ABSTRACT. The usefulness of SAS macros is without question. But when macros are shared with others, their value increases immeasurably. Consider the time. variables and macros. In SAS code: – &name refers to a macro variable. – % name refers to a macro. • Macro code consists of these two elements and. The macro facility is an important feature of the SAS generating macros that can save time and effort. substitution to provide simple yet powerful SAS code.
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Generating SAS Code Using Macros. 5. More Advanced Macro Techniques. 8. Other Features of the Macro Language. Chapter 2. SAS Programs and Macro . Oct 31, But, while macros certainly can be challenging, it is also the macro processor which then “resolves” your macros generating standard SAS. Nine Steps to Get Started using SAS® Macros. Jane Stroupe, SAS Institute, Chicago, IL. ABSTRACT. Have you ever heard your coworkers rave about macros?.
C-x u Undo Copy region: From now on when you open this file, XEmacs will know what to do; the first time, however, you will need to manually tell XEmacs what to do. The current version of the macros is 2. C-s Return stops search Search backward: Once T-bird starts, from the Edit menu select Account Settings. C-g Exit Emacs: C-c c M-x comment-region Uncomment a region:
The covariance matrix output from the Proc IML statement is, therefore, the correct covariance matrix. The vector of partial derivatives is computed in the program: Section 3 An example of use of the program on a dataset is given here and also in Appendix C. The entire program is given in Appendix C. The dataset used for these examples contains information on individuals who participated in the Framingham Heart Study who ranged in age from 30 to 74 years who were initially free of CVD cardiovascular disease.
There were males and females. The example female from Anderson et al. The outcome we consider here is called Hard CHD and includes coronary death or myocardial infarction.
This is a stricter outcome than that used by Anderson et al. In one of their examples, Anderson et al. Using the same female example that Anderson et al. It took only 11 iterations for the Newton—Raphson to converge, using a convergence criteria of 0.
These calculations are performed in the macro and the results are shown in Eq. The macro for the Weibull model see Appendix E is similar to the macro for the non-proportional model.
Results from models The results for the example from each model are presented Table 3. It is the decision of the investigator as to which type of model to use depending upon issues of proportionality and choice of a parametric or semi-parametric model. Whichever model is selected, it is interesting to see that the Cox and Weibull models appear to agree while the non-proportional Weibull model generally gives a higher prediction.
Discussion This article seeks to review the methods and provide the tools needed to utilize the model presented by Anderson et al. The Weibull model is similar to the non-proportional Weibull model. This paper should provide useful analytic tools for investigators interested in devel- oping prediction equations for di erent cardiovascular disease endpoints.
These tools are not available for all the models shown and provide readily accessible methods for use in prediction equations. Further research in cardiovascular prediction equations is still warranted.
We are grateful to Dr. Govindarajulu, Dr. Glickman, and Dr. Horton for their careful review. Appendix A. Derivation of log-likelihood Eq. Now if the following substitutions are allowed in the functions, g1 ; g2 , and g3: Usha Govindarajulu et al. For the purposes of this macro, the last term in the above log-likelihood was excluded since we did not have time at beginning of follow-up and only had time at failure. Appendix B.
Derivation of vector of partial derivatives Computation of values of Du: Appendix C. This macro is designed for right censoring i. Variables must be in same order as had in analysis i. Variables must be same order as had in analysis i. If want length of prediction to be 10 years, specify a lower and upper bound to have the length be as close to 10 as possible i. A Practical Guide. SAS Institute, Inc.
For an episodically consumed food i. If a replicate variable is in use the percentage shown will be weighted. The counts are not weighted. In the amount model, there must be at least two subjects with at least two positive recalls.
In the uncorrelated and correlated models there must be at least one subject with two positive recalls, or else MIXTRAN will be stopped. Otherwise if there are 10 or fewer subjects with two positive recalls in any of the models, a warning is printed in the log, stating that results might be unstable. For amount models a consumption value of 0 is changed to half the minimum amount consumed.
The parameter name is unchanged, but any two part recall variable can be used.
The values of the "type" variable in the parameter names have changed. The value 1. The following minor issues have been corrected: For most cases, the new method will produce estimates very similar to those produced using the older Taylor linearization method, but for cases where the Box-Cox parameter is small.
Code for the Monte Carlo simulations has been streamlined. If the "cutpoints" parameter is used, there is no longer a requirement that at least two cut points be specified. It now contains two sub-macros, one of which calculates the estimated intake values, and the other the percentiles and other descriptive statistics. This allows percentiles to be calculated for subgroups, while using the same basis of estimated intake. The options are: The ability to test estimated intake against a recommend amount of intake has been added.
A flag variable is set to 1 if the comparison is true. The proportion of the population or subgroups meeting the requirement will be saved in the descript data set output by the DISTRIB macro.
The new parameters introduced for the recommended amount comparison are: The values permitted are: LT - less than LE - less than or equal to GE - greater than or equal to GT - greater than R - a range, inclusive of the minimum and maximum values Recamt - The name of the variable containing the value for comparison, or the lower end of a range.
This is to accommodate the fact that data containing subgroup information can now include additional information for the recommended amount.
The parameter subgroup has been added. It is used to obtain percentiles and other descriptive information for the levels of the subgroup variable.