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• Title:Biostatistics for epidemiology and public health using R / Bertram K.C. Chan.
  
•    
• Author/Creator:Chan, B. K. C. (Bertram Kim-Cheong), author.
  
• Published/Created:New York, NY : Springer Publishing Company, LLC, [2016]
  

• Holdings

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  • Location:BMB LIBRARY (VGH) stacksWhere is this?
    
  • Call Number: WA950 .C38 2016
    
  • Number of Items:1
    
  • Status:Available
    

  • Location:WOODWARD LIBRARY stacksWhere is this?
    
  • Call Number: WA950 .C38 2016
    
  • Number of Items:1
    
  • Status:c.1 On loan - Due on 09-15-2024
    

   
• Library of Congress Subjects:Epidemiology--Statistical methods.
  Biometry.
  
• Medical Subjects: Biostatistics--methods.
  Epidemiology.
  Programming Languages.
  Public Health.
  
• Description:xii, 446 pages ; 26 cm
  
• Notes:Includes bibliographical references and index.
  
• ISBN:9780826110251 paperback
  0826110258 paperback
  9780826110268 e-book
  
• Contents:Machine generated contents note: 1. INTRODUCTION
  1.1. Medicine, Preventive Medicine, Public Health, and Epidemiology
  Medicine
  Preventive Medicine and Public Health
  Public Health and Epidemiology
  Review Questions for Section 1.1
  1.2. Personal Health and Public Health
  Personal Health Versus Public Health
  Review Questions for Section 1.2
  1.3. Research and Measurements in EPDM and PH
  EPDM: The Basic Science of PH
  Main Epidemiologic Functions
  Cause of Diseases
  Exposure Measurement in Epidemiology
  Additional Issues
  Review Questions for Section 1.3
  1.4. BIOS and EPDM
  Review Questions for Section 1.4
  References
  2. RESEARCH AND DESIGN IN EPIDEMIOLOGY AND PUBLIC HEALTH
  Introduction
  2.1. Causation and Association in Epidemiology and Public Health
  Bradford-Hill Criteria for Causation and Association in Epidemiology
  Legal Interpretation Using Epidemiology
  Disease Occurrence
  Review Questions for Section 2.1
  2.2. Causation and Inference in Epidemiology and Public Health
  Rothman's Diagrams for Sufficient Causation of Diseases
  Causal Inferences
  Using the Causal Criteria
  Judging Scientific Evidence
  Review Questions for Section 2.2
  2.3. Biostatistical Basis of Inference
  Modes of Inference
  Levels of Measurement
  Frequentist BIOS in EPDM
  Confidence Intervals in Epidemiology and Public Health
  Bayesian Credible Interval
  Review Questions for Section 2.3
  2.4. BIOS in EPDM and PH
  Applications of BIOS
  BIOS in EPDM and PH
  Processing and Analyzing Basic Epidemiologic Data
  Analyzing Epidemiologic Data
  Using R
  Evaluating a Single Measure of Occurrence
  Poisson Count (Incidence) and Rate Data
  Binomial Risk and Prevalence Data
  Evaluating Two Measures of Occurrence
  -Comparison of Risk: Risk Ratio and Attributable Risk
  Comparing Two Rate Estimates: Rate Ratio rr
  Comparing Two Risk Estimates: Risk Ratio RR and Disease (Morbidity) Odds Ratio DOR
  Comparing Two Odds Estimates From Case
  Control: The Salk Polio Vaccine Epidemiologic Study
  Review Questions for Section 2.4
  Exercises for Chapter 2
  Using Probability Theory
  Disease Symptoms in Clinical Drug Trials
  Risks and Odds in Epidemiology
  Case
  Control Epidemiologic Study
  Mortality, Morbidity, and Fertility Rates
  Incidence Rates in Case-Cohort Survival Analysis
  Prevalence
  Mortality Rates
  Estimating Sample Sizes
  References
  Appendix
  3. DATA ANALYSIS USING R PROGRAMMING
  Introduction
  3.1. Data and Data Processing
  Data Coding
  Data Capture
  Data Editing
  Imputations
  Data Quality
  Producing Results
  Review Questions for Section 3.1
  3.2. Beginning R
  R and Biostatistics
  First Session Using R
  R Environment
  Review Questions for Section 3.2
  3.3. R as a Calculator
  Mathematical Operations Using R
  Assignment of Values in R and Computations Using Vectors and Matrices
  Computations in Vectors and Simple Graphics
  Use of Factors in R Programming
  Simple Graphics
  X as Vectors and Matrices in Biostatistics
