Analysis of Correlated Data with SAS and R 4th Edition by Mohamed Shoukri – Ebook PDF Instant Download/Delivery: 9781315277714, 1315277719
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ISBN 10: 1315277719
ISBN 13: 9781315277714
Author: Mohamed M. Shoukri
Analysis of Correlated Data with SAS and R
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.
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
- Nonexperimental or Observational Studies
- Types of Nonexperimental Designs (Descriptive/Exploratory, Correlational, Cross-Sectional, Longitudinal, Case-Control, etc.)
- Quasi-Experimental Designs
- Single-Subject Design (SSD)
- Confounding and Sampling Strategies
- Quality of Designs
1.2 Effect Size
- What is Effect Size?
- Why Report Effect Sizes?
- Measures of Effect Size
- ASA Statement on p-value
Chapter 2: Comparing Group Means When the Standard Assumptions Are Violated
2.1 Introduction
2.2 Nonnormality
2.3 Heterogeneity of Variances
- Bartlett’s Test
- Levene’s Test
2.4 Testing Equality of Group Means - Welch’s Statistic
- Brown and Forsythe Statistic
2.5 Nonindependence
2.6 Nonparametric Tests - Nonparametric Analysis Using SAS
Chapter 3: Analyzing Clustered Data
3.1 Introduction
3.2 Cluster Data Features
3.3 Regression Analysis for Clustered Data
- Generalized Linear Models (GLMs)
- Generalized Estimating Equation (GEE)
3.4 Alternative Models for Clustered Data - Proc Mixed for Clustered Data
- Model Examples with Covariates
Chapter 4: Statistical Analysis of Cross-Classified Data
4.1 Introduction
4.2 Measures of Association in 2×2 Tables
- Risk Difference, Odds Ratio, Relative Risk, etc.
4.3 Statistical Inference
4.4 Analysis of Matched Pairs
4.5 Statistical Analysis of Clustered Binary Data - Adjustments to Pearson’s Chi-Square
- Confidence Interval Construction
Chapter 5: Modeling Binary Outcome Data
5.1 Introduction
5.2 Logistic Regression
- Coding Categorical Variables
- Goodness of Fit and Model Comparisons
5.3 Modeling Correlated Binary Data - Generalized Estimating Equations (GEE)
5.4 Sample Size Calculations for Logistic Regression
Chapter 6: Analysis of Clustered Count Data
6.1 Introduction
6.2 Poisson Regression
- Overdispersion in Count Data
6.3 Generalized Linear Mixed Models (GLMM)
Chapter 7: Repeated Measures and Longitudinal Data Analysis
7.1 Introduction
7.2 Analysis of Repeated Measures Data
7.3 Mixed Linear Regression Models
- Covariance Patterns, Statistical Inference
7.4 Missing Observations in Repeated Measures
Chapter 8: Introduction to Time Series Analysis
8.1 Introduction
8.2 Models for Stationary Time Series
- Autoregressive Processes, Moving Average Processes
8.3 ARIMA Models
8.4 Interrupted Time Series
8.5 Forecasting with Exponential Smoothing Models
Chapter 9: Analysis of Survival Data
9.1 Introduction
9.2 Estimating Survival Probabilities
9.3 Nonparametric Methods
- Log-Rank Test, Exponential and Weibull Models
9.4 Cox Proportional Hazards Model
9.5 Competing Risk Models
9.6 Sample Size Requirements for Survival Data
Chapter 10: Introduction to Propensity Score Analysis
10.1 Introduction
10.2 Confounding and Control Methods
10.3 Propensity Score Estimation and Methods
10.4 Controversies and Criticisms of Propensity Scores
Chapter 11: Introductory Meta-Analysis
11.1 Introduction to Meta-Analysis
11.2 Performing the Analysis
11.3 Issues in Meta-Analysis
- Publication Bias, Heterogeneity
11.4 Statistical Methods - Fixed Effect, Random Effect Models
11.5 Meta-Analysis of Diagnostic Accuracy
Chapter 12: Missing Data
12.1 Introduction
12.2 Patterns and Mechanisms of Missing Data
12.3 Handling Missing Data
- Listwise Deletion, Regression, Multiple Imputation
12.4 Missing Data in SAS and R
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