Unified Methods for Censored Longitudinal Data and Causality:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2003
|
| Schriftenreihe: | Springer Series in Statistics
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. The goal of such studies can be formulated as estimation of a finite dimensional parameter of the population distribution corresponding to the observed time- dependent process. Such estimation problems arise in survival analysis, causal inference and regression analysis. This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures subject to informative censoring and treatment assignment in so called semiparametric models. Semiparametric models are particularly attractive since they allow the presence of large unmodeled nuisance parameters. These techniques include estimation of regression parameters in the familiar (multivariate) generalized linear regression and multiplicative intensity models. They go beyond standard statistical approaches by incorporating all the observed data to allow for informative censoring, to obtain maximal efficiency, and by developing estimators of causal effects. It can be used to teach masters and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data |
| Beschreibung: | 1 Online-Ressource (XII, 399 p) |
| ISBN: | 9780387217000 9781441930552 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/978-0-387-21700-0 |
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Datensatz im Suchindex
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| discipline | Mathematik |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:03Z |
| institution | BVB |
| isbn | 9780387217000 9781441930552 |
| issn | 0172-7397 |
| language | English |
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| spelling | Laan, Mark J. Verfasser aut Unified Methods for Censored Longitudinal Data and Causality by Mark J. Laan, James M. Robins New York, NY Springer New York 2003 1 Online-Ressource (XII, 399 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Statistics 0172-7397 During the last decades, there has been an explosion in computation and information technology. This development comes with an expansion of complex observational studies and clinical trials in a variety of fields such as medicine, biology, epidemiology, sociology, and economics among many others, which involve collection of large amounts of data on subjects or organisms over time. The goal of such studies can be formulated as estimation of a finite dimensional parameter of the population distribution corresponding to the observed time- dependent process. Such estimation problems arise in survival analysis, causal inference and regression analysis. This book provides a fundamental statistical framework for the analysis of complex longitudinal data. It provides the first comprehensive description of optimal estimation techniques based on time-dependent data structures subject to informative censoring and treatment assignment in so called semiparametric models. Semiparametric models are particularly attractive since they allow the presence of large unmodeled nuisance parameters. These techniques include estimation of regression parameters in the familiar (multivariate) generalized linear regression and multiplicative intensity models. They go beyond standard statistical approaches by incorporating all the observed data to allow for informative censoring, to obtain maximal efficiency, and by developing estimators of causal effects. It can be used to teach masters and Ph.D. students in biostatistics and statistics and is suitable for researchers in statistics with a strong interest in the analysis of complex longitudinal data Statistics Mathematical statistics Statistical Theory and Methods Statistics for Life Sciences, Medicine, Health Sciences Statistik Längsschnittuntersuchung (DE-588)4034036-3 gnd rswk-swf Semiparametrisches Modell (DE-588)4232479-8 gnd rswk-swf Schätzung (DE-588)4193791-0 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Längsschnittuntersuchung (DE-588)4034036-3 s Datenanalyse (DE-588)4123037-1 s Semiparametrisches Modell (DE-588)4232479-8 s Schätzung (DE-588)4193791-0 s 1\p DE-604 Robins, James M. Sonstige oth https://doi.org/10.1007/978-0-387-21700-0 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Laan, Mark J. Unified Methods for Censored Longitudinal Data and Causality Statistics Mathematical statistics Statistical Theory and Methods Statistics for Life Sciences, Medicine, Health Sciences Statistik Längsschnittuntersuchung (DE-588)4034036-3 gnd Semiparametrisches Modell (DE-588)4232479-8 gnd Schätzung (DE-588)4193791-0 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4034036-3 (DE-588)4232479-8 (DE-588)4193791-0 (DE-588)4123037-1 |
| title | Unified Methods for Censored Longitudinal Data and Causality |
| title_auth | Unified Methods for Censored Longitudinal Data and Causality |
| title_exact_search | Unified Methods for Censored Longitudinal Data and Causality |
| title_full | Unified Methods for Censored Longitudinal Data and Causality by Mark J. Laan, James M. Robins |
| title_fullStr | Unified Methods for Censored Longitudinal Data and Causality by Mark J. Laan, James M. Robins |
| title_full_unstemmed | Unified Methods for Censored Longitudinal Data and Causality by Mark J. Laan, James M. Robins |
| title_short | Unified Methods for Censored Longitudinal Data and Causality |
| title_sort | unified methods for censored longitudinal data and causality |
| topic | Statistics Mathematical statistics Statistical Theory and Methods Statistics for Life Sciences, Medicine, Health Sciences Statistik Längsschnittuntersuchung (DE-588)4034036-3 gnd Semiparametrisches Modell (DE-588)4232479-8 gnd Schätzung (DE-588)4193791-0 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Statistics Mathematical statistics Statistical Theory and Methods Statistics for Life Sciences, Medicine, Health Sciences Statistik Längsschnittuntersuchung Semiparametrisches Modell Schätzung Datenanalyse |
| url | https://doi.org/10.1007/978-0-387-21700-0 |
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