Clinical Biochemistry of Domestic Animals (Sixth Edition) - UMK ...

Chapter 1
Concepts of Normality in
Clinical Biochemistry
Thomas B. Farver
Department of Population Health and Reproduction
School of Veterinary Medicine
University of California, Davis
Davis, California
I. POPULATIONS AND THEIR DISTRIBUTIONS
II. REFERENCE INTERVAL DETERMINATION
AND USE
A. Gaussian Distribution
B. Evaluating Probabilities Using a Gaussian Distribution
C. Conventional Method for Determining Reference
Intervals
D. Methods for Determining Reference Intervals for Analytes
Not Having the Gaussian Distribution
E. Sensitivity and Specificity of a Decision Based on a
Reference Interval
F. Predictive Value of a Decision Based on a
Reference Interval
G. ROC Analysis
III. ACCURACY IN ANALYTE MEASUREMENTS
IV. PRECISION IN ANALYTE MEASUREMENTS
V. INFERENCE FROM SAMPLES
A. Simple Random Sampling
B. Descriptive Statistics
C. Sampling Distributions
D. Constructing an Interval Estimate of the
Population Mean, μ
E. Comparing the Mean Response of
Two Populations
F. Comparing the Mean Response of Three or
More Populations Using Independent Samples
G. Efficiency in Experimental Designs
H. Nesting Designs
REFERENCES
I . POPULATIONS AND THEIR
DISTRIBUTIONS
A population is a collection of individuals or items having
something in common. For example, one could say that the
population of healthy dogs consists of all dogs that are free
of disease. Whether a given dog belongs to the population
of healthy dogs depends on someone’s ability to determine
if the dog is or is not free of disease. Populations may be
finite or infinite in size.
A population can be described by quantifiable characteristics
frequently called observations or measures . If it
were possible to record an observation for all members in
the population, one most likely would demonstrate that not
all members of the population have the same value for the
given observation. This reflects the inherent variability in
populations. For a given measure, the list of possible values
that can be assumed with the corresponding frequency
with which each value appears in the population relative to
the total number of elements in the population is referred
to as the distribution of the measure or observation in the
population. Distributions can be displayed in tabular or
graphical form or summarized in mathematical expressions.
Distributions are classified as discrete distributions or continuous
distributions on the basis of values that the measure
can assume. Measures with a continuous distribution
can assume essentially an infinite number of values over
some defined range of values, whereas those with a discrete
distribution can assume only a relatively few values
within a given range, such as only integer values.
Each population distribution can be described by quantities
known as parameters . One set of parameters of a
population distribution provides information on the center
of the distribution or value(s) of the measure that seems
to be assumed by a preponderance of the elements in the
population. The mean, median, and mode are three members
of the class of parameters describing the center of the
distribution. Another class of parameters provides information
on the spread of the distribution. Spread of the distribution
has to do with whether most of the values that are
assumed in the population are close to the center of the
distribution or whether a wider range of values is assumed.
The standard deviation, variance, and range are examples
Clinical Biochemistry of Domestic Animals, 6th Edition 1
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