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The Resource Innovative statistical methods for public health data, Ding-Geng (Din) Chen, Jeffrey Wilson, editors

Innovative statistical methods for public health data, Ding-Geng (Din) Chen, Jeffrey Wilson, editors

Editor
Contributor
Summary
The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference, and it can be used in graduate level classes
Language
eng
Work
Publication
Extent
1 online resource
Contents
  • Part 1: Modelling Clustered Data
  • Methods for Analyzing Secondary Outcomes in Public Health Case Control Studies
  • Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems
  • On the Inference of Partially Correlated Data with Applications to Public Health Issues
  • Modeling Time-Dependent Covariates in Longitudinal Data Analyses
  • Solving Probabilistic Discrete Event Systems with Moore-Penrose Generalized Inverse Matrix Method to Extract Longitudinal Characteristics from Cross-Sectional Survey Data
  • Part II: Modelling Incomplete or Missing Data
  • On the Effects of Structural Zeros in Regression Models
  • Modeling Based on Progressively Type-I Interval Censored Sample
  • Techniques for Analyzing Incomplete Data in Public Health Research
  • A Continuous Latent Factor Model for Non-ignorable Missing Data
  • Part III: Healthcare Research Models
  • Health Surveillance
  • Standardization and Decomposition Analysis: A Useful Analytical Method for Outcome Difference, Inequality and Disparity Studies
  • Cusp Catastrophe Modeling in Medical and Health Research
  • On Ranked Set Sampling Variation and its Applications to Public Health Research
  • Weighted Multiple Testing Correction for Correlated Endpoints in Survival Data
  • Meta-analytic Methods for Public Health Research
Isbn
9783319185354
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-18536-1
Instance
Label
Innovative statistical methods for public health data
Title
Innovative statistical methods for public health data
Statement of responsibility
Ding-Geng (Din) Chen, Jeffrey Wilson, editors
Contributor
Editor
Subject
Genre
Language
eng
Summary
The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference, and it can be used in graduate level classes
Member of
Cataloging source
N$T
Dewey number
614.407/27
Index
index present
LC call number
RA652.2.P82
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Chen, Ding-Geng
  • Wilson, Jeffrey
Series statement
ICSA book series in statistics,
http://library.link/vocab/subjectName
Public health
Label
Innovative statistical methods for public health data, Ding-Geng (Din) Chen, Jeffrey Wilson, editors
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-18536-1
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Part 1: Modelling Clustered Data -- Methods for Analyzing Secondary Outcomes in Public Health Case Control Studies -- Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems -- On the Inference of Partially Correlated Data with Applications to Public Health Issues -- Modeling Time-Dependent Covariates in Longitudinal Data Analyses -- Solving Probabilistic Discrete Event Systems with Moore-Penrose Generalized Inverse Matrix Method to Extract Longitudinal Characteristics from Cross-Sectional Survey Data -- Part II: Modelling Incomplete or Missing Data -- On the Effects of Structural Zeros in Regression Models -- Modeling Based on Progressively Type-I Interval Censored Sample -- Techniques for Analyzing Incomplete Data in Public Health Research -- A Continuous Latent Factor Model for Non-ignorable Missing Data -- Part III: Healthcare Research Models -- Health Surveillance -- Standardization and Decomposition Analysis: A Useful Analytical Method for Outcome Difference, Inequality and Disparity Studies -- Cusp Catastrophe Modeling in Medical and Health Research -- On Ranked Set Sampling Variation and its Applications to Public Health Research -- Weighted Multiple Testing Correction for Correlated Endpoints in Survival Data -- Meta-analytic Methods for Public Health Research
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9783319185354
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Other control number
10.1007/978-3-319-18536-1
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)919611846
  • (OCoLC)ocn919611846
Label
Innovative statistical methods for public health data, Ding-Geng (Din) Chen, Jeffrey Wilson, editors
Link
https://ezproxy.lib.ou.edu/login?url=http://link.springer.com/10.1007/978-3-319-18536-1
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Part 1: Modelling Clustered Data -- Methods for Analyzing Secondary Outcomes in Public Health Case Control Studies -- Controlling for Population Density Using Clustering and Data Weighting Techniques When Examining Social Health and Welfare Problems -- On the Inference of Partially Correlated Data with Applications to Public Health Issues -- Modeling Time-Dependent Covariates in Longitudinal Data Analyses -- Solving Probabilistic Discrete Event Systems with Moore-Penrose Generalized Inverse Matrix Method to Extract Longitudinal Characteristics from Cross-Sectional Survey Data -- Part II: Modelling Incomplete or Missing Data -- On the Effects of Structural Zeros in Regression Models -- Modeling Based on Progressively Type-I Interval Censored Sample -- Techniques for Analyzing Incomplete Data in Public Health Research -- A Continuous Latent Factor Model for Non-ignorable Missing Data -- Part III: Healthcare Research Models -- Health Surveillance -- Standardization and Decomposition Analysis: A Useful Analytical Method for Outcome Difference, Inequality and Disparity Studies -- Cusp Catastrophe Modeling in Medical and Health Research -- On Ranked Set Sampling Variation and its Applications to Public Health Research -- Weighted Multiple Testing Correction for Correlated Endpoints in Survival Data -- Meta-analytic Methods for Public Health Research
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9783319185354
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
SpringerLink
Other control number
10.1007/978-3-319-18536-1
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • (OCoLC)919611846
  • (OCoLC)ocn919611846

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