Dynamic regression models for survival data:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer
2006
|
| Schriftenreihe: | Statistics for biology and health
|
| Schlagworte: | |
| Online-Zugang: | Klappentext Inhaltsverzeichnis |
| Beschreibung: | XIII, 470 S. zahlr. graph. Darst. |
| ISBN: | 0387202749 9780387202747 |
Internformat
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| 245 | 1 | 0 | |a Dynamic regression models for survival data |c Torben Martinussen ; Thomas H. Scheike |
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| 650 | 4 | |a Models, Statistical | |
| 650 | 4 | |a Regression Analysis | |
| 650 | 4 | |a Regression analysis | |
| 650 | 4 | |a Survival Analysis | |
| 650 | 4 | |a Survival analysis (Biometry) | |
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Datensatz im Suchindex
| _version_ | 1804135309692108800 |
|---|---|
| adam_text | In survival analysis there has long been a need for models that go beyond the Cox
model as the proportional hazards assumption often fails in practice. This book
studies and applies modern flexible regression models for survival data with a
special focus on extensions of the Cox model and alternative models with the spe¬
cific aim of describing time-varying effects of explanatory variables. One model
that receives special attention is Aalen s additive hazards model that is particular¬
ly well suited for dealing with time-varying effects. The book covers the use of
residuals and resampling techniques to assess the fit of the models and also points
out how the suggested models can be utilised for clustered survival data. The
authors demonstrate the practically important aspect of how to do hypothesis
testing of time-varying effects making backwards model selection strategies pos¬
sible for the flexible models considered.
The use of the suggested models and methods is illustrated on real data examples.
The methods are available in the R-package timereg developed by the authors,
which is applied throughout the book with worked examples for the data sets.
This gives the reader a unique chance of obtaining hands-on experience.
This book is well suited for statistical consultants as well as for those who would
like to see more about the theoretical justification of the suggested procedures. It
can be used as a textbook for a graduate/master course in survival analysis, and
students will appreciate the exercises included after each chapter. The applied side
of the book with many worked examples accompanied with R-code shows in
detail how one can analyse real data and at the same time gives a deeper under¬
standing of the underlying theory.
Torben Martinussen
Veterinary and Agricultural University. He has a Ph.D. from University of
Copenhagen and is associate editor of the Scandinavian Journal of Statistics.
Thomas H. Scheike is at the Department of Biostatistics at University of
Copenhagen. He has a Ph.D. from University of California at Berkeley and is
Doctor of Science at the University of Copenhagen. He is the editor of the
Scandinavian Journal of Statistics and associate editor of several other journals.
Contents
Preface vii
1
1.1
1.2
2
2.1
2.2
2.3
2.4
2.5
2.6
3
3.1
3.2
3.3
3.4
4
4.1
4.2
4.2.1
xii Contents
4.2.2
4.3
5
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
6
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
6.10
6.11
7
7.1
7.1.1
7.1.2
7.1.3
7.1.4
7.1.5
7.2
7.2.1
7.2.2
7.2.3
7.2.4
7.2.5
7.3
8
8.1
8.2
Contents xiii
8.3
9
9.1
9.1.1
9.1.2
9.1.3
<9.2 Frailty models
9.3
10
lOjl Product limit estimator
10:2
10.3
10.4
11
11.1
11.2
11.3
11.4
11.5
A Khmaladze s transformation
B Matrix derivatives
C The Timereg survival package for R
Bibliography
Index
|
| adam_txt |
In survival analysis there has long been a need for models that go beyond the Cox
model as the proportional hazards assumption often fails in practice. This book
studies and applies modern flexible regression models for survival data with a
special focus on extensions of the Cox model and alternative models with the spe¬
cific aim of describing time-varying effects of explanatory variables. One model
that receives special attention is Aalen's additive hazards model that is particular¬
ly well suited for dealing with time-varying effects. The book covers the use of
residuals and resampling techniques to assess the fit of the models and also points
out how the suggested models can be utilised for clustered survival data. The
authors demonstrate the practically important aspect of how to do hypothesis
testing of time-varying effects making backwards model selection strategies pos¬
sible for the flexible models considered.
The use of the suggested models and methods is illustrated on real data examples.
The methods are available in the R-package timereg developed by the authors,
which is applied throughout the book with worked examples for the data sets.
