Related Experiment Video
Updated: Jun 21, 2026

Reproducibility and Harmonization in Research Using Biological Standards: The Example of Platelet Agonist Collagen-Related Peptide
Published on: August 4, 2023
Statistical considerations for assessment of bioanalytical incurred sample reproducibility
1Preclinical and Research Biostatistics, Sanofi-Aventis, Bridgewater, NJ 08807, USA. david.hoffman@sanofi-aventis.com
Abstract:
Bioanalytical method validation is generally conducted using standards and quality control (QC) samples which are prepared to be as similar as possible to the study samples (incurred samples) which are to be analyzed. However, there are a variety of circumstances in which the performance of a bioanalytical method when using standards and QCs may not adequately approximate that when using incurred samples. The objective of incurred sample reproducibility (ISR) testing is to demonstrate that a bioanalytical method will produce consistent results from study samples when re-analyzed on a separate occasion. The Third American Association of Pharmaceutical Scientists (AAPS)/Food and Drug Administration (FDA) Bioanalytical Workshop and subsequent workshops have led to widespread industry adoption of the so-called "4-6-20" rule for assessing incurred sample reproducibility (i.e. at least 66.7% of the re-analyzed incurred samples must agree within +/-20% of the original result), though the performance of this rule in the context of ISR testing has not yet been evaluated. This paper evaluates the performance of the 4-6-20 rule, provides general recommendations and guidance on appropriate experimental designs and sample sizes for ISR testing, discusses the impact of repeated ISR testing across multiple clinical studies, and proposes alternative acceptance criteria for ISR testing based on formal statistical methodology.
More Related Videos
08:52Improving Reproducibility to Meet Minimal Information for Studies of Extracellular Vesicles 2018 Guidelines in Nanoparticle Tracking Analysis
Published on: November 17, 2021
05:07Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
Published on: November 7, 2025
Related Concept Videos
Bioequivalence Data: Statistical Interpretation
Statistical Analysis: Overview
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Contaminants and Errors
Another key consideration is determining the appropriate number of samples required to...
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Data Validation
Key parameters for method validation include:
Sampling Methods: Overview
In analytical chemistry, the choice of sampling...