Noncompartmental Analysis (R Track)

A computational, audit-minded workflow for running NCA in R: structure → compute → diagnose → report

A computational, audit-minded workflow for running NCA in R: define profiles, choose intervals, compute exposure metrics, diagnose reliability, and produce report-ready tables.

Welcome

Noncompartmental Analysis (R Track) is a hands-on module for clinical pharmacologists, pharmacometricians, and quantitative scientists who need to run NCA in a way that is fast, reproducible, and defensible.

The focus isn’t just how to compute AUC — it’s how to run NCA like an audit-minded PMx scientist:

define what a “profile” is (and what breaks it)
choose intervals intentionally (0–T, 0–tau, 0–inf, partial AUC windows)
compute exposure metrics with transparent assumptions
diagnose reliability so results are trustworthy
produce clean tables and figures you can drop into a report

Get Access

Enroll — Noncompartmental Analysis R Track

Try Free Previews First

Note

Already enrolled? Your enrollment is active and no additional purchase is required. Simply use the course navigation in the sidebar to access the course modules and continue where you left off.

Who this module is for

Clinical pharmacologists running exposure summaries in early studies
Pharmacometrics scientists who need defensible NCA workflows
Analysts producing report-ready AUC/Cmax/Tmax tables
Scientists who want to stop “click-and-export” NCA and make it reproducible
Anyone who has ever wondered: “Are these NCA results actually valid?”

Learning objectives

By the end of this module, you will be able to:

Explain what NCA is (and isn’t) within PMx workflows
Define what counts as one PK profile (ID + occasion/analyte/matrix when needed)
Build PKNCAconc(), PKNCAdose(), and PKNCAdata() objects correctly
Define exposure intervals deliberately (partial AUC windows, AUC over tau, AUCinf)
Run pk.nca() and extract results into clean, report-ready tables
Diagnose reliability using practical checks (extrapolated fraction, half-life sanity checks, visual QC)
Handle BLQ and missingness transparently and consistently
Spot the common structural failure modes that silently break NCA

Course structure

This module is organized as a repeatable workflow you can reuse on any study:

Structure → define profiles, validate uniqueness, confirm units and sorting
Compute → build PKNCA objects, define intervals, run pk.nca()
Diagnose → reliability checks (terminal phase, AUCinf extrapolation, visual QC)
Report → clean summary tables + reproducible plots for communication

After the final lesson, you’ll reach a Course Completion page where you can confirm completion and generate your certificate.

Tools used

R (≥ 4.2 recommended)
RStudio or Positron
tidyverse
PKNCA

Get started

Begin with NCA Foundations and complete the structural QC checklist before computing any exposure metrics.

--- description: "A computational, audit-minded workflow for running NCA in R: define profiles, choose intervals, compute exposure metrics, diagnose reliability, and produce report-ready tables." execute: echo: true message: false warning: false format: html: code-fold: true code-tools: true number-sections: false theme: cosmo toc: true toc-depth: 2 subtitle: "A computational, audit-minded workflow for running NCA in R: structure → compute → diagnose → report" title: Noncompartmental Analysis (R Track) --- ## Welcome **Noncompartmental Analysis (R Track)** is a hands-on module for clinical pharmacologists, pharmacometricians, and quantitative scientists who need to run NCA in a way that is fast, reproducible, and defensible. The focus isn't just *how to compute AUC* --- it's how to run NCA like an audit-minded PMx scientist: - define what a "profile" is (and what breaks it) - choose intervals intentionally (0--T, 0--tau, 0--inf, partial AUC windows) - compute exposure metrics with transparent assumptions - diagnose reliability so results are trustworthy - produce clean tables and figures you can drop into a report ------------------------------------------------------------------------ ## Get Access <div style="display: flex; gap: 0.75rem; flex-wrap: wrap; margin-top: 0.75rem;"> <a href="#" data-ms-modal="signup" data-ms-price:add="prc_nca-r-track-lifetime-access-standard--pse0097i" class="btn-primary" > Enroll — Noncompartmental Analysis R Track </a> <a href="/free/" class="btn-outline"> Try Free Previews First </a> </div> ::: {.callout-note} **Already enrolled?** Your enrollment is active and no additional purchase is required. Simply use the course navigation in the sidebar to access the course modules and continue where you left off. ::: ------------------------------------------------------------------------ ## Who this module is for - Clinical pharmacologists running exposure summaries in early studies - Pharmacometrics scientists who need defensible NCA workflows - Analysts producing report-ready AUC/Cmax/Tmax tables - Scientists who want to stop "click-and-export" NCA and make it reproducible - Anyone who has ever wondered: "Are these NCA results actually valid?" ------------------------------------------------------------------------ ## Learning objectives By the end of this module, you will be able to: - Explain what NCA is (and isn't) within PMx workflows - Define what counts as one PK profile (ID + occasion/analyte/matrix when needed) - Build `PKNCAconc()`, `PKNCAdose()`, and `PKNCAdata()` objects correctly - Define exposure intervals deliberately (partial AUC windows, AUC over tau, AUCinf) - Run `pk.nca()` and extract results into clean, report-ready tables - Diagnose reliability using practical checks (extrapolated fraction, half-life sanity checks, visual QC) - Handle BLQ and missingness transparently and consistently - Spot the common structural failure modes that silently break NCA ------------------------------------------------------------------------ ## Course structure This module is organized as a repeatable workflow you can reuse on any study: 1. Structure → define profiles, validate uniqueness, confirm units and sorting\ 2. Compute → build PKNCA objects, define intervals, run `pk.nca()`\ 3. Diagnose → reliability checks (terminal phase, AUCinf extrapolation, visual QC)\ 4. Report → clean summary tables + reproducible plots for communication After the final lesson, you'll reach a **Course Completion** page where you can confirm completion and generate your certificate. ------------------------------------------------------------------------ ## Tools used - R (≥ 4.2 recommended) - RStudio or Positron - tidyverse - PKNCA ------------------------------------------------------------------------ ## Get started Begin with **NCA Foundations** and complete the structural QC checklist before computing any exposure metrics.