Revolutionizing SDTM Programming in Pharma with ChatGPT

Revolutionizing SDTM Programming in Pharma with ChatGPT
By Sarath

Introduction
In the pharmaceutical industry, standardizing…

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Unleashing the Power of PROC DATASETS in SAS

The PROC DATASETS procedure is a versatile and efficient tool within SAS for managing datasets. Often described as the “Swiss Army Knife” of SAS procedures, it allows users to perform a variety of tasks such as renaming, deleting, modifying attributes, appending datasets, and much more, all while consuming fewer system resources compared to traditional data steps. In this article, we’ll explore key use cases, functionality, and examples of PROC DATASETS, illustrating why it should be part of every SAS programmer’s toolkit.

1. Why Use PROC DATASETS?

Unlike procedures like PROC APPEND, PROC CONTENTS, and PROC COPY, which focus on specific tasks, PROC DATASETS integrates the functionalities of these procedures and more. By using PROC DATASETS, you avoid the need for multiple procedures, saving both time and system resources since it only updates metadata instead of reading and rewriting the entire dataset.

2. Basic Syntax of PROC DATASETS

The basic structure of PROC DATASETS is as follows:

PROC DATASETS LIBRARY=;
    ;
RUN; QUIT;

Here, you specify the library containing the datasets you want to modify. Commands such as CHANGE, DELETE, APPEND, MODIFY, and RENAME follow within the procedure.

3. Use Case 1: Renaming Datasets and Variables

Renaming datasets and variables is a simple yet powerful capability of PROC DATASETS. Here’s an example of how you can rename a dataset:

PROC DATASETS LIBRARY=mylib;
    CHANGE old_data=new_data;
RUN; QUIT;

To rename a variable within a dataset:

PROC DATASETS LIBRARY=mylib;
    MODIFY dataset_name;
    RENAME old_var=new_var;
RUN; QUIT;

4. Use Case 2: Appending Datasets

The APPEND statement is a highly efficient alternative to using SET in a data step because it only reads the dataset being appended (the DATA= dataset), instead of reading both datasets.

PROC DATASETS LIBRARY=mylib;
    APPEND BASE=master_data DATA=new_data;
RUN; QUIT;

5. Use Case 3: Deleting Datasets

Deleting datasets or members within a library is simple with PROC DATASETS. You can delete individual datasets or use the KILL option to remove all members of a library:

PROC DATASETS LIBRARY=mylib;
    DELETE dataset_name;
RUN; QUIT;

PROC DATASETS LIBRARY=mylib KILL;
RUN; QUIT;

6. Use Case 4: Modifying Attributes

You can modify variable attributes such as labels, formats, and informats without rewriting the entire dataset:

PROC DATASETS LIBRARY=mylib;
    MODIFY dataset_name;
    LABEL var_name='New Label';
    FORMAT var_name 8.2;
RUN; QUIT;

7. Advanced Operations with PROC DATASETS

7.1. Working with Audit Trails

You can use PROC DATASETS to manage audit trails, which track changes made to datasets. For instance, the following code creates an audit trail for a dataset:

PROC DATASETS LIBRARY=mylib;
    AUDIT dataset_name;
    INITIATE;
RUN; QUIT;

7.2. Managing Indexes

Indexes help retrieve subsets of data efficiently. You can create or delete indexes with PROC DATASETS:

PROC DATASETS LIBRARY=mylib;
    MODIFY dataset_name;
    INDEX CREATE var_name;
RUN; QUIT;

7.3. Cascading File Renaming with the AGE Command

Another useful feature is the AGE command, which renames a set of files in sequence:

PROC DATASETS LIBRARY=mylib;
    AGE file1-file5;
RUN; QUIT;

8. Conclusion

PROC DATASETS is an indispensable tool for SAS programmers. Its efficiency and versatility allow you to manage datasets with ease, from renaming and deleting to appending and modifying attributes. By leveraging its full potential, you can streamline your SAS workflows and significantly reduce processing times.

Sources

• Ken Friedman, “Using PROC DATASETS for Efficient SAS Processing”
• David Fickbohm, “Why Use PROC DATASETS”
• Michael A. Raithel, “PROC DATASETS: The Swiss Army Knife of SAS Procedures”

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Advanced SDTM Mapping Pitfalls and How to Avoid Them

Advanced SDTM Mapping Pitfalls and How to Avoid Them

Introduction
Mapping clinical data to SDTM domains is a complex process involving many technical and logical chall…

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proc sort data=mydata nodupkey;
    by usubjid visitnum;
run;

%macro create_sdtm_report(domain);
    data &domain._report;
        set sdtm.&domain.;
        /* Apply domain-specific transformations */
    run;
%mend;

%create_sdtm_report(DM);
%create_sdtm_report(LB);

data dc_domain;
    set raw_data;
    /* Capture specific collected demographic data */
    where not missing(collected_age) and not missing(collected_gender);
run;

data check;
    set sdtm.dm;
    if missing(usubjid) then putlog "ERROR: Missing USUBJID at obs " _n_;
run;

data lab_repeated;
    set sdtm.lb;
    array lb_vals{3} lbtestcd1-lbtestcd3;
    do i=1 to dim(lb_vals);
        if lb_vals{i} = . then putlog "WARNING: Missing value for LBTESTCD at " _n_;
    end;
run;

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Excluding Variables in PROC COMPARE Using the DROP Dataset Option

When comparing two datasets using PROC COMPARE in SAS, there may be cases where you want to exclude specific variables from the comparison. One efficient way to do this is by using the DROP dataset option. This option allows you to remove certain variables from consideration during the comparison process.

Using the DROP Dataset Option in PROC COMPARE

The DROP dataset option is applied directly to the dataset references in the BASE and COMPARE options of PROC COMPARE. When you use DROP, the specified variables are excluded from the comparison.

Here is the syntax for using the DROP option:

proc compare base=dataset1(drop=var_to_exclude) 
                compare=dataset2(drop=var_to_exclude);
    run;

In this example, var_to_exclude is the name of the variable you want to exclude from both datasets (dataset1 and dataset2) before the comparison is made.

Example: Excluding a Variable from the Comparison

Let’s say you have two datasets, sales_2023 and sales_2024, and you want to compare them, but you want to exclude a variable called region from the comparison. Here is how you can do that:

proc compare base=sales_2023(drop=region) 
                compare=sales_2024(drop=region);
    run;

This code ensures that the variable region will not be included in the comparison, while all other variables in the two datasets will be compared.

Excluding Multiple Variables

You can also exclude multiple variables by listing them inside the DROP option, separated by spaces:

proc compare base=sales_2023(drop=region quarter) 
                compare=sales_2024(drop=region quarter);
    run;

In this case, both region and quarter will be excluded from the comparison.

Conclusion

The DROP dataset option is a powerful and simple tool for excluding variables from comparison in PROC COMPARE. It provides flexibility by allowing you to exclude one or more variables without needing to manually specify which variables should be included in the comparison.

By leveraging the DROP option, you can ensure that only relevant variables are considered when comparing datasets, making your SAS programming more efficient and focused.

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