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Geoff Der | Brian S. Everitt

Applied Medical Statistics Using SAS

★★★★★
★★★★★
€179.80
A01=Brian S. Everitt
A01=Geoff Der
advanced statistical methods for medical research
Air Pollution Data
Author_Brian S. Everitt
Author_Geoff Der
Bayesian inference methods
biostatistics
Category=UFM
clinical trial design
code
coefficients
confidence
Cov Parm Subject Estimate Standard
data
Data Set
DF Parameter Estimate Standard Error
epidemiological analysis
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Estimated Survivor Function
graphic
Hazard Function
interval
longitudinal data modeling
Method Num DF Den DF
missing data techniques
Multiple Linear Regression
ods
Ods Graphics
Parameter DF Estimate Standard
proc
Proc Freq Data
Proc Genmod Data
Proc Logistic Data
Proc Reg Data
Proc Sgplot Data
Proc Ttest Data
regression
Regression Model
Root MSE
SAS Code
SAS Data Set
Scatter Plot
sgplot
Source DF Sum
Source DF Type
Std Dev
Test Chi Square DF Pr

Product details

  • ISBN 9781439867976
  • Weight: 946g
  • Dimensions: 156 x 234mm
  • Publication Date: 01 Oct 2012
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

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