Applied Medical. Statistics Using SAS. Geoff Der. Brian S. Everitt. CRC Press. Taylor Si Francis Croup. Taylor & Francis Croup, an informa business

Transcription

1 Applied Medical Statistics Using SAS Geoff Der Brian S. Everitt CRC Press Taylor Si Francis Croup Boca Raton London New York CRC Press is an imprint of the Taylor & Francis Croup, an informa business A CHAPMAN 6t HALL BOOK

2 Preface The Authors xiii xv 1. An Introduction to SAS Introduction User Interface Editor Window Log Window Output Window Results Window Explorer Window Results Viewer Window Options for Displaying Procedure Results Help and Documentation SAS Programs Program Steps Variable Names and Data Set Names Variable Lists Reading Data The Data Step Creating SAS Data Sets from Raw Data Data Statement Infile Statement Input Statement List Input Column Input Formatted Input Multiple Lines per Observation Multiple Observations per Line Delimited Data Reading Data Proc Import 18 Excel Files Reading and Writing Temporary and Permanent SAS Data Sets SAS Libraries Reading Data from an Existing SAS Data Set 20 SAS Data Modifying Creating and Modifying Variables Missing Values in Arithmetic Expressions Deleting Deleting Observations Subsetting Data Sets 24 Variables 24 v

3 vi Data Sets Concatenating and Merging Merging Data Sets Adding Variables Operation of the Data Step ProcStep Proc Statement Var Statement Where Statement By Statement Class Statement Global Statements Options 1.8 SAS 30 Graphics xy Plots proc sgplot Summary Plots Panel Plots ODS Output Delivery System ODS Procedure Output ODS 33 Styles 1.10 Saving Output in SAS Data Sets ods output ODS Graphics 1.11 Enhancing Output Variable Labels Value Labels SAS Formats SAS Macros Some Tips for Preventing and Correcting Errors Statistics and Measurement in Medicine Introduction A Brief History of Medical Statistics Measurement in Medicine Scales of Measurement Nominal or Categorical Measurements Ordinal Scale Measurements Interval Scales Ratio Scales Assessing Bias and Reliability of Measurements Assessing Reliability and Bias for Binary and Other Categorical Observations Assessing the Reliability of Quantitative Measurements Diagnostic Tests Summary Clinical Trials 3.1 Introduction 73 73

4 vii 3.2 Clinical Trials Types of Randomisation Blocked Randomisation Stratified Randomisation Minimisation Method How Many Participants Do I Need in My Trial? of Analysis Data from Clinical Trials p-values and Confidence Intervals Some Examples of Analysis of Data from Clinical Trials Using Familiar Statistical Methods Summary Epidemiology Introduction Types of Epidemiological Study Surveys Case-Control Studies Ill Cohort Studies Relative Risk and Odds Ratios Sample Size Estimation for Epidemiologic Studies Sample Size Estimation for Case-Control Studies Sample Size Estimation for Cohort Studies Simple Analyses for Data from Observational Studies Chi-Squared Test for Association Finding a Confidence Interval for the Relative Risk and the Odds Ratio Applying SAS to Analyse Examples of Epidemiological Data Fisher's Test Matched Case-Control Data Stratified 2x2 Tables Summary Meta-Analysis Introduction Study Selection Publication Bias Statistics of Meta-Analysis Fixed-Effects Model Random-Effects Model An Example of the Application of Meta-Analysis Meta-Analysis on Sparse Data Meta-Regression Summary 155

5 viii 6. Analysis of Variance and Covariance Introduction A Simple Example of One-Way Analysis of Variance One-Way Analysis of Variance Model Applying the One-Way Analysis of Variance Model to Sickle Cell Disease Data Multiple Comparison Procedures Planned Comparisons Post Hoc Comparisons A Factorial Experiment Model for Three-Factor Design Unbalanced Designs Type I Sums of Squares Type II Sums of Squares Type III Sums of Squares Analysis of Antipyrine Data Nonparametric Analysis of Variance Kruskal-Wallis Distribution-Free Test for One-Way Analysis of Variance Applying the Kruskal-Wallis Test Analysis of Covariance Summary Scatter Plots, Correlation, Simple Regression, and Smoothing Introduction Scatter Plot and Correlation Coefficient Simple Linear Regression and Locally Weighted Regression Locally Weighted Regression Aspect Ratio of a Scatter Plot Estimating Bivariate Densities Scatter Plot Matrices Summary Multiple Linear Regression Introduction Multiple Linear Regression Model Some Examples of the Application of the Multiple Linear Regression Model Effect of the Amount of Anaesthetic Agent Administered during an Operation Mortality and Water Hardness Weight and Physical Measurements in Men Identifying a Parsimonious Model All Possible Subsets Regression Stepwise Methods 236

6 ix 8.5 Checking Model Assumptions: Residuals and Other Regression Diagnostics General Linear Model Summary Logistic Regression Introduction Logistic Regression Two Examples of the Application of Logistic Regression Psychiatric'Caseness' Birth Weight of Babies Diagnosing a Logistic Regression Model Logistic Regression for 1:1 Matched Studies Propensity Scores Summary Generalised Linear Model Introduction Generalised Linear Models Applying the Generalised Linear Model Poisson Regression Regression with Gamma Errors Residuals for GLMs Overdispersion Summary Generalised Additive Models Introduction Scatter Plot Smoothers Additive and Generalised Additive Models Examples of the Application of GAMs Summary Analysis of Longitudinal Data Introduction Graphical Displays of Longitudinal Data Summary Measure Analysis of Longitudinal Choosing Summary Data 333 Measures Applying the Summary Measure Approach Incorporating Pretreatment Outcome Values into the Summary Measure Approach Dealing with Missing Values When Using the Summary Measure Approach Summary Measure Approach for Binary Responses Summary 347

7 X 13. Analysis of Longitudinal Data II: Linear Mixed-Effects Models for Normal Response Variables Introduction 349 Measures Data Linear Mixed-Effects Models for Repeated Random Intercept and Random Intercept and Slope Models Applying the Random Intercept and Random Intercept and Slope Models Dropouts in Longitudinal Data Summary 14. Analysis of Longitudinal Data III: Non-Normal Responses Introduction Marginal Models and Conditional Models Marginal Models Conditional Models Analysis of the Respiratory Data Marginal Models Generalised Linear Mixed-Effects Models Analysis of Epilepsy Data Marginal Models Generalised Linear Mixed-Effects Models Summary 15. Survival 399 Analysis 15.1 Introduction Survivor Function and the Hazard Function Survivor Function Hazard Function Comparing Groups of Survival Times Log-Rank Test Stratified Tests Sample Size Estimation Summary Cox's Proportional Hazards Models for Survival Data Introduction Modelling the Hazard Function: Cox's Regression Examples of Cox's Regression Estimating the Baseline Hazard Function Checking Assumptions in Cox's Regression Stratified Cox's Regression Time-Varying Covariates Random-Effects Models for Survival Data Summary

8 *i 17. Bayesian Methods Introduction Bayesian Estimation Markov Chain Monte Carlo Prior Distributions Model Selection When Using a Bayesian Approach Some Examples of the Application of Bayesian Statistics Psychiatric 'Caseness' 465 in Babies Cardiac Surgery 17.7 Summary Missing Values Introduction Patterns of Missing Data Missing Data Mechanisms Exploring Missingness Dealing with Missing Values Imputing Missing Values Analysing Multiply Imputed Data Some Examples of the Application of Multiple Imputation Air Pollution in US Cities Growth of Danish Boys Summary 507 References 509 Index 519