  Some Special Functions That Create Vectors
  Arrays and Matrices
  Use of the Dimension Function dim in R
  Use of the Matrix Function matrix in R
  Some Useful Functions Operating on Matrices in R
  NA: "Not Available" for Missing Values in Datasets
  Special Functions That Create Vectors
  Review Questions for Section 3.3
  Exercises for Section 3.3
  3.4. Using R in Data Analysis in BIOS
  Entering Data at the R Command Prompt
  Function list() and the Making of data.frame() in R
  Review Questions for Section 3.4
  Exercises for Section 3.4
  3.5. Univariate, Bivariate, and Multivariate Data Analysis
  Univariate Data Analysis
  Bivariate and Multivariate Data Analysis
  Multivariate Data Analysis
  Analysis of Variance (ANOVA)
  Review Questions for Section 3.5
  Exercises for Section 3.5
  References
  Appendix: Documentation for the plot function
  Generic X
  Y Plotting
  4. GRAPHICS USING R
  Introduction
  Choice of System
  Packages
  4.1. Base (or Traditional) Graphics
  High-Level Functions
  Low-Level Plotting Functions
  Interacting with Graphics
  Using Graphics Parameters
  Parameters List for Graphics
  Device Drivers
  Review Questions for Section 4.1
  Exercises for Section 4.1
  4.2. Grid Graphics
  lattice Package: Trellis Graphics
  Grid Model for R Graphics
  Grid Graphics Objects
  Applications to Biostatistical and Epidemiologic Investigations
  Review Questions for Section 4.2
  Exercises for Section 4.2
  References
  5. PROBABILITY AND STATISTICS IN BIOSTATISTICS
  Introduction
  5.1Theories of Probability
  What Is Probability?
  Basic Properties of Probability
  Probability Computations Using R
  Applications of Probability Theory to Health Sciences
  Typical Summary Statistics in Biostatistics: Confidence Intervals, Significance Tests, and Goodness of Fit
  Review Questions for Section 5.1
  Exercises for Section 5.1
  5.2. Typical Statistical Inference in Biostatistics: Bayesian Biostatistics
  What Is Bayesian Biostatistics?
  Bayes's Theorem in Probability Theory
  Bayesian Methodology and Survival Analysis (Time-to-Event) Models for Biostatistics in Epidemiology and Preventive Medicine
  Inverse Bayes Formula
  Modeling in Biostatistics
  Review Questions for Section 5.2
  Exercises for Section 5.2
  References
  6. CASE
  CONTROL STUDIES AND COHORT STUDIES IN EPIDEMIOLOGY
  Introduction
  6.1. Theory and Analysis of Case
  Control Studies
  Advantages and Limitations of Case
  Control Studies
  Analysis of Case
  Control Studies
  Review Questions for Section 6.1
  Exercises for Section 6.1
  6.2. Theory and Analysis of Cohort Studies
  Important Application of Cohort Studies
  Clinical Trials
  Randomized Controlled Trials
  Cohort Studies for Diseases of Choice and Noncommunicable Diseases
  Cohort Studies and the Lexis Diagram in the Biostatistics of Demography
  Review Questions for Section 6.2
  Exercises for Section 6.2
  References
  7. RANDOMIZED TRIALS, PHASE DEVELOPMENT, CONFOUNDING IN SURVIVAL ANALYSIS, AND LOGISTIC REGRESSIONS
  7.1. Randomized Trials
  Classifications of RTs by Study Design
  Randomization
  Biostatistical Analysis of Data from RTs
  Biostatistics for RTs in the R Environment
  Review Questions for Section 7.1
  Exercises for Section 7.1
  7.2. Phase Development
  Phase 0 or Preclinical Phase
  Phase I
  Phase II
  Phase III
  Pharmacoepidemiology: A Branch of Epidemiology
  Some Basic Tests in Epidemiologic Phase Development
  Review Questions for Section 7.2
  Exercises for Section 7.2
  7.3. Confounding in Survival Analysis
  Biostatistical Approaches for Controlling Confounding
  Using Regression Modeling for Controlling Confounding
  Confounding and Collinearity
  Review Questions for Section 7.3
  Exercises for Section 7.3
  7.4. Logistic Regressions
  Inappropriateness of the Simple Linear Regression When y Is a Categorical Dependent Variable
  Logistic Regression Model
  Logit
  Logistic Regression Analysis
  Generalized Linear Models in R
  Review Questions for Section 7.4
  Exercises for Section 7.4.