(Ebook) Analysis of Correlated Data with SAS and R 4th Edition by Mohamed M Shoukri ISBN 1138197459 9781138197459

(Ebook) Analysis of Correlated Data with SAS and R 4th Edition by Mohamed M Shoukri ISBN 1138197459 9781138197459

(Ebook) Analysis of Correlated Data with SAS and R 4th Edition by Mohamed M Shoukri - Ebook PDF Instant Download/Delivery: 1138197459, 9781138197459
Full download (Ebook) Analysis of Correlated Data with SAS and R 4th Edition after payment

Product details:

ISBN 10: 1138197459 
ISBN 13: 9781138197459
Author: Mohamed M Shoukri

Analysis of Correlated Data with SAS and R: 4th edition presents an applied treatment of recently developed statistical models and methods for the analysis of hierarchical binary, count and continuous response data. It explains how to use procedures in SAS and packages in R for exploring data, fitting appropriate models, presenting programming codes and results. The book is designed for senior undergraduate and graduate students in the health sciences, epidemiology, statistics, and biostatistics as well as clinical researchers, and consulting statisticians who can apply the methods with their own data analyses. In each chapter a brief description of the foundations of statistical theory needed to understand the methods is given, thereafter the author illustrates the applicability of the techniques by providing sufficient number of examples. The last three chapters of the 4th edition contain introductory material on propensity score analysis, meta-analysis and the treatment of missing data using SAS and R. These topics were not covered in previous editions. The main reason is that there is an increasing demand by clinical researchers to have these topics covered at a reasonably understandable level of complexity. Mohamed Shoukri is principal scientist and professor of biostatistics at The National Biotechnology Center, King Faisal Specialist Hospital and Research Center and Al-Faisal University, Saudi Arabia. Professor Shoukri’s research includes analytic epidemiology, analysis of hierarchical data, and clinical biostatistics. He is an associate editor of the 3Biotech journal, a Fellow of the Royal Statistical Society and an elected member of the International Statistical Institute.

(Ebook) Analysis of Correlated Data with SAS and R 4th Table of contents:

