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/amsus16.2.1 Examples of Coxa#39;s Regression We begin by returning to the data on HPA staining and breast cancer described in Chapter 15. ... model weeks*censor(1)= staining /rl; run; As with proc lifetest in the previous chapter, the model statement has the survival time variable followed by an asterisk and then the censoring variable with the value, or values, indicating censored observations in parentheses.
|Title||:||Applied Medical Statistics Using SAS|
|Author||:||Geoff Der, Brian S. Everitt|
|Publisher||:||CRC Press - 2012-10-01|