@: Advanced SDTM Programming Tips: Streamline Your SDTM Development with Expert Techniques

Advanced SDTM Programming Tips

Streamline Your SDTM Development with Expert Techniques

Tip 1: Automating SUPPQUAL Domain Creation

The SUPPQUAL (Supplemental Qualifiers) domain can be automated using SAS macros to handle additional variables in a systematic way. Refer to the macro example provided earlier to simplify your SUPPQUAL generation process.

Tip 2: Handling Date Imputation

Many SDTM domains require complete dates, but raw data often contains partial or missing dates. Use the following code snippet for date imputation:

                
data imputed_dates;
    set raw_data;
    /* Impute missing day to the first day of the month */
    if length(strip(date)) = 7 then date = cats(date, '-01');
    /* Impute missing month and day to January 1st */
    else if length(strip(date)) = 4 then date = cats(date, '-01-01');
    format date yymmdd10.;
run;

Tip: Always document the imputation logic and ensure it aligns with the study protocol.

Tip 3: Dynamic Variable Label Assignment

Avoid hardcoding labels when creating SDTM domains. Use metadata-driven programming for consistency:

                
data AE;
    set raw_ae;
    attrib
        AESTDTC label="Start Date/Time of Adverse Event"
        AEENDTC label="End Date/Time of Adverse Event";
run;

Tip: Store labels in a metadata file (e.g., Excel or CSV) and read them dynamically in your program.

Tip 4: Efficient Use of Pinnacle 21 Outputs

Pinnacle 21 validation reports can be overwhelming. Focus on the following key areas:

Major Errors: Address structural and required variable issues first.
Traceability: Ensure SUPPQUAL variables and parent records are linked correctly.
Controlled Terminology: Verify values against the CDISC CT library to avoid deviations.

Tip: Use Excel formulas or conditional formatting to prioritize findings in Pinnacle 21 reports.

Tip 5: Debugging Complex Mapping Issues

When debugging mapping logic, use PUTLOG statements strategically:

                
data SDTM_AE;
    set raw_ae;
    if missing(AEDECOD) then putlog "WARNING: Missing AEDECOD for USUBJID=" USUBJID;
run;

Tip: Use PUTLOG with conditions to reduce unnecessary log clutter.

Tip 6: Mapping RELREC Domain

The RELREC domain is used to define relationships between datasets. Automate its creation using a data-driven approach:

                
data RELREC;
    set parent_data;
    RELID = "REL1";
    RDOMAIN1 = "AE"; USUBJID1 = USUBJID; IDVAR1 = "AESEQ"; IDVARVAL1 = AESEQ;
    RDOMAIN2 = "CM"; USUBJID2 = USUBJID; IDVAR2 = "CMSEQ"; IDVARVAL2 = CMSEQ;
    output;
run;

Tip: Validate RELREC with Pinnacle 21 to ensure all relationships are correctly represented.

Tip 7: Using PROC DATASETS for Efficiency

Leverage PROC DATASETS for efficient dataset management:

                
                
proc datasets lib=work nolist;
    modify AE;
        label AESTDTC = "Start Date/Time of Adverse Event"
              AEENDTC = "End Date/Time of Adverse Event";
    run;
quit;

Tip: Use PROC DATASETS to modify attributes like labels, formats, and lengths without rewriting the dataset.

Tip 8: Deriving Epoch Variables

EPOCH is a critical variable in SDTM domains, representing the study period during which an event occurred. Automate its derivation as follows:

                
data AE;
    set AE;
    if AESTDTC >= TRTSDTC and AESTDTC <= TRTEDTC then EPOCH = "TREATMENT";
    else if AESTDTC < TRTSDTC then EPOCH = "SCREENING";
    else if AESTDTC > TRTEDTC then EPOCH = "FOLLOW-UP";
run;

Tip: Ensure EPOCH values are consistent with the study design and align with other SDTM domains like EX and SV.

Tip 9: Validating VISITNUM and VISIT Variables

VISITNUM and VISIT are critical for aligning events with planned visits. Use a reference table for consistency:

                
proc sql;
    create table validated_data as
    select a.*, b.VISIT
    from raw_data a
    left join visit_reference b
    on a.VISITNUM = b.VISITNUM;
quit;

Tip: Cross-check derived VISITNUM and VISIT values against the Trial Design domains (e.g., TV and TA).

Tip 10: Generating Define.XML Annotations

Define.XML is a crucial deliverable for SDTM datasets. Use metadata to dynamically create annotations:

                
data define_annotations;
    set metadata;
    xml_annotation = cats("<ItemDef OID='IT.", name, "' Name='", name, 
                          "' Label='", label, "' DataType='", type, "'/>");
run;

proc print data=define_annotations noobs; run;

Tip: Validate the Define.XML file using tools like Pinnacle 21 or XML validators to ensure compliance.

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Tuesday, December 3, 2024