Designed for medical statisticians, this intermediate-level reference explores the use of SAS for analyzing medical data. A chapter on visualizing data includes a detailed account of graphics for investigating data and smoothing techniques. The book also includes chapters on measurement in medicine, epidemiology/observational studies, meta-analysis, Bayesian methods, and handling missing data. The book maintains an example-based approach, with SAS code and output included throughout and available online.
Designed for medical statisticians, this intermediate-level reference explores the use of SAS for analyzing medical data. A chapter on visualizing data includes a detailed account of graphics for investigating data and smoothing techniques. The book also includes chapters on measurement in medicine, epidemiology/observational studies, meta-analysis, Bayesian methods, and handling missing data. The book maintains an example-based approach, with SAS code and output included throughout and available online.
An Introduction to SAS. Statistics and Measurement in Medicine. Clinical Trials. Epidemiology. Meta-analysis. Analysis of Variance and Covariance. Scatter Plots, Correlation, Simple Regression, and Smoothing. Multiple Linear Regression. Logistic Regression. The Generalised Linear Model. Generalised Additive Models. The Analysis of Longitudinal Data I. The Analysis of Longitudinal Data II: Linear Mixed-Effects Models for Normal Response Variables. The Analysis of Longitudinal Data III: Non-Normal Responses. Survival Analysis. Cox's Proportional Hazards Models for Survival Data. Bayesian Methods. Missing Values.
An Introduction to SAS. Statistics and Measurement in Medicine. Clinical Trials. Epidemiology. Meta-analysis. Analysis of Variance and Covariance. Scatter Plots, Correlation, Simple Regression, and Smoothing. Multiple Linear Regression. Logistic Regression. The Generalised Linear Model. Generalised Additive Models. The Analysis of Longitudinal Data I. The Analysis of Longitudinal Data II: Linear Mixed-Effects Models for Normal Response Variables. The Analysis of Longitudinal Data III: Non-Normal Responses. Survival Analysis. Cox's Proportional Hazards Models for Survival Data. Bayesian Methods. Missing Values.
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