Partitioning of nongaussian-distributed biochemical reference data into subgroups.
BACKGROUND
The aim of this study was to develop new methods for partitioning biochemical reference data, covering in particular nongaussian distributions.


METHODS
We recently proposed partitioning criteria for gaussian distributions. These criteria relate to proportions of the subgroups outside each of the reference limits of the combined distribution (proportion criteria) and to distances between the subgroup distributions as correlates of these proportions (distance criteria). However, distance criteria do not seem to be ideal for nongaussian distributions because a generally valid relationship between proportions and distances cannot be established for these.


RESULTS
Proportion criteria appear preferable to distance criteria for two additional reasons: (a) The prevalences of the subgroup populations may have a considerable effect on stratification, but these are hard to account for by using distance criteria. Two methods to handle prevalences are described, the root method and the multiplication method. (b) Tied reference values, another complication of the partitioning problem, could also be hard to take care of using distance criteria. Some solutions to the problems caused by tied reference values are suggested.


CONCLUSIONS
Partitioning of biochemical reference data should preferably be based on proportion criteria; this is particularly true for nongaussian distributions. Both of the described complications of the partitioning problem, the prevalences of the subgroups and tied reference values, are hard to deal with using distance criteria, but the proposed methods make it possible to account for them when proportion criteria are applied.
Lahti
Ari
A
Department of Clinical Chemistry, Rikshospitalet University Hospital of Oslo, Oslo, Norway. ari.lahti@rikshospitalet.no
Petersen
Per Hyltoft
PH
Boyd
James C
JC
Rustad
Pål
P
Laake
Petter
P
Solberg
Helge Erik
HE
D019411:Clinical Laboratory Techniques / Q000706:statistics & numerical data*; D006801:Humans; D012016:Reference Values; D016008:Statistical Distributions