Advanced SAS Programming Techniques for SDTM Implementation

Date: November 3, 2024

In the realm of clinical trials data management, SDTM (Study Data Tabulation Model) implementation requires sophisticated programming techniques to ensure data accuracy and compliance. This article explores advanced SAS programming methods that can streamline SDTM dataset creation and validation.

1. Efficient Variable Derivation Using Hash Objects

Hash objects in SAS provide a powerful way to perform quick lookups and merges, especially useful when dealing with large SDTM datasets.

data work.ae;

if _n_ = 1 then do;

declare hash h_dm(dataset: "sdtm.dm");

h_dm.definekey("usubjid");

h_dm.definedata("age", "sex", "race");

h_dm.definedone();

end;

set raw.ae;

rc = h_dm.find();

/* Continue processing */

run;

Pro Tip: Hash objects remain in memory throughout the DATA step, making them more efficient than traditional merge operations for large datasets.

2. Standardizing Controlled Terminology with Format Catalogs

Creating and maintaining CDISC-compliant terminology is crucial for SDTM implementation.

proc format library=library.sdtm_formats;

value $severity

'MILD' = 'MILD'

'MOD' = 'MODERATE'

'MODERATE' = 'MODERATE'

'SEV' = 'SEVERE'

'SEVERE' = 'SEVERE'

other = 'UNKNOWN';

run;

data sdtm.ae;

set work.ae;

aesev = put(raw_severity, $severity.);

run;

3. Macro Systems for Dynamic SDTM Generation

Developing reusable macro systems can significantly improve efficiency and reduce errors in SDTM implementation.

%macro create_supp(domain=, vars=);

proc sql noprint;

select distinct usubjid, &vars

into :subjids separated by ',',

:values separated by ','

from sdtm.&domain;

quit;

data sdtm.supp&domain;

set sdtm.&domain(keep=usubjid &vars);

length qnam $8 qlabel $40 qval $200;

/* Generate supplemental qualifiers */

run;

%mend create_supp;

4. Advanced Error Checking and Validation

Implementing robust error-checking mechanisms ensures data quality and compliance with SDTM standards.

%macro validate_domain(domain=);

proc sql noprint;

/* Check for duplicate records */

create table work.duplicates as

select *, count(*) as count

from sdtm.&domain

group by usubjid, &domain.dtc

having count > 1;

/* Verify required variables */

select name into :reqvars separated by ' '

from sashelp.vcolumn

where libname='SDTM' and memname=upcase("&domain")

and name in ('USUBJID', 'DOMAIN', "&domain.SEQ");

quit;

%mend validate_domain;

5. Handling Custom Domains and Extensions

Sometimes, standard SDTM domains need to be extended to accommodate study-specific requirements.

proc sql;

create table sdtm.custom_domain as

select a.usubjid,

a.visit,

b.startdt,

calculated enddt format=datetime20.

from derived.custom_data as a

left join sdtm.sv as b

on a.usubjid = b.usubjid

and a.visit = b.visit;

quit;

6. Optimizing Performance for Large Studies

When dealing with large studies, performance optimization becomes crucial:

1. Use WHERE clauses instead of IF statements when possible
2. Implement parallel processing for independent domains
3. Optimize sort operations using PROC SORT NODUPKEY

options mprint mlogic symbolgen;

%let parallel_domains = ae cm eg lb mh vs;

%macro process_domains;

%do i = 1 %to %sysfunc(countw(¶llel_domains));

%let domain = %scan(¶llel_domains, &i);

%submit;

%create_domain(domain=&domain)

%endsubmit;

%end;

%mend process_domains;

Best Practice: Always document your code thoroughly and include version control information for traceability.

Conclusion

Mastering these advanced SAS programming techniques can significantly improve the efficiency and quality of SDTM implementation. Remember to always validate your outputs against SDTM Implementation Guide requirements and maintain clear documentation of your programming decisions.