Download Applied medical statistics using SAS by Geoff Der PDF

By Geoff Der

ISBN-10: 1439867984

ISBN-13: 9781439867983

''Adding issues valuable to clinical statisticians, this new version of a well-liked intermediate-level reference explores using SAS for studying scientific information. a brand new bankruptcy on visualizing facts encompasses a exact account of pictures for investigating info and smoothing recommendations. The ebook additionally contains new chapters on size in medication, epidemiology/observational stories, meta-analysis, Bayesian tools, and Read more...

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Extra resources for Applied medical statistics using SAS

Example text

Often the solution is to use the pad option, which adds spaces to the end of each data line as it is read. There is one situation where an infile statement is not needed: when the data are contained within the SAS program itself. This is referred to as ‘instream’ data. If data are instream, an infile statement is only needed when additional options are required. 2), so the expandtabs option is not needed. In practice, raw data are more commonly contained in an external file. 4 Input Statement The input statement in the example specifies that four variables are to be read in from the raw data file: idno, team, startweight, and weightnow.

This continues until all the data have been read in and processed. The data step will then finish and the execution of the program will pass on to the next step. In effect, then, the data step consists of a loop of instructions executed repeatedly until all the data are processed. The automatic SAS variable ( _n_ ) records the iteration number but is not stored in the data set. Its use will be illustrated in later chapters. The point at which SAS adds an observation to the data set can be controlled by the use of the output statement.

After running a program: r Examine the SAS log for warning and error messages. r Check for the message ‘SAS went to a new line when INPUT statement reached past the end of a line’ when using list input. r Verify that the number of observations and variables read in is correct. r When reading raw data, check the number of lines read and the maximum and minimum line lengths reported. r Print out small data sets to ensure that they have been read correctly. If there is an error message for a statement that appears to be correct, check whether the semicolon was omitted from the previous statement.

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