Independent-model diagnostics for a priori identification and interpretation of outliers from a full PK (PD) dataset. Nabil SEMMAR 1,3 , Saik URIEN 2,4 , Bernard BRUGUEROLLE 1 and Nicolas SIMON 1. 3: ISSBAT, High Institute of Applied Biological . Sciences of Tunis, Tunisia.
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Independent-model diagnostics for a priori identification and interpretation of outliers from a full PK (PD) dataset
Nabil SEMMAR1,3, Saik URIEN2,4, Bernard BRUGUEROLLE1 and Nicolas SIMON1
3: ISSBAT, High Institute of Applied Biological . Sciences of Tunis, Tunisia
4: INSERM, Paris, France
1: Laboratory of Clinical Pharmacology, EA3784, . Medical School of Marseilles, Marseilles, France
2: Pharmacology Department, Centre René Huguenin, . Saint-Cloud, France
Different kinds of outliers
Multivariate outlier diagnostics and corresponding metric distances
A priori identification of outliers requires distance computations by reference to an average or a middle state: Higher a distance value is, more the corresponding case tends to be outlier compared with all the other individual cases. There are three kinds of outliers:
1) Outliers due to high absolute values can be detected from euclidean distance, computed by multivariate Andrews curves.
2) Outliers due to high relative values or significant shift can be detected from the chi-square distance, computed by correspondence analysis method.
1) Points that are far because of high absolute coordinates;
2) Points occupying neighbor spaces but non shared by other points;
3) Outliers tending to stretch the global direction of the cloud of points can be detected from Mahalanobis distance, computed as jackknifed robust distance.
3) Points oriented along atypical directions in the multivariate space.
Graphical representations of factorial coordinates
Decomposition of residual matrix into linear combinations based on eigenvectors computation
How much observed data are far from independence states?
Correspondence analysis: Principle and illustration
(R) = (T) – (T0)
Standard and Jackknifed Mahalanobis distance: Principle and illustration
Identification and interpretation of outliers based on ² criterion
Andrews curves: Principle and illustration
Graphical representation of Andrews function values
Recalculation of JMD without different time variables to identify outlier concentrations
Outlier diagnostics from a full PK dataset: Capecitabin administrated orally two times in 40 patients
Jackknifed Mahalanobis distance (JMD)
Patients with relatively high concentrations at some times compared with all the values of the same times in the whole population
Plasma samplings at 0.5, 1, 2, 3, 4, 6h after each dosing
Is there a link between a priori outlier diagnostics and a posteriori PK modeling?
What are the outlier concentrations?
Outlier PK profiles: High absolute concentrations
Without times 0.5, 1, 24.5 and 25h
The outlier curves became non-outliers after removing of outlier
Without time 2h
Without time 28h
More detected was an outlier concentration a priori, higher was its residual (NPDE) after PK modeling