| LoBD | R Documentation |
A portion of an experiment to determine the limit of blank/limit of detection in a biochemical assay.
LoBD
A data frame with 84 observations on the following 9 variables.
poola factor with levels 1 2 3 4
5 6 7 8 9 10 11 12
denoting the 12 pools used in the experiment;
each pool had a different level of drug.
I1L1a numeric vector giving the measured concentration in pmol/L of drug in the assay
I1L2a numeric vector giving the measured concentration in pmol/L of drug in the assay
I2L1a numeric vector giving the measured concentration in pmol/L of drug in the assay
I2L2a numeric vector giving the measured concentration in pmol/L of drug in the assay
I3L1a numeric vector giving the measured concentration in pmol/L of drug in the assay
I3L2a numeric vector giving the measured concentration in pmol/L of drug in the assay
I4L1a numeric vector giving the measured concentration in pmol/L of drug in the assay
I4L2a numeric vector giving the measured concentration in pmol/L of drug in the assay
Important characteristics of a clinical chemistry assay are its limit of blank (LoB), and its limit of detection (LoD). The LoB, conceptually the highest reading likely to be obtained from a zero-concentration sample, is defined operationally by the upper 95% point of readings obtained from samples that do not contain the analyte. The LoD, conceptually the lowest level of analyte that can be reliably determined not to be blank, is defined operationally as true value at which there is a 95% chance of the reading being above the LoB.
These data are from a portion of a LoB/D study of an assay for a drug used to treat certain cancers. Twelve pools were used, four of them blanks of different types, and eight with successively increasing drug levels. The 8 columns of the data set refer to measurements made using different instruments I and reagent lots L.
Used as an illustrative example for Box-Cox type transformations with
negative readings in Hawkins and Weisberg (2015).
For examples of its use, see skewPower.
Hawkins, D. and Weisberg, S. (2015) Combining the Box-Cox Power and Generalized Log Transformations to Accommodate Negative Responses, submitted for publication.
LoBD