Statistics quantifying random error

The quantifiable components of sampling error are the natural variability within a sample itself and the variability between sample populations that may be randomly selected from the same sampled area. Both of these errors can be evaluated quantitatively in the form of statistical variance and controlled to some extent through the application of appropriate sampling designs. 

The statistical submodel characterizes the pharmacokinetic variability of the mAb and includes the influence of random - that is, not quantifiable or uncontrollable factors. If multiple doses of the antibody are administered, then three hierarchical components of random variability can be defined inter-individual variability inter-occasional variability and residual variability. Inter-individual variability quantifies the unexplained difference of the pharmacokinetic parameters between individuals. If data are available from different administrations to one patient, inter-occasional variability can be estimated as random variation of a pharmacokinetic parameter (for example, CL) between the different administration periods. For mAbs, this was first introduced in sibrotuzumab data analysis. In order to individualize therapy based on concentration measurements, it is a prerequisite that inter-occasional variability (variability within one patient at multiple administrations) is lower than inter-individual variability (variability between patients). Residual variability accounts for model misspecification, errors in documentation of the dosage regimen or blood sampling time points, assay variability, and other sources of error. 

At this point, it is worth emphasizing the difference between the terms "standard error" and "standard deviation," which, despite the same initial word, represent very different aspects of a data set. Standard error is a measure of how certain we are that the sample mean represents the population mean. Standard deviation is a measure of the dispersion of the original random variable. There is a standard error associated with any statistical estimator, including a sample proportion, the difference in two means, the difference in two proportions, and the ratio of two proportions. When presented with the term "standard error" in these applications the concept is the same. The standard error quantifies the extent to which an estimator varies over samples of the same size. As the sample size increases (for the same standard deviation) there... 

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