Below is the currently effective AICPA SASs in codified format. The titles and codified AU-C sections in AICPA Professional Standards are provided.

In pharmacology, the area under the plot of plasma concentration of a drug versus time after dosage (called “area under the curve” or AUC) gives insight into the extent of exposure to a drug and its clearance rate from the body. [2] The AUC (from zero to infinity) represents the total drug exposure across time.

In 2020 U.S. peer reviews, the currently effective AU-C section 315, Understanding the Entity and Its Environment and Assessing the Risks of Material Misstatement, was the leading source of matters for further consideration.

SAS No. 148, Amendment to AU-C Section 935, amends AU-C section 935, Compliance Audits, in AICPA Professional Standards to Update the appendix, “AU-C Sections That Are Not Applicable to Compliance Audits”

Mar 10, 2020 · A new standard issued Tuesday by the AICPA Auditing Standards Board (ASB) is designed to align the AU-C 800 series in AICPA Professional Standards with the new standard on auditor reporting.

AUC 0-inf extrapolates the area to infinite time. This requires a terminal elimination phase to be characterized. AUC 0-inf comprises AUC 0-last plus the extrapolated area AUC t-inf which is calculated by; AUC t-inf = C pt / Kel. where C pt is the plasma concentration at the last quantifiable time point and Kel is the terminal elimination rate ...

U.S. Auditing Standards — AICPA (Clarified) AU-C Cross-References to SASs AU-C INTRODUCTION AU-C Sections 200–299 AU-C Sections 300–499 AU-C Sections 500–599 AU-C Sections 600–699 AU-C Sections 700–799 AU-C Sections 800–899 AU-C Sections 900–999 AU-C Appendixes AU-C Topical Index

This table shows the AU-C sections that are created, superseded, or amended by SASs No. 134– 140. The affected AU-C sections are color-coded to the first SAS to amend that section.

The c-statistic, also known as AUROC (area under the curve), the concordance statistic, is equal to to the AUC (area under curve)

Sep 16, 2023 · In Cox regression we usually take one minus c c because the linear predictor is such that high values ⇒ ⇒ shorter survival time. Note that c c is a generalization of AUROC and is computed during cross-validation or bootstrap using for example the R rms package validate function. It is important to correct for overfitting, which validate does.