Description
Applied Medical Statistics Using SAS (2nd Ed.)
Language: English
Subjects for Applied Medical Statistics Using SAS:
Keywords
SAS Code; Data Set; ods; Proc Sgplot Data; graphic; Multiple Linear Regression; code; Source DF Sum; proc; Ods Graphics; sgplot; Scatter Plot; data; Proc Reg Data; confidence; Proc Freq Data; interval; SAS Data Set; regression; Method Num DF Den DF; coefficients; Test Chi Square DF Pr; Regression Model; Root MSE; Proc Genmod Data; Parameter DF Estimate Standard; DF Parameter Estimate Standard Error; Estimated Survivor Function; Proc Ttest Data; Hazard Function; Source DF Type; Cov Parm Subject Estimate Standard; Proc Logistic Data; Air Pollution Data; Std Dev
480 p. · 15.6x23.4 cm · Hardback
Description
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Written with medical statisticians and medical researchers in mind, this intermediate-level reference explores the use of SAS for analyzing medical data. Applied Medical Statistics Using SAS covers the whole range of modern statistical methods used in the analysis of medical data, including regression, analysis of variance and covariance, longitudinal and survival data analysis, missing data, generalized additive models (GAMs), and Bayesian methods. The book focuses on performing these analyses using SAS, the software package of choice for those analysing medical data.
Features
- Covers the planning stage of medical studies in detail; several chapters contain details of sample size estimation
- Illustrates methods of randomisation that might be employed for clinical trials
- Covers topics that have become of great importance in the 21st century, including Bayesian methods and multiple imputation
Its breadth and depth, coupled with the inclusion of all the SAS code, make this book ideal for practitioners as well as for a graduate class in biostatistics or public health.
Complete data sets, all the SAS code, and complete outputs can be found on an associated website: http://support.sas.com/amsus
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.
