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MEANING: A data analysis unit
CONTEXT: NONMEM input/output
DISCUSSION:
Data to be analyzed with NONMEM are often population data, by which is
meant multiple data arising from each of a number of individual units.
Individuals are typically persons, but they may be any other appropri-
ate units, such as families, geographic localities, etc. Data are
regarded as being statistically independent from unit to unit.
With NONMEM, there are two nested levels of random effects, The first
level applies to individuals; different individuals are regarded as
having different realizations of level-one random effects. A second
level of random effects applies to the observations from each individ-
ual; different (univariate) observations are regarded as having dif-
ferent realizations of level-two randoms effects, but the same real-
ization of level-one random effects.
The data from an individual is given in the data set by a contiguous
group of data records, with one observation on each data record, and
all data records having the same identification (ID) data item. This
group of data records is called the individual record, or level-one
(L1) record. (Do not confuse a data record, which is one record in a |
data file, with an individual record, which is a group of data |
records.)
Data to be analyzed may be single-subject data. These are data that
appear to require at most one level of random effects. (In fact,
there are population data which require only one level of the two NON-
MEM levels of random effects, along with a second level of random
effects which may be expressed in a way that is transparent to NONMEM.
This type of situation is communicated in such a way that NONMEM does
not mistake these data for single-subject data.) Such data may actu-
ally arise physically from different individual units, or individuals.
Indeed, when they do, they may even be comprised of multiple data from
different units, e.g. pairs of plasma and saliva concentrations
obtained at the same time point, each from a number of different sub-
jects. However, if as with this example, only one level of random
effects is needed, these data are nonetheless considered to be single-
subject data. The data are regarded as being statistically indepen-
dent from unit to unit. When single-subject data indeed arise physi-
cally from the same subject, the data can also be grouped into indi-
vidual units such that the data are regarded as being statistically
independent from unit to unit. These units are also called "individu-
als". As an example, there may be pairs of plasma and saliva concen-
trations from the same subject. More precisely, NM-TRAN recognizes
population data to be data that do not qualify as single-subject data.
NONMEM counts the number of distinct individuals in the data set, and
reports this count as a check.
E.g.,
TOT. NO. OF INDIVIDUALS: 166
NONMEM 7 also reports how the data is to be analyzed: |
ANALYSIS TYPE: SINGLE-SUBJECT |
ANALYSIS TYPE: POPULATION |
ANALYSIS TYPE: POPULATION WITH UNCONSTRAINTED ETAS |
Population analysis with unconstrained etas is new with NONMEM 7, and |
can be used to analyze a population data set as separate individuals. |
OMEGA diagonal values are fixed to a special value 1.0E+06. |
See Guide Introduction_7 "Analyzing Single-subject data as Population |
with Unconstrained etas". |
(See recid2.exa).
REFERENCES: Guide I Section E
REFERENCES: Guide Section Introduction_7
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