How to Conduct Accurate and Robust Statistical Analyses in Clinical Trials

Lessons Learned from My Experience Managing Global Clinical Research Projects

By Patricio Ledesma

One of the most important lessons I have learned during my career in clinical research is that a clinical trial is only as reliable as the statistical analysis behind it.

Sponsors often invest millions of dollars in patient recruitment, site management, monitoring, data management, and regulatory activities. 

Yet even the best-designed clinical trial can fail if the statistical programming and analysis are not performed with rigor, consistency, and quality controls.

Over the years, I have seen successful studies reach regulatory submission smoothly because statistical planning was performed correctly from the beginning. 

I have also seen situations where avoidable programming errors, inconsistent datasets, or poorly specified analyses created significant delays.

In my experience, high-quality statistical analysis is not simply about running SAS programs. 

It requires a combination of strong methodology, regulatory compliance, robust programming practices, independent quality control, and deep understanding of the clinical objectives.

Below are the most important best practices I recommend to sponsors, CROs, biostatisticians, and statistical programmers seeking reliable and submission-ready results.

1. Start With a Clear Statistical Analysis Plan (SAP)

One of the biggest mistakes I see is beginning programming activities before the Statistical Analysis Plan is fully developed.

A comprehensive SAP should clearly define:

• Primary endpoints
• Secondary endpoints
• Analysis populations
• Handling of missing data
• Statistical methods
• Subgroup analyses
• Sensitivity analyses
• Interim analyses
• Multiplicity adjustments

The guidance established in ICH E9 emphasizes the importance of defining trial objectives, estimands, analysis methodologies, and sensitivity analyses before data review begins. 

My recommendation is simple:

Never start producing Tables, Listings and Figures (TLFs) until the SAP is finalized, approved, and version controlled.

2. Build SDTM and ADaM Datasets Correctly

Regulatory authorities expect data to follow CDISC standards.

The industry-standard process typically involves:

Raw Data → SDTM → ADaM → Statistical Outputs

According to industry guidance on clinical SAS programming, SDTM and ADaM remain foundational standards for regulatory submissions and reproducible clinical analyses. 

Among all programming activities, ADaM dataset creation is often where the greatest value is generated.

Well-designed ADaM datasets should:

• Be traceable back to source data
• Support reproducible analyses
• Minimize programming complexity
• Facilitate regulatory review

In my experience, investing additional time in ADaM development saves hundreds of programming hours later in the study.

3. Verify Every Critical Output Independently

One principle I strongly believe in is:

No important analysis should ever be based on a single programmer's output.

For pivotal studies, especially Phase 3 oncology trials, independent programming validation is essential.

My preferred approach is:

• Primary programmer produces outputs
• Independent programmer replicates results
• Differences are investigated immediately
• Final outputs are released only after reconciliation

This dual-programming method dramatically reduces the risk of unnoticed programming errors.

4. Use Standardized and Validated SAS Macros

Many organizations waste time recreating programs for every new study.

Instead, I recommend building validated macro libraries for:

• Demographics tables
• Adverse events summaries
• Laboratory analyses
• Concomitant medications
• Survival analyses
• Kaplan-Meier plots
• Forest plots

Clinical SAS programming best practices emphasize the importance of reusable macro programming, standard reporting workflows, and systematic quality control approaches. 

Standardization provides:

• Greater consistency
• Faster study startup
• Reduced validation effort
• Lower risk of programming errors

5. Pay Special Attention to Missing Data

Missing data represents one of the greatest threats to statistical validity.

Clinical trials frequently experience:

• Patient withdrawal
• Missed visits
• Lost follow-up assessments
• Protocol deviations

Regulatory guidance under ICH E9(R1) highlights the importance of defining how intercurrent events and missing information will be handled through estimands and sensitivity analyses. 

I always advise teams to:

• Define missing data strategies early
• Perform sensitivity analyses
• Evaluate potential biases
• Document assumptions clearly

Never assume missing data are random without evidence.

6. Implement Rigorous Programming Quality Control

High-quality statistical analyses require formal QC procedures.

My preferred QC framework includes:

Code Review

• Peer review of all programming code
• Documentation of findings
• Resolution tracking

Validation

• Independent dataset checks
• Record-level comparisons
• Derived variable verification

Output Verification

• Manual spot-checking
• Programmatic comparison
• Cross-validation against specifications

Quality is not something added at the end.

Quality must be embedded into every step of the statistical workflow.

7. Maintain Complete Traceability

Regulatory reviewers increasingly focus on data traceability.

Every result should have a documented path from:

Raw Data → SDTM → ADaM → Analysis Dataset → Output

The clinical programming ecosystem supporting regulatory submissions places significant emphasis on traceability, metadata documentation, and Define-XML specifications. 

When reviewers ask:

"How was this number generated?"

The answer should be obtainable within minutes.

Not days.

8. Use Appropriate Statistical Methods for the Endpoint

I frequently encounter studies where statistical methods are selected because they are familiar rather than appropriate.

Each endpoint requires careful consideration.

Examples include:

Modern clinical SAS programming routinely incorporates procedures such as PROC PHREG, PROC LIFETEST, PROC LOGISTIC, and other validated statistical methods aligned with trial objectives. 

Methodology should always be driven by scientific objectives.

Not programmer convenience.

9. Document Everything

I consider documentation one of the most undervalued activities in clinical research.

Every study should maintain:

• Programming specifications
• Dataset specifications
• SAP versions
• Validation reports
• QC logs
• Issue trackers
• Programming conventions

Good documentation protects the project long after key personnel have moved on.

10. Build Strong Collaboration Between Statisticians and Programmers

The best projects I have participated in shared one common characteristic:

Excellent communication.

Statisticians understood programming limitations.

Programmers understood scientific objectives.

Clinical teams understood endpoint definitions.

When these functions work in isolation, mistakes happen.

When they collaborate effectively, the quality of the final analyses improves significantly.

My Top 5 Practical Tips for Sponsors

If I had to summarize everything into five recommendations, they would be:

1. Finalize the SAP before programming begins.
2. Build ADaM datasets with traceability in mind.
3. Validate all critical outputs independently.
4. Standardize SAS programming through reusable macros.
5. Perform sensitivity analyses to demonstrate robustness.

Conclusion

After many years working with biotech companies, pharmaceutical sponsors, CROs, biostatisticians, and statistical programmers worldwide, I have learned that robust statistical analysis is much more than a regulatory requirement.

It is a scientific responsibility.

Every statistical table, figure, and analysis may ultimately influence decisions affecting patient care, regulatory approvals, and future treatment options.

That is why the most successful clinical trials typically share the same characteristics: well-designed protocols, clear Statistical Analysis Plans, rigorous data standards such as CDISC, independent programming validation, and comprehensive quality control processes grounded in principles established by ICH E9. 

In my experience, organizations that invest in these practices consistently generate faster, more reliable, and regulator-ready analyses, while reducing risk, rework, and submission delays.