BioPET: Biomarker Prognostic Enrichment Tool

BioPET is a tool to help evaluate biomarkers for prognostic enrichment of clinical trials. Prognostic Enrichment (Temple, 2010; PMID 20944560) is a clinical trial strategy of evaluating an intervention in a patient population with a higher rate of the unwanted event than the broader patient population. This higher event rate translates to a lower sample size for the clinical trial, which can have both practical and ethical advantages.

BioPET is also available as a package for the R Statistical Computing Platform. The R package offers extended functionality, allowing investigators to analyze their own biomarker data rather than relying on prototypical ROC curves.

Input Information


Clinical Trial Information

Event rate in the non-intervention group

The prevalence of the adverse event in the non-intervention group. For example, 20% of patients in the non-intervention group may be expected to experience the unwanted event.

Percent reduction in event rate under treatment

The effect size of the intervention as represented by the percent reduction in the event rate for patients using the therapy that the trial should be powered to detect. For example, we may want to design a trial powered to detect a 30% reduction in the event rate.

Form of alternative hypothesis

Indicates whether the study will use one- or two-sided hypothesis testing.

Type I error rate

The probability of rejecting the null hypothesis, given that the null hypothesis is actually true. Common settings are 0.025 and 0.05.


The probability of rejecting the null hypothesis, given that the null hypothesis is actually false. For example, we might design our clinical trial to have 90% power to detect the treatment effect.

Biomarker Information

AUC of biomarker

The area under the ROC curve for the biomarker, summarizing the biomarker's ability to distinguish between cases and controls.

ROC curve for biomarker

Asks for additional information about the shape of the ROC curve that yields the inputted AUC. The three displayed ROC curves have the same AUC, but have different shapes (symmetric, left-shifted, or right-shifted) which provide information about the predictive capacility of the biomarker.

Cost Information


Cost of screening a patient to determine trial eligibility

For example the cost of measuring the biomarker to determine patient eligibility for the trial may be $100.

“Cost of running a patient through the trial”

For example, the cost of enrolling and retaining a patient in a trial may be $1000.

Sample size

The sample size required for a clinical trial enrolling only patients who are biomarker-positive.

Number needed to screen (NNS)

The estimated number of patients who need to be screened to identify one patient eligible for the trial.

Event rate among biomarker-positive patients

The estimated event rate among the trial participants if the biomarker were used for prognostic enrichment.

Total screened

The estimated total number of individuals who must be screened to enroll the prognostically enriched trial.

Total cost

The estimated total cost of running the trial if the biomarker were used for prognostic enrichment.


This work was supported by NIH grant R01085757 (

Clinical Trial Information

Biomarker Information

Cost Information


Table 1: Summary Measures as a Function of Biomarker 1 Value Used for Screening

Table 2: Summary Measures as a Function of Biomarker 2 Value Used for Screening

Table 3: Summary Measures as a Function of Biomarker 3 Value Used for Screening