Chapter 1 Study Designs and Measures of Effect Size
1.1 Study Designs
1.1.1 Introduction
1.1.2 Nonexperimental or Observational Studies
1.1.3 Types of Nonexperimental Designs
1.1.3.1 Descriptive/Exploratory Survey Studies
1.1.3.2 Correlational Studies (Ecological Studies)
1.1.3.3 Cross-Sectional Studies
1.1.3.4 Longitudinal Studies
1.1.3.5 Prospective or Cohort Studies
1.1.3.6 Case-Control Studies
1.1.3.7 Nested Case-Control Study
1.1.3.8 Case-Crossover Study
1.1.4 Quasi-Experimental Designs
1.1.5 Single-Subject Design (SSD)
1.1.6 Quality of Designs
1.1.7 Confounding
1.1.8 Sampling
1.1.9 Types of Sampling Strategies
1.1.10 Summary
1.2 Effect Size
1.2.1 What Is Effect Size?
1.2.2 Why Report Effect Sizes?
1.2.3 Measures of Effect Size
1.2.4 What Is Meant by “Small,” “Medium,” and “Large”?
1.2.5 Summary
1.2.6 American Statistical Association (ASA) Statement about the p-value
Exercises
Chapter 2 Comparing Group Means When the Standard Assumptions Are Violated
2.1 Introduction
2.2 Nonnormality
2.3 Heterogeneity of Variances
2.3.1 Bartlett’s Test
2.3.2 Levene’s Test (1960)
2.4 Testing Equality of Group Means
2.4.1 Welch’s Statistic (1951)
2.4.2 Brown and Forsythe Statistic (1974b) for Testing Equality of Group Means
2.4.3 Cochran’s (1937) Method of Weighing for Testing Equality of Group Means
2.5 Nonindependence
2.6 Nonparametric Tests
2.6.1 Nonparametric Analysis of Milk Data Using SAS
Chapter 3 Analyzing Clustered Data
3.1 Introduction
3.2 The Basic Feature of Cluster Data
3.3 Effect of One Measured Covariate on Estimation of the Intracluster Correlation
3.4 Sampling and Design Issues
3.4.1 Comparison of Means
3.5 Regression Analysis for Clustered Data
3.6 Generalized Linear Models
3.6.1 Marginal Models (Population Average Models)
3.6.2 Random Effects Models
3.6.3 Generalized Estimating Equation (GEE)
3.7 Fitting Alternative Models for Clustered Data
3.7.1 Proc Mixed for Clustered Data
3.7.2 Model 1: Unconditional Means Model
3.7.3 Model 2: Including a Family Level Covariate
3.7.4 Model 3: Including the Sib-Level Covariate
3.7.5 Model 4: Including One Family Level Covariate and Two Subject Level Covariates
Appendix
Exercises
Chapter 4 Statistical Analysis of Cross-Classified Data
4.1 Introduction
4.2 Measures of Association in 2 × 2 Tables
4.2.1 Absolute Risk
4.2.2 Risk Difference
4.2.3 Attributable Risk
4.2.4 Relative Risk
4.2.5 Odds Ratio
4.2.6 Relationship between Odds Ratio and Relative Risk
4.2.7 Incidence Rate and Incidence Rate Ratio As a Measure of Effect Size
4.2.8 What Is Person-Time?
4.3 Statistical Analysis from the 2 × 2 Classification Data
4.3.1 Cross-Sectional Sampling
4.3.2 Cohort and Case-Control Studies
4.4 Statistical Inference on Odds Ratio
4.4.1 Significance Tests
4.4.2 Interval Estimation
4.5 Analysis of Several 2 × 2 Contingency Tables
4.5.1 Test of Homogeneity
4.5.2 Significance Test of Common Odds Ratio
4.5.3 Confidence Interval on the Common Odds Ratio
4.6 Analysis of Matched Pairs (One Case and One Control)
4.6.1 Estimating the Odds Ratio
4.6.2 Testing the Equality of Marginal Distributions
4.7 Statistical Analysis of Clustered Binary Data
4.7.1 Approaches to Adjust the Pearson’s Chi-Square
4.7.2 Donner and Donald Adjustment
4.7.3 Procedures Based on Ratio Estimate Theory
4.7.4 Confidence Interval Construction
4.7.5 Adjusted Chi-Square for Studies Involving More than Two Groups
4.8 Inference on the Common Odds Ratio
4.8.1 Donald and Donner’s Adjustment
4.8.2 Rao and Scott’s Adjustment
4.9 Calculations of Relative and Attributable Risks from Clustered Binary Data
4.10 Sample Size Requirements for Clustered Binary Data
4.10.1 Paired-Sample Design
4.10.2 Comparative Studies for Cluster Sizes Greater or Equal to Two
4.11 Discussion
Exercises
Chapter 5 Modeling Binary Outcome Data
5.1 Introduction
5.2 The Logistic Regression Model
