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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
 analyze  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-
 covariance matrix of ETA (across individuals); the SIGMA matrix serves
 the same function for EPSILON  (its  variance  covariance  is  assumed
 identical 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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