What Are the Most Common Statistical Programming Questions Clinical Trial Sponsors Ask?

A Practical Perspective from Patricio Ledesma

When I speak with biotech companies, pharmaceutical sponsors, and clinical development teams, I often notice the same pattern.

The questions may come from different organizations.

The therapeutic areas may be different.

The studies may range from Phase I first-in-human trials to global Phase III pivotal programs.

But the underlying statistical programming concerns are remarkably similar.

Sponsors want confidence.

They want quality.

They want regulatory compliance.

And they want submission-ready deliverables that will stand up to scrutiny from authorities such as the FDA, EMA, MHRA, PMDA, and Health Canada.

Over the years at Sofpromed, I have participated in hundreds of discussions with sponsors regarding CDISC implementation, ADaM derivations, TLF production, SAS validation, and submission readiness.

In this article, I will discuss the most common statistical programming questions sponsors ask and explain why these topics matter so much.

CDISC Standards & Regulatory Compliance

Question 1: How are SDTM and ADaM datasets implemented?

This is probably the most common question sponsors ask.

Before a sponsor can submit data to regulators, raw clinical data must be organized according to CDISC standards.

The process typically follows a structured flow.

Table 1: Stages from EDC data collection to TLF production

Table 2: Typical SDTM domains

After SDTM creation, the data are transformed into ADaM datasets.

ADaM datasets support statistical analyses.

Table 3: ADaM dataset examples

Sponsors want assurance that every derivation follows CDISC standards and regulatory expectations.

They also want to understand whether reusable, validated programming standards are being used.

Question 2: Is the define.xml file accurate and complete?

A define.xml file acts as the metadata roadmap for a regulatory submission.

Regulators rely heavily on it.

A technically correct dataset submission can still generate regulatory questions if define.xml documentation is incomplete.

Table 4: Key components of define.xml

Sponsors typically ask:

• Are all variables described?
• Are computational algorithms documented?
• Are codelists correctly referenced?
• Are derivation methods traceable?
• Does the file validate against current standards?

A high-quality define.xml reduces review risks substantially.

Question 3: How is data traceability maintained?

Traceability is a cornerstone of regulatory compliance.

Regulators expect to trace any result back to its source.

A sponsor may ask:

"Can you demonstrate exactly how this efficacy endpoint was derived?"

The answer should always be yes.

Table 5: Data traceability flow in a clinical study

Table 6: Example of data traceability chain

Every analysis variable should have documented lineage.

This is especially important during regulatory inspections.

Validation & Quality Control

Question 1: How are SAS programs validated?

Sponsors understandably place tremendous emphasis on validation.

A programming output is only valuable if it can be trusted.

Table 7: Common validation approaches in statistical programming

Table 8: Relative effort of validation methods

Double programming remains one of the most respected methods because two independent programmers produce the same result separately.

If both outputs match, confidence increases significantly.

Question 2: How are discrepancies between the Statistical Analysis Plan (SAP) and outputs resolved?

This question appears in nearly every study.

The SAP defines what analyses should be performed.

The statistical programming team implements those analyses.

Occasionally discrepancies emerge.

Table 9: Examples of discrepancies in statistical analyses

Table 10: Discrepancy resolution process

Sponsors expect complete documentation of every decision.

Question 3: How are SAS logs and error/warning flags handled?

This is another very common audit question.

Every SAS log should be reviewed.

Table 11: SAS log review expectations

Table 12: Example log quality metrics

Clean logs are a hallmark of mature programming teams.

Data Handling & Edge Cases

Question 1: How are missing data, partial dates, or imputed values handled?

Clinical trial data are rarely perfect.

Missing dates and incomplete information are common.

Sponsors want to know exactly how those situations are managed.

Table 13: Common partial date examples

Table 14: Common imputation methods

Every imputation rule should be predefined.

Consistency is essential.

Question 2: How are adverse events (AEs), concomitant medications, or lab data anomalies managed?

This topic generates many sponsor questions.

Real-world clinical datasets contain complications.

Table 15: Typical data challenges

Table 16: AE programming checks

Laboratory data undergo similar validation.

Extreme values.

Duplicate visits.

Reference range changes.

Unit conversion discrepancies.

All require systematic review.

Efficiency & Deliverables

Question 1: Are macros and automated workflows modular and documented?

Sponsors increasingly evaluate programming infrastructure.

They do not simply review final deliverables.

They assess the programming environment itself.

Table 17: Characteristics of high-quality macros

Table 18: Benefits of automation

At Sofpromed, we strongly encourage standardized, reusable programming frameworks because they improve both quality and efficiency.

Question 2: How are TLFs formatted for review?

Sponsors spend enormous amounts of time reviewing Tables, Listings, and Figures.

Presentation quality matters.

Even perfectly correct results can slow review cycles if formatting is poor.

Table 19: Typical TLF deliverables

Table 20: Key formatting requirements

Table 21: Common regulatory submission TLF characteristics

Modern SAS programming frequently uses PROC REPORT, ODS RTF, ODS PDF, and automated reporting frameworks to generate submission-ready outputs.

Conclusions

In my experience, sponsors are not simply hiring statistical programmers to write SAS code.

They are looking for regulatory partners.

They want confidence that their data can withstand regulatory review.

They want SDTM and ADaM datasets that follow CDISC standards.

They want traceability from source data to final outputs.

They want validated programs.

They want clean SAS logs.

They want high-quality Tables, Listings, and Figures.

Most importantly, they want a team that understands both statistics and regulatory science.

At Sofpromed, our statistical programming teams work alongside biostatisticians, clinical data managers, medical writers, and regulatory specialists to help sponsors achieve exactly that goal.

Whether the project is a Phase I oncology study, a rare disease program, a global Phase III trial, or an integrated submission package, the questions sponsors ask tend to be remarkably similar.

The difference lies in how effectively those questions are answered.

And that is where experienced statistical programming teams create real value for clinical trial sponsors.