The function get_model_list() fits four types of linear regression
models that are often used for the assessment of stability data, e.g. for
the estimation of the shelf life or retest period.
get_model_list(data, response_vbl, time_vbl, batch_vbl)A list of three elements is returned, containing the following elements:
A list of four elements named cics, dics,
dids.pmse and dids. The first three elements contain the
‘lm’ objects of the “common intercept / common slope”
(cics), “different intercept / common slope” (dics)
and “different intercept / different slope” (dids) models.
The fourth element is a list of the ‘lm’ objects that is
obtained from fitting a regression model to the data of each level of the
categorical variable separately. The cics, dics and
dids.pmse elements are NA if data of only a single batch
is available.
A numeric named vector of the Akaike Information Criterion (AIC)
values of the cics, dics and dids.pmse models.
A numeric named vector of the Bayesian Information Criterion (BIC)
values of each of the cics, dics and dids.pmse
models.
A data frame with the columns specified by response_vbl,
time_vbl and batch_vbl.
A character string that specifies the response variable
name that must be a column of data.
A character string that specifies the time variable name
that must be a column of data.
A character string that specifies the column in data
with the grouping information (i.e. a factorial variable) for the
differentiation of the observations of the various batches.
The function get_model_list() expects a data frame with
a response variable, a time variable and a categorical variable which
usually has factor levels of multiple batches of a drug product that was
assessed over a certain period of time with respect to the time-dependent
behaviour of characteristic parameters. Using these results, the function
fits
a common intercept / common slope model (cics),
a different intercept / common slope model (dics) or
a different intercept / different slope model with pooled mean square error (dids.pmse) and
a different intercept / different slope model (dids) in which individual models are fitted to each level of the categorical variable.
If the categorical variable has only a single factor level, then the first
three models are NA and only a single regression model is fitted.