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.
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.
Indicates whether the study will use one- or two-sided hypothesis testing.
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.
The area under the ROC curve for the biomarker, summarizing the biomarker's ability to distinguish between cases and controls.
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.
For example the cost of measuring the biomarker to determine patient eligibility for the trial may be $100.
For example, the cost of enrolling and retaining a patient in a trial may be $1000.
The sample size required for a clinical trial enrolling only patients who are biomarker-positive.
The estimated number of patients who need to be screened to identify one patient eligible for the trial.
The estimated event rate among the trial participants if the biomarker were used for prognostic enrichment.
The estimated total number of individuals who must be screened to enroll the prognostically enriched trial.
The estimated total cost of running the trial if the biomarker were used for prognostic enrichment.
This work was supported by NIH grant R01085757 (http://patr.yale.edu)