Positive and negative estimates are commonly used by clinicians to evaluate the likelihood of a disease stage being present based on test results. The predicted values are dependent on the prevalence of the underlying illness. However, for certain diseases or clinical conditions, the prevalence is unknown or different from one region to another or from one population to another, leading to an erroneous diagnosis. This article introduces innovative post-test diagnostic precision measures for continuous tests or biomarkers based on the combined areas under the predictive value curves for all possible prevalence values. The proposed measures do not vary as a function of the prevalence of the disease. They can be used to compare different diagnostic tests and/or biomarkers' abilities for rule-in, rule-out, and overall accuracy based on the combined areas under the predictive value curves. The relationship of the proposed measures to other diagnostic accuracy measures is discussed. We illustrate the proposed measures numerically and use a real data example on breast cancer.