This page provides an independent statistical analysis and educational interpretation of publicly reported results. ClinicalTrials.gov provides the official trial registry record.
1. Trial at a Glance
UKPDS was a completed clinical trial investigating high-sensitivity C-reactive protein and the United Kingdom Prospective Diabetes Study Risk Score in individuals with type 2 diabetes.
| Feature | Registry Information |
|---|---|
| ClinicalTrials.gov identifier | NCT01099865 |
| Status | Completed |
| Conditions | Atherosclerosis; Type 2 Diabetes |
| Lead sponsor | Korea University |
| Sponsor type | Other |
2. Clinical Question
Population
Participants with type 2 diabetes enrolled in the UKPDS study.
Intervention
The registry describes evaluation of high-sensitivity C-reactive protein and UKPDS Risk Score measurements.
Comparator
No comparator group is reported in the registry information available for this analysis.
Primary question
The study evaluates the relationship between high-sensitivity C-reactive protein and the UKPDS Risk Score in type 2 diabetes.
3. Trial Design
Design characteristics
The registry identifies UKPDS as a completed clinical trial with enrollment of 56 participants.
Study period
The recorded study period began in December 2009 with primary completion in April 2010.
4. Endpoints
The ClinicalTrials.gov record does not list registered primary endpoints for this study.
5. Statistical Methodology
Because registered primary endpoints and posted statistical analyses are not reported in the registry record, the formal confirmatory analysis framework cannot be described from the available record.
For biomarker-oriented studies, statistical approaches commonly depend on the measurement scale, distributional assumptions, study design, and prespecified objectives.
6. Planned Analysis
The registry identifies evaluation of high-sensitivity C-reactive protein and UKPDS Risk Score in type 2 diabetes. Results have not been posted on ClinicalTrials.gov.
For a study of this type, analyses would typically describe biomarker distributions and evaluate relationships between high-sensitivity C-reactive protein measurements and risk-score measures using methods appropriate to the observed data structure.
7. Statistical Methods Explained
Why are biomarkers often analyzed with distribution-aware methods?
Biomarkers frequently have skewed distributions. Statistical models may need to account for this through transformations or methods that match the measurement characteristics.
What does an association analysis measure?
An association describes whether two measured variables vary together. It does not by itself establish causality.
Why does sample size matter?
The enrollment of 56 participants affects the precision of estimates that can be obtained and the ability to detect small relationships.
Why must endpoints be prespecified?
Prespecified endpoints help define the intended scientific questions and reduce the risk of selectively emphasizing findings after observing results.
8. Limitations
- The registry does not list registered primary endpoints.
- The registry does not report posted statistical analyses.
- ClinicalTrials.gov does not report trial outcome results for this record.
- The available registry information does not describe randomization, masking, stratification, missing-data methods, or multiplicity adjustments.
9. Why This Trial Matters Statistically
UKPDS provides an example of how clinical biomarkers and risk prediction measures can be evaluated within a clinical research framework. It illustrates the importance of clearly defined endpoints, appropriate statistical methods, and transparent reporting of analyses.
| Concept | Application |
|---|---|
| Biomarker analysis | High-sensitivity C-reactive protein evaluation |
| Risk prediction | UKPDS Risk Score assessment |
| Study reporting | Registry-based clinical trial documentation |
10. Sources
- ClinicalTrials.gov: NCT01099865