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 |                            NONMEM MODEL                            |
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 MEANING: The name of a type of statistical model.
 CONTEXT: NONMEM input/output

 DISCUSSION:

 The acronym NONMEM denotes both the model and the program used to ana-
 lyze data according to such a model.  (See nonmem_program) This  entry
 discusses the NONLINEAR MIXED EFFECTS MODEL (NONMEM).

 Regression  models  structurally link (possibly multivariate) observa-
 tions  (Dependent  variables,  DV)  to  independent  variables  (fixed
 effects,  represented  by  other data items) through a functional form
 (model) quantified by (fixed effect) parameters.   These  model  forms
 may be nonlinear in the parameters.

 Random  effects  may  also  enter the model. In NONMEM they are of two
 types which usually enter the model  at  two  different  levels.   The
 first type, ETA, describes differences between individuals; the second
 type, EPSILON, describes errors between model predictions and observa-
 tions.  (When  all  data  come  from the same individual, or when each
 observation is to be treated as  statistically  independent  from  all
 others, then ETA-type random effects describe all errors, and EPSILON-
 type random effects do not appear).

 Within an individual, ETA may be a vector. Likewise, within one obser-
 vation,  EPSILON   may be a vector, especially if the observations are
 multivariate, as several random effects of each type may be needed  to
 characterize the data adequately.

 The parameter vector THETA contains all fixed effect population param-
 eters.

 The OMEGA matrix, a random effects parameter, is the  variance-covari-
 ance  matrix  of ETA (across individuals); the SIGMA matrix serves the
 same function for EPSILON (its variance covariance is assumed  identi-
 cal across all observations).

 (See eta, eps, theta, effect).  (See parameter, model, omega, sigma).

REFERENCES: Guide I Section C, D, E
REFERENCES: Guide V Section 3, 4


  
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