Mastering Global and Local Macro Variables in SAS: Essential Techniques and Best Practices for SDTM Programmers

-
Mastering Global and Local Macro Variables in SAS: A Detailed Guide for SDTM Programmers

Mastering Global and Local Macro Variables in SAS: A Detailed Guide for SDTM Programmers

In SAS programming, especially when dealing with complex tasks such as SDTM (Study Data Tabulation Model) dataset creation, macro variables play a critical role in automating processes and managing large amounts of data efficiently. Understanding the distinction between Global and Local macro variables, and how to use them appropriately, is essential for writing clean, maintainable, and robust code.

What are Macro Variables in SAS?

Macro variables in SAS are placeholders that store text strings, which can be reused throughout your SAS programs. They are part of the SAS Macro Language and are used to make your code more dynamic and flexible. By using macro variables, you can avoid hardcoding values and make your code easier to modify and scale.

There are two primary types of macro variables in SAS:

  • Global Macro Variables: These are accessible from anywhere in your SAS session after they are defined. They persist until the end of the session or until explicitly deleted.
  • Local Macro Variables: These are only accessible within the macro where they are created. They are automatically deleted when the macro finishes executing.

Creating and Using Global Macro Variables

Global macro variables are ideal for values that need to be consistent and accessible across multiple programs or within different parts of the same program. In SDTM programming, global macro variables can be used to define study-specific parameters, dataset paths, and other constants that are used throughout your code.

How to Create Global Macro Variables

To create a global macro variable, you can use either the %GLOBAL statement or the %LET statement at the top level of your SAS program or macro. The %GLOBAL statement explicitly declares the macro variable as global, while %LET can implicitly create a global variable if used outside of a macro.

* Using %GLOBAL to declare a global macro variable;
%global study_id;
%let study_id = ABC123;

* Using %LET at the top level (implicitly global);
%let sdtm_path = C:\SASProjects\SDTM\datasets;

* Accessing the global macro variable;
%put The study ID is &study_id;
%put The SDTM path is &sdtm_path;

In the example above, both study_id and sdtm_path are global macro variables that can be accessed anywhere in your SAS session. This is particularly useful when you need to refer to these values in multiple DATA steps, procedures, or macros.

Examples in SDTM Programming

Global macro variables are frequently used in SDTM programming to store key information such as dataset paths, study identifiers, and other parameters that need to be consistent across multiple datasets. Here are some practical examples:

Example 1: Managing Dataset Paths

In a typical SDTM project, you might have several datasets stored in a specific directory. Instead of hardcoding the directory path in every step, you can define a global macro variable to store the path:

%global sdtm_path;
%let sdtm_path = C:\SASProjects\SDTM\datasets;

data dm;
    set "&sdtm_path.\dm.sas7bdat";
run;

data ae;
    set "&sdtm_path.\ae.sas7bdat";
run;

data lb;
    set "&sdtm_path.\lb.sas7bdat";
run;

By using the sdtm_path global macro variable, you can easily change the dataset path in one place, and the change will be reflected wherever the macro variable is used. This approach reduces errors and makes your code more flexible.

Example 2: Study-Specific Information

In SDTM programming, certain information like the study ID, sponsor name, or protocol version may be required across multiple datasets. Defining these as global macro variables ensures consistency and makes your code easier to update.

%global study_id sponsor protocol_version;
%let study_id = ABC123;
%let sponsor = PharmaCorp;
%let protocol_version = 1.0;

data dm;
    set sdtm.dm;
    studyid = "&study_id";
    sponsor = "&sponsor";
    protocol = "&protocol_version";
run;

data ae;
    set sdtm.ae;
    studyid = "&study_id";
    sponsor = "&sponsor";
    protocol = "&protocol_version";
run;

With these global macro variables, you ensure that the study ID, sponsor, and protocol version are consistent across all datasets without the need to manually enter these values in each step.

Creating and Using Local Macro Variables

Local macro variables are designed for use within specific macros, where they are created, used, and then automatically deleted when the macro finishes execution. This makes them ideal for temporary calculations or for ensuring that variable names do not conflict with other variables in your program.

How to Create Local Macro Variables

Local macro variables are created using the %LOCAL statement within a macro. Any macro variables created with %LOCAL are only accessible within that macro and are not visible outside of it.

%macro calculate_age(birth_date, ref_date);
    %local age_years;
    
    age_years = intck('year', &birth_date, &ref_date);
    if month(&ref_date) lt month(&birth_date) or
       (month(&ref_date) = month(&birth_date) and
        day(&ref_date) lt day(&birth_date)) then
        age_years = age_years - 1;
    
    &age_years
%mend calculate_age;

In this example, age_years is a local macro variable that is used to store the calculated age. Once the macro finishes executing, age_years is deleted and is not accessible outside of the macro.

Examples in SDTM Programming

Local macro variables are particularly useful in SDTM programming when you need to perform temporary calculations, create intermediate variables, or ensure that variable names do not conflict with those in other parts of your program.

Example 1: Deriving Variables within a Macro

When deriving variables such as age, visit number, or treatment period within a macro, local macro variables can be used to store intermediate values.

%macro derive_variables(data_in, data_out);
    %local age visit_num trt_period;
    
    data &data_out;
        set &data_in;
        age = %calculate_age(dob, rfstdtc);
        visit_num = input(visit, best.);
        trt_period = trtan;
    run;
%mend derive_variables;

%derive_variables(sdtm.dm, derived.dm);

In this example, the local macro variables age, visit_num, and trt_period are used to store intermediate values during the derivation process. These variables are local to the derive_variables macro and do not interfere with other variables outside the macro.

