'Femi Fajimi

Avoiding Type I Error Misinterpretation in Clinical Study Reports: A Practical Perspective from Medical Writing

By Femi Fajimi | 15 April 2026

(Educational content. No confidential information included.)

In clinical trials, control of Type I error is a statistical responsibility defined in the protocol and the Statistical Analysis Plan (SAP). However, the interpretation and communication of those results occur later — primarily in the Clinical Study Report (CSR).

While statisticians control Type I error mathematically, misalignment between the SAP, Tables/Figures/Listings (TFLs), and the written narrative can lead to unintentional misrepresentation of results, particularly in CSR Sections 10–13 (Study Patients, Efficacy, Safety, and Discussion).

This article focuses on where this happens in practice, and how careful interpretation of SAP-defined analyses and TFL outputs is essential to maintaining the integrity of statistical conclusions.

1. Where Type I Error Becomes a Writing Risk

Type I error refers to the probability of concluding that a treatment effect exists when it does not (False Positive).

In trials with multiple endpoints, treatment arms, timepoints, or subgroup analyses, the risk of false-positive findings increases. This phenomenon is referred to as multiplicity, and without appropriate control, the overall Type I error rate — known as the family-wise error rate (FWER) can be inflated.

As highlighted in regulatory and statistical guidance, including work aligned with U.S. Food and Drug Administration expectations, FWER is typically controlled across endpoints intended to support regulatory claims, often through hierarchical testing or alpha allocation strategies.

These controls are defined in SAP, but they must be accurately reflected in the CSR narrative.

2. Why the CSR Is a Critical Point of Risk

By the time results reach the CSR:

• Statistical analyses have been completed
• Multiplicity strategies have been applied

TFLs reflect the correct outputs

However, the CSR requires the medical writer to:

➡️ translate statistical outputs into a structured narrative

➡️ describe results across endpoints and populations

➡️ interpret findings in the context of study objectives

This translation step introduces risk — not in the analysis, but in how results are described.

3. Practical Writing Risks Observed in CSR Development

3.1 Multiplicity and Interpretation of Secondary Endpoints

A common issue in CSR development arises when results for secondary or exploratory endpoints are described without adequate reference to the pre-specified multiplicity control strategy defined in the SAP.

Example risk (non-compliant wording):

“Treatment X demonstrated significant improvement in secondary endpoint B (p=0.04).”

Where a hierarchical (fixed-sequence) testing strategy has been implemented, statistical inference for secondary endpoints is conditional upon:

• Successful testing of preceding endpoints in the hierarchy, and
• Preservation of the pre-specified testing sequence

If these conditions are not met, subsequent endpoints are not considered part of the confirmatory testing framework, regardless of the observed p-value.

Regulatory-aligned interpretation

“A nominal p-value of 0.04 was observed for secondary endpoint B; however, as this endpoint was not controlled for multiplicity, the result should be interpreted with caution.”

Key principle

Statistical significance can be interpreted as confirmatory only when the endpoint is included in the predefined Type I error control strategy.

Where this is not the case, the CSR narrative should:

• Avoid definitive claims of efficacy
• Clearly distinguish exploratory findings
• Maintain alignment with SAP-defined testing rules

4. Understanding Multiplicity in Context

Multiplicity arises when multiple statistical tests are performed simultaneously, including:

• Multiple endpoints
• Multiple treatment arms or doses
• Repeated measurements over time
• Interim analyses
• Subgroup analyses

Without adjustment, the probability of at least one false positive increases according to:

➡️ FWER = 1 − (1 − α)^k

where k is the number of tests.

To control this, regulatory trials often apply:

• Hierarchical testing procedures
• Alpha splitting strategies
• Multiplicity adjustments (e.g. Bonferroni-type approaches)

Only endpoints included within this framework are considered confirmatory.

5. Maintaining Alignment During CSR Writing

In practice, maintaining statistical integrity during writing involves:

Mapping SAP → TFL → CSR

• Confirm endpoints included in multiplicity control
• Track testing hierarchy

Verifying populations and denominators

• Ensure correct analysis sets are used consistently.

Using precise, qualified language

• “Nominal p-value”
• “Exploratory analysis”
• “Not adjusted for multiplicity”

Avoiding over-interpretation

• Separate statistical findings from clinical interpretation

6. Conclusion

Type I error is controlled during trial design and analysis; however, its interpretation can be compromised during reporting.

The CSR represents a critical stage where statistical outputs are translated into regulatory evidence. Ensuring alignment with the SAP and accurate interpretation of TFLs is essential to preserving the validity of conclusions.

In this context, precision in writing is not only a matter of clarity, but it is also fundamental to maintaining the scientific and regulatory integrity of the submission.

References

• International Council for Harmonisation E9: Statistical Principles for Clinical Trials
• ICH E9(R1): Estimands and Sensitivity Analysis
• ICH E3: Clinical Study Reports
• U.S. Food and Drug Administration Draft Guidance on Multiple Endpoints in Clinical Trials
• Phastar Knowledge Centre – Multiplicity in Clinical Trials