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Kidney Transplant Rejection Device Monitoring Longitudinal Surveillance NCT04566055

ADMIRAL: Complete Statistical Analysis of AlloSure in Kidney Transplant Rejection

An independent statistical review of ADMIRAL, evaluating AlloSure donor-derived cell-free DNA monitoring insights for renal allografts with longitudinal surveillance.

ClinicalTrials.gov registry analysis · ADMIRAL · NCT04566055
Scope of this record

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

ADMIRAL was a clinical study evaluating AlloSure monitoring insights of renal allografts with longitudinal surveillance in the setting of kidney transplant rejection.

1000
Enrollment
1
Study Arm
2019-03-01
Start Date
2020-10-01
Primary Completion
FeatureADMIRAL
ClinicalTrials.gov identifierNCT04566055
StatusUNKNOWN
ConditionKidney Transplant Rejection
Lead sponsorCareDx
Sponsor typeINDUSTRY
InterventionAlloSure (device)

2. Clinical Question

Population

Patients represented in a longitudinal surveillance study of renal allografts.

Intervention

AlloSure device monitoring.

Comparator

The registry describes one study arm.

Primary question

Validation of AlloSure in allograft rejection.

3. Trial Design

01
Enrollment
1000 participants
02
Monitoring
AlloSure device
03
Surveillance
Longitudinal follow-up
04
Endpoint
October 2020
Study structure

Single-arm clinical study.

Study objective

Assessment of AlloSure in allograft rejection.

4. Primary Endpoint

EndpointTime frame
Validation of AlloSure in Allograft rejectionOctober 2020

5. Planned Analysis

The registry identifies validation of AlloSure in allograft rejection as the primary endpoint. Results have not been posted on ClinicalTrials.gov.

For a validation endpoint involving a diagnostic or monitoring device, statistical evaluation commonly focuses on the relationship between the device measurement and evidence of the clinical condition being evaluated. Depending on the prespecified analysis plan, this type of study may involve measures of diagnostic performance, agreement, discrimination, or association with rejection outcomes.

Statistical perspective

A validation study differs from a randomized treatment comparison. The primary statistical question is generally whether a measurement method provides reliable information for identifying or monitoring a clinical state rather than whether one intervention improves outcomes compared with another.

6. Statistical Methodology

Validation study framework

Validation studies require a clearly defined clinical reference standard and prespecified criteria for determining how well a measurement performs. Important considerations include the population evaluated, timing of measurements, missing observations, and the relationship between device readings and clinical outcomes.

Single-arm analysis

Because the registry describes one study arm, the interpretation focuses on characterization and validation of AlloSure monitoring rather than a randomized between-group treatment effect.

Uncertainty in statistical estimates

Any estimated diagnostic or predictive performance measure should be interpreted together with uncertainty measures such as confidence intervals. A point estimate alone does not describe the precision of the finding.

7. Statistical Methods Explained

How is a monitoring device validated?

Validation generally evaluates whether measurements from the device correspond to clinically meaningful states. The statistical approach depends on the endpoint definition and reference method.

Why does a single-arm design matter?

Without a randomized comparator group, the study is not designed to estimate a treatment effect between two assigned interventions. Instead, it evaluates performance or association within the studied population.

Why are confidence intervals important?

Confidence intervals describe uncertainty around an estimate. A narrow interval suggests greater precision, while a wide interval indicates more uncertainty.

Why does endpoint definition matter?

The statistical interpretation depends on exactly how rejection and monitoring performance are defined. Different endpoint definitions answer different clinical questions.

Why can missing data affect validation studies?

Incomplete measurements or unavailable reference assessments can influence estimates and should be addressed according to a prespecified analysis plan.

8. Limitations

9. Why This Trial Matters Statistically

ADMIRAL illustrates important concepts in clinical measurement research and statistical validation.

ConceptApplication
Validation analysisEvaluating whether a monitoring approach provides clinically useful information.
Single-arm designFocuses on characterization rather than randomized treatment comparison.
Endpoint definitionDetermines the exact statistical question being answered.
Confidence intervalsDescribe uncertainty around estimates.

10. Sources