ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Confidence and Prediction Intervals in Linear Mixed Models, Trueness and Accuracy (Trueness + Precision) in Assay Qualification

3 September 2018, 15:50 – 16:10


Submitted by
Bernard Francq
Bernard Francq (GSK, CMC StatS), Dan Lin (GSK, CMC StatS), Walter Hoyer (GSK, CMC StatS)
During development of a vaccine, different analytical methods for determining the antigen concentration or the (relative) potency in the produced vaccine batches need to be developed. In assay development, a linear mixed model across all samples is applied to estimate the variance components to evaluate the precision of the method (repeatability and intermediate precision). Trueness is the systematic deviation (bias) between the reference value and the measured concentration. Accuracy is the closeness between the reference value and an individual test value, which takes into account the systematic error (trueness) and random error (precision). Trueness is therefore assessed by using Confidence Intervals (CIs) at each measured concentration while accuracy is evaluated by using Prediction Intervals (PIs).

The advantages and convergence of these intervals will be discussed in the framework of linear mixed models with different sample sizes. The literature about PI in mixed models is scarce as often the methodology is developed for specific design (one random factor) by using explicit analytical formulae, which are not appropriate for unbalanced or more complex designs.

We propose a PI formula that is generalizable under a wide variety of designs with a variance component structure (random, nested, crossed, balanced or unbalanced designs). The methodology is based on the Hessian matrix which leads to a straightforward generalized solution. Performance of our methodology will be evaluated by means of simulations and application to a case study in assay qualification.
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