hesketh ; Anders Skrondal |
| title_short | Multilevel and longitudinal modeling using Stata |
| title_sort | multilevel and longitudinal modeling using stata categorical responses counts and survival |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024892317&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV040035494 |
| work_keys_str_mv | AT rabeheskethsophia multilevelandlongitudinalmodelingusingstatavolume2 AT skrondalanders multilevelandlongitudinalmodelingusingstatavolume2 |