5.3 Coding Categorical Explanatory Variables and Interpretation of Coefficients
5.4 Interaction and Confounding in Logistic Regression
5.5 The Goodness of Fit and Model Comparisons
5.5.1 The Pearson’s ? 2 Statistic
5.5.2 The Likelihood Ratio Criterion (Deviance)
5.6 Modeling Correlated Binary Outcome Data
5.6.1 Introduction
5.6.2 Population Average Models: The Generalized Estimating Equation (GEE) Approach
5.6.3 Cluster-Specific Models (Random-Effects Models)
5.6.4 Interpretation of Regression Parameters
5.6.5 Multiple Levels of Clustering
5.7 Logistic Regression for Case-Control Studies
5.7.1 Cohort versus Case-Control Models
5.7.2 Matched Analysis
5.7.3 Fitting Matched Case-Control Study Data in SAS and R
5.7.4 Some Cautionary Remarks on the Matched Case-Control Designs
5.8 Sample Size Calculations for Logistic Regression
5.9 Sample Size for Matched Case Control Studies
Exercises
Chapter 6 Analysis of Clustered Count Data
6.1 Introduction
6.2 Poisson Regression
6.2.1 Model Inference and Goodness of Fit
6.2.2 Overdispersion in Count Data
6.2.3 Count Data Random Effects Models
6.2.4 Introducing the Generalized Linear Mixed Model (GLMM)
6.2.5 Fitting GLMM Using SAS GLIMMIX
6.3 Other Models: Poisson Inverse Gaussian and Zero Inflated Poisson with Random Effects
Exercises
Chapter 7 Repeated Measures and Longitudinal Data Analysis
7.1 Introduction
7.2 Examples
7.2.1 Experimental Studies
7.2.1.1 Liver Enzyme Activity
7.2.1.2 Effect of Mycobacterium Inoculation on Weight
7.2.2 Observational Studies
7.2.2.1 Variations in Teenage Pregnancy Rates in Canada
7.2.2.2 Number of Tuberculosis Cases in Saudi Arabia
7.3 Methods for the Analysis of Repeated Measures Data
7.4 Basic Models
7.5 The Issue of Missing Observations
7.6 Mixed Linear Regression Models
7.6.1 Formulation of the Models
7.6.2 Covariance Patterns
7.6.3 Statistical Inference and Model Comparison
7.6.4 Estimation of Model Parameters
7.7 Examples Using the SAS Mixed and GLIMMIX Procedures
7.7.1 Linear Mixed Model for Normally Distributed Repeated Measures Data
7.7.2 Analysis of Longitudinal Binary and Count Data
7.8 Two More Examples for Longitudinal Count Data: Fixed Effect Modeling Strategy
7.9 The Problem of Multiple Comparisons in Repeated Measures Experiments
7.10 Sample Size Requirements in the Analysis of Repeated Measures
Exercises
Chapter 8 Introduction to Time Series Analysis
8.1 Introduction
8.2 Simple Descriptive Methods
8.2.1 Multiplicative Seasonal Variation Model
8.2.2 Additive Seasonal Variation Model
8.2.3 Detection of Seasonality: Nonparametric Test
8.2.4 Autoregressive Errors: Detection and Estimation
8.2.5 Modeling Seasonality and Trend Using Polynomial and Trigonometric Functions
8.3 Fundamental Concepts in the Analysis of Time Series
8.3.1 Stochastic Processes
8.3.2 Stationary Series
8.3.3 Autocovariance and Autocorrelation Functions
8.4 Models for Stationary Time Series
8.4.1 Autoregressive Processes
8.4.2 The AR(1) Model
8.4.3 AR(2) Model (Yule’s Process)
8.4.4 Moving Average Processes
8.4.5 First-Order Moving Average Process MA(1)
8.4.6 Second-Order Moving Average Process MA(2)
8.4.7 Mixed Autoregressive Moving Average Processes
8.4.8 ARIMA Models
8.5 Forecasting
8.5.1 AR(1) Model
8.5.2 AR(2) Model
8.5.3 MA(1) Model
8.6 Forecasting with Exponential Smoothing Models
8.7 Modeling Seasonality with ARIMA: Condemnation Rates Series Revisited
8.8 Interrupted Time Series (Quasi-Experiments)
8.9 Stationary versus Nonstationary Series
Exercises
Chapter 9 Analysis of Survival Data
9.1 Introduction
9.2 Fundamental Concept in Survival Analysis
9.3 Examples
9.3.1 Cystic Ovary Data
9.3.2 Breast Cancer Data
9.3.3 Ventilating Tube Data
9.3.4 Age at Culling of Dairy Cows
9.3.5 Model for End-Stage Liver Disease and Its Effect on Survival of Liver Transplanted Patients
