LabStats

  1. Introduction The assessment of assay accuracy is critical in analytical chemistry, particularly when facing the challenge of variable matrix effects during quantitative measurements. The phenomenon commonly referred to as the matrix effect occurs when components of a sample other than the analyte influence the response of the assay. This effect can lead to significant discrepancies in the measurement of target analytes, particularly at intermediate and high dilution levels, where the concentration of the analyte is considerably lower and increasingly susceptible to interference. Matrix effects can arise from various sources, including co-eluting substances, ion suppression or enhancement, and physical properties such as viscosity and pH. These factors may alter the ionization or detection efficiency of the target analyte, impacting accuracy and precision (Tan et al., 2014; Rej and Norton-Wenzel, 2015; Rao, 2018). The challenge becomes more pronounced when analyzing samples ...

  R function conf_int_Accuracy <- function(GMTobs, s, nDays = 2, nAnalysts = 2, nPlates = 2, nReplicates = 3, FoldDilution = 2, Threshold = 1, alpha = 0.05) {   # INPUT:   # GMTobs: vector of observed geometric means at each fold dilution   # s: vector of the standard deviations of the log-transformed replicates   # nDays: number of experimental days (default = 2)   # nAnalysts: number of analysts performing the tests in validation (default = 2)   # nPlates: number of plates used per analyst per day (default = 2)   # nReplicates: number of measurements that each analyst performs per plate per day   # FoldDilution: step of the serial dilution (default = 2)   # Threshold: critical threshold of the log-difference between the observed and the true mean (default=1)   # alpha: significance level (default=0.05)   #   # OUTPUT:   # vector of Relative Accuracy calculated at each fold dilution and its confidence interval. In...

A bioassay validation is a set of procedures that ensure the precision and accuracy of the test results. Precision refers to the proximity of results to each other, and accuracy is the proximity of measurements results to the true value. Here we want to focus on the accuracy and the building of a confidence interval around it.   Let's design the validation experiments with three possible sources of variability, day, analyst, and plate having nd levels, na levels, and np levels, respectively. Each analyst performs nm measurements per plate per day on each sample. The samples will be serially diluted by a factor of two, resulting in dl dilution levels from 1/1 to 1/(2^(dl-1)). So, the total number of measurements, N, for each dilution level will N=nd*na*np*nm.   To calculate the accuracy , A, we can follow the FDA recommendation [1] of comparing the mean of the measured values with the "true" value, and define accuracy as that is the ratio between the observed geometric m...