This gives the reader a unique chance of obtaining hands-on experience.
This book is well suited for statistical consultants as well as for those who would
like to see more about the theoretical justification of the suggested procedures. It
can be used as a textbook for a graduate/master course in survival analysis, and
students will appreciate the exercises included after each chapter. The applied side
of the book with many worked examples accompanied with R-code shows in
detail how one can analyse real data and at the same time gives a deeper under¬
standing of the underlying theory.
Torben Martinussen
Veterinary and Agricultural University. He has a Ph.D. from University of
Copenhagen and is associate editor of the Scandinavian Journal of Statistics.
Thomas H. Scheike is at the Department of Biostatistics at University of
Copenhagen. He has a Ph.D. from University of California at Berkeley and is
Doctor of Science at the University of Copenhagen. He is the editor of the
Scandinavian Journal of Statistics and associate editor of several other journals.
Contents
Preface vii
1
1.1
1.2
2
2.1
2.2
2.3
2.4
2.5
2.6
3
3.1
3.2
3.3
3.4
4
4.1
4.2
4.2.1
xii Contents
4.2.2
4.3
5
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
6
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
6.10
6.11
7
7.1
7.1.1
7.1.2
7.1.3
7.1.4
7.1.5
7.2
7.2.1
7.2.2
7.2.3
7.2.4
7.2.5
7.3
8
8.1
8.2
Contents xiii
8.3
9
9.1
9.1.1
9.1.2
9.1.3
<9.2 Frailty models
9.3
10
lOjl Product limit estimator
10:2
10.3
10.4
11
11.1
11.2
11.3
11.4
11.5
A Khmaladze's transformation
B Matrix derivatives
C The Timereg survival package for R
Bibliography
Index |
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| index_date | 2024-07-02T14:32:14Z |
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| isbn | 0387202749 9780387202747 |
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| spelling | Martinussen, Torben (DE-588)131662929 aut Dynamic regression models for survival data Torben Martinussen ; Thomas H. Scheike New York, NY Springer 2006 XIII, 470 S. zahlr. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Statistics for biology and health Analyse de la survie (Biométrie) Analyse de régression Models, Statistical Regression Analysis Regression analysis Survival Analysis Survival analysis (Biometry) Regressionsmodell (DE-588)4127980-3 gnd rswk-swf Ereignisdatenanalyse (DE-588)4132103-0 gnd rswk-swf Ereignisdatenanalyse (DE-588)4132103-0 s Regressionsmodell (DE-588)4127980-3 s b DE-604 Scheike, Thomas H. 1965- (DE-588)13166302X aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014768776&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Klappentext Digitalisierung UBRegensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014768776&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Martinussen, Torben Scheike, Thomas H. 1965- Dynamic regression models for survival data Analyse de la survie (Biométrie) Analyse de régression Models, Statistical Regression Analysis Regression analysis Survival Analysis Survival analysis (Biometry) Regressionsmodell (DE-588)4127980-3 gnd Ereignisdatenanalyse (DE-588)4132103-0 gnd |
| subject_GND | (DE-588)4127980-3 (DE-588)4132103-0 |
| title | Dynamic regression models for survival data |
| title_auth | Dynamic regression models for survival data |
| title_exact_search | Dynamic regression models for survival data |
| title_exact_search_txtP | Dynamic regression models for survival data |
| title_full | Dynamic regression models for survival data Torben Martinussen ; Thomas H. Scheike |
| title_fullStr | Dynamic regression models for survival data Torben Martinussen ; Thomas H. Scheike |
| title_full_unstemmed | Dynamic regression models for survival data Torben Martinussen ; Thomas H. Scheike |
| title_short | Dynamic regression models for survival data |
| title_sort | dynamic regression models for survival data |
| topic | Analyse de la survie (Biométrie) Analyse de régression Models, Statistical Regression Analysis Regression analysis Survival Analysis Survival analysis (Biometry) Regressionsmodell (DE-588)4127980-3 gnd Ereignisdatenanalyse (DE-588)4132103-0 gnd |
| topic_facet | Analyse de la survie (Biométrie) Analyse de régression Models, Statistical Regression Analysis Regression analysis Survival Analysis Survival analysis (Biometry) Regressionsmodell Ereignisdatenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014768776&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014768776&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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