Example 2: Avoiding Name Conflicts in Large Projects

In large SDTM projects, multiple programmers may use similar variable names across different macros. Using local macro variables helps prevent conflicts and unintended overwriting of values.

%macro create_dm_var();
    %local subj_id;
    subj_id = "0001";  /* Local variable used within this macro */
    call symputx('subj_id', subj_id);
%mend create_dm_var;

%create_dm_var;
%put Subject ID: &subj_id;

In this example, subj_id is a local macro variable within the create_dm_var macro. This ensures that the variable does not conflict with any global variables or other variables in different macros, preserving the integrity of the data.

Advanced Techniques: Using Global and Local Macro Variables Together

In complex SDTM programming tasks, you may find yourself using both global and local macro variables together to achieve more sophisticated data manipulations. For example, you might use a global macro variable to set a general condition or path, and local macro variables within macros to perform specific calculations or transformations.

Example: Creating a Reusable Macro for Multiple Studies

Suppose you need to create a macro that processes SDTM datasets for multiple studies. You can use global macro variables to store study-specific information and local macro variables for intermediate calculations.

%global study_id sdtm_path;
%let study_id = XYZ789;
%let sdtm_path = C:\SASProjects\SDTM\datasets;

%macro process_sdtm(data_set);
    %local age visit_num;
    
    data processed_&data_set;
        set "&sdtm_path.\&data_set..sas7bdat";
        age = %calculate_age(dob, rfstdtc);
        visit_num = input(visit, best.);
    run;
    
    proc print data=processed_&data_set;
    run;
%mend process_sdtm;

%process_sdtm(dm);
%process_sdtm(ae);

In this example, the global macro variables study_id and sdtm_path store information that is consistent across the study, while local macro variables age and visit_num are used for specific calculations within the macro. This approach makes the macro reusable across different datasets while ensuring that variable names do not conflict with others.

Best Practices for Using Macro Variables in SDTM Programming

To get the most out of macro variables in SDTM programming, consider the following best practices:

  • Use Descriptive Names: Choose clear and descriptive names for your macro variables to make your code easier to understand and maintain.
  • Scope Control: Be mindful of the scope of your macro variables. Use local macro variables for temporary calculations and global macro variables for values that need to be consistent across multiple programs.
  • Document Your Code: Include comments in your code to explain the purpose of each macro variable, especially if they are used in multiple places.
  • Reuse and Modularize: Create reusable macros that can be applied across different studies or datasets, using macro variables to customize behavior.
  • Test Thoroughly: Test your macros thoroughly to ensure that global and local variables are behaving as expected, especially in complex programs.

Conclusion

Mastering the use of global and local macro variables is essential for effective SDTM programming in SAS. Global macro variables provide a way to store and access values consistently across your entire SAS session, while local macro variables allow for precise control within specific macros, avoiding conflicts and unintended overwrites. By understanding and applying these concepts, you can create more flexible, maintainable, and robust SAS programs.

Whether you're managing study-specific information, performing complex data transformations, or developing reusable macros, the strategic use of macro variables will enhance your efficiency and effectiveness as an SDTM programmer.

Popular posts from this blog

SAS Interview Questions and Answers: CDISC, SDTM and ADAM etc

-
1) What do you know about CDISC and its standards? CDISC stands for Clinical Data Interchange Standards Consortium and it is developed keeping in mind to bring great deal of efficiency in the entire drug development process. CDISC brings efficiency to the entire drug development process by improving the data quality and speed-up the whole drug development process and to do that CDISC developed a series of standards, which include Operation data Model (ODM), Study data Tabulation Model (SDTM) and the Analysis Data Model ADaM ). 2) Why people these days are more talking about CDSIC and what advantages it brings to the Pharmaceutical Industry? A) Generally speaking, Only about 30% of programming time is used to generate statistical results with SAS®, and the rest of programming time is used to familiarize data structure, check data accuracy, and tabulate/list raw data and statistical results into certain formats. This non-statistical programming time will be significantly reduced...
Read more

Comparing Two Methods for Removing Formats and Informats in SAS: DATA Step vs. PROC DATASETS

-
Comparing Two Approaches to Removing Formats and Informats in SAS Comparing Two Approaches to Removing Formats and Informats in SAS When working with SAS datasets, there are times when you need to remove formats and informats that have been previously assigned to variables. Two primary approaches can be used for this task: Using the DATA Step Using the PROC DATASETS Procedure This article compares and contrasts these two approaches to help you determine which method is most appropriate for your needs. Approach 1: Using the DATA Step The DATA step is a versatile and commonly used method for removing formats and informats. By assigning variables to a null format or informat, you can effectively remove these attributes from your dataset. Example: data mydata_clean; set mydata; format _all_; informat _all_; run; In this example, the mydata dataset is processed in the DATA step, and...
Read more

Studyday calculation ( --DY Variable in SDTM)

-
USE OF THE “STUDY DAY” VARIABLES The permissible Study Day variables (--DY, --STDY, and --ENDY) describe the relative day of the observation starting with the reference date as Day 1. They are determined by comparing the date portion of the respective date/time variables (--DTC, --STDTC, and --ENDTC) to the date portion of the Subject Reference Start Date (RFSTDTC from the Demographics domain). The Subject Reference Start Date (RFSTDTC) is designated as Study Day 1. The Study Day value is incremented by 1 for each date following RFSTDTC. Dates prior to RFSTDTC are decremented by 1, with the date preceding RFSTDTC designated as Study Day -1 (there is no Study Day 0). This algorithm for determining Study Day is consistent with how people typically describe sequential days relative to a fixed reference point, but creates problems if used for mathematical calculations because it does not allow for a Day 0. As such, Study Day is not suited for use in subsequent numerical computations...
Read more