9.4 Estimating Survival Probabilities
9.5 Nonparametric Methods
9.5.1 Methods for Noncensored Data
9.5.2 Methods for Censored Data
9.6 Nonparametric Techniques for Group Comparisons
9.6.1 The Log-Rank Test
9.6.2 Log-Rank Test for More Than Two Groups
9.7 Parametric Methods
9.7.1 Exponential Model
9.7.2 Weibull Model
9.8 Semiparametric Models
9.8.1 Cox Proportional Hazards Model
9.8.2 Estimation of Regression Parameters
9.8.3 Treatment of Ties in the Proportional Hazards Model
9.9 Survival Analysis of Competing Risk
9.9.1 Cause-Specific Hazard
9.9.2 Subdistribution Hazard
9.10 Time-Dependent Variables
9.10.1 Types of Time-Dependent Variables
9.10.2 Model with Time-Dependent Variables
9.11 Joint Modeling of Longitudinal and Time to Event Data
9.12 Submodel Specification
9.12.1 The Survival Submodel
9.12.2 Estimation: JM Package
9.13 Modeling Clustered Survival Data
9.13.1 Marginal Models (GJE Approach)
9.13.2 Random Effects Models (Frailty Models)
9.13.2.1 Weibull Model with Gamma Frailty
9.14 Sample Size Requirements for Survival Data
9.14.1 Sample Size Based on Log-Rank Test
9.14.2 Exponential Survival and Accrual
9.14.3 Sample Size Requirements for Clustered Survival
Exercises
Chapter 10 Introduction to Propensity Score Analysis
10.1 Introduction
10.2 Confounding
10.2.1 Definition of Confounding
10.2.2 Identification of Confounding
10.2.3 Control of Confounding in Study Design
10.2.3.1 Restriction
10.2.3.2 Matching
10.3 Propensity Score Methods
10.3.1 Propensity Scores
10.3.2 Propensity Score Estimation and Covariate Balance
10.4 Methods for Propensity Score Estimation
10.5 Propensity Score Estimation When Units of Analysis Are Clusters
10.6 The Controversy Surrounding Propensity Score
10.7 Examples
10.8 Propensity Score Matching in R
10.9 Propensity Score Stratification in R
Exercises
Chapter 11 Introductory Meta-Analysis
11.1 Introduction
11.2 Definition and Goals of Meta-Analysis
11.3 How Is a Meta-Analysis Done?
11.3.1 Decide on a Research Topic and the Hypothesis to be Tested
11.3.2 Inclusion Criteria
11.3.3 Searching Strategy and Data Extraction
11.3.4 Study Evaluation
11.3.5 Establish Database
11.3.6 Performing the Analysis
11.4 Issues in Meta-Analysis
11.4.1 Design Issues
11.4.2 Positive Studies Are More Likely to be Published (Publication Bias)
11.4.3 Funnel Plot
11.4.4 Studies May Be Heterogeneous
11.4.5 Confounding
11.4.6 Modeling
11.4.7 Evaluating the Results
11.5 Assessing Heterogeneity in Meta-Analysis
11.5.1 Sources of Heterogeneity
11.5.2 Measuring Heterogeneity
11.5.3 Measures of Heterogeneity
11.6 Statistical Methods
11.6.1 Fixed Effect Approach
11.6.2 Binary Data
11.7 Random Effect Model
11.8 Examples
11.9 Meta-Analysis of Diagnostic Accuracy
Exercises
Chapter 12 Missing Data
12.1 Introduction
12.2 Patterns of Missing Data
12.3 Mechanisms of Missing Data
12.3.1 Data Missing Completely at Random (MCAR)
12.3.1.1 Remarks on MCAR
12.3.2 Missing at Random (MAR)
12.3.3 Nonignorable, or Missing Not at Random (MNAR)
12.4 Methods of Handling Missing Data
12.4.1 Listwise or Casewise Data Deletion
12.4.2 Pairwise Data Deletion
12.4.3 Mean Substitution
12.4.4 Regression Methods
12.4.5 Maximum Likelihood Methods
12.4.6 Multiple Imputation (MI)
12.4.7 Expectation Maximization (EM)
12.5 Pattern-Mixture Models for Nonignorable Missing Data
12.6 Strategies to Cope with Incomplete Data
12.7 Missing Data in SAS
12.8 Missing Data in R: MICE
12.9 Examples
References

People also search for (Ebook) Analysis of Correlated Data with SAS and R 4th:

analysis of correlated data with sas and r
    
how to find correlation coefficient in sas
    
correlation between two variables in sas
    
sas analysis of covariance
    
sas z-scores
    
sas cross correlation example
    
correlation of variables in sas

Tags: Mohamed M Shoukri, Analysis, Correlated