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Author:   Bertram K.C. Chan, PhD ,  B K C Chan
Publisher:   Springer Publishing Co Inc
Imprint:   Springer Publishing Co Inc
Dimensions:   Width: 17.80cm , Height: 2.30cm , Length: 25.40cm
Weight:   0.808kg
ISBN:  

9780826110251

ISBN 10:   0826110258
Pages:   458
Publication Date:   05 November 2015
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

Table of Contents

Contents Preface 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 The 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 The 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 A First Session Using R The 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 The 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 The lattice Package: Trellis Graphics The 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 5.1 Theories 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 The 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 An 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 The Logistic Regression Model The Logit Logistic Regression Analysis Generalized Linear Models in R Review Questions for Section 7.4 Exercises for Section 7.4 References Index

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Author Information

Bertram K. C. (Bert) Chan, PhD, is currently Consulting Biostatistician at the School of Medicine, Department of Preventive Medicine at Loma Linda University.

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