Reserved keywords
Some keywords are used in Mlxtran for dedicated purposes. Thus these keywords are not available for re-definition or overloading.
Time-related keyword
The reserved keyword for the time is t. It corresponds to the time defined in the TIME column in Monolix, and the times defined in the treatment, observation and regressor elements in Simulx.
Example:
The keyword time can be used to define a time-varying clearance for instance:
[LONGITUDINAL]
input = {V, CLini, CLss, T12}
EQUATION:
Cl = CLss - (CLss-CLini)*exp(-t/T12)
Cc = pkmodel(V,Cl)Administration-related keywords
Information related to the doses defined in the data set or in the Simulx treatment elements are available in the structural model via several keyword. They are piece-wise constant functions which value is updated each time there is a dose. Before the first dose, their value is zero.
The value of the keyword are directly related to the design, they are unaffected by the absorption/depot macros arguments such as Tlag or p. Note that doses are not discriminated according to their administration types.
tDose: holds the time of the last administered dose.
amtDose: holds the amount of the last administered dose.
inftDose: holds the infusion duration of the last administered dose.
Example:
The keyword amtDose can be used to define a dose-dependent absorption for instance:
[LONGITUDINAL]
input = {ka, V, Cl, D50}
EQUATION:
F = D50/(amtDose+D50)
Cc = pkmodel(ka, V, Cl, p=F)Full list of keywords
Here is the full list of Mlxtran keywords to use in Simulx, except
the keywords used for distribution definition (that are listed here)
the keywords used for classical mathematical definition (that are listed here)
Name | Meaning | where can it be used ? | link |
absorption | Macro used for defining the absorption process in a longitudinal model | In subsection [LONGITUDINAL] after PK: | |
adm | Type of administration (type and adm are equivalent) | In argument of the following macros: absorption, depot, iv and oral | |
amount | Name of the variable defined as the dose amount within the compartment | In argument of the following macros: compartment and peripheral | |
amtDose | Amount of the last administered dose. This is a step function and is null before the first dose. | In the longitudinal model, in subsection [LONGITUDINAL], in a block EQUATION: | current page |
bsmm | Mixture of continuous observations. It is a mixture between subjects model mixtures (BSMM). It assumes no inter individual variabilities for the proportions of each group (ı.e. the probabilities to belong to the different groups). It is relevant only for Monolix. | In the longitudinal model, in subsection [LONGITUDINAL], in a block EQUATION: | |
categorical | Type of observation model | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: after a type= | |
categories | List of the available ordered categories for a categorical observation. They are usually represented by increasing successive integers. | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is a category | |
cmt | Label of the compartment | In argument of the following macros: absorption, compartment, depot, effect, elimination, iv, oral | |
coefficient | Coefficient under consideration for a distribution definition depending on covariates | In any distribution definition in a block DEFINITION: | |
combined1 | Combined error model | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is continuous | |
combined1c | Corresponds to a combined error model | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is continuous | |
combined2 | Combined error model | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is continuous | |
combined2c | Combined error model | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is continuous | |
compartment | Macro used for defining a compartment | In subsection [LONGITUDINAL] after PK: | |
concentration | Name of the variable defined as the concentration within the compartment | In argument of the following macros: compartment, effect, peripheral | |
constant | Constant error model | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is continuous | |
continuous | Type of observation model. | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: after a type= | |
count | Type of observation model. | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: after a type= | |
covariate | Covariate under consideration for a distribution definition | In any distribution definition in a block DEFINITION: | |
delay | It corresponds to delay function to define DDE. | In the longitudinal model, in subsection [LONGITUDINAL], in a block EQUATION: | |
dependence | It corresponds to the label used to defined that an observation variable for ordered categorical data modelled as a Markov chain. | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is categorical | |
depot | It corresponds to an absorption targeting a depot. A component of an ODE system is defined as the target depot for the doses. | In subsection [LONGITUDINAL] after PK: | |
effect | This macro defines an effect compartment. It is linked to a simple compartment and used through the variable for its effect concentration. | In subsection [LONGITUDINAL] after PK: | |
elimination | This macro defines an elimination process. | In subsection [LONGITUDINAL] after PK: | |
else | It is part of a conditional statement. A conditional statment can be built by combining the keywords if, elseif, else and end. | In a block EQUATION: | |
elseif | It is part of a conditional statement. A conditional statment can be built by combining the keywords if, elseif, else and end. | In a block EQUATION: | |
end | It is part of a conditional statement. A conditional statment can be built by combining the keywords if, elseif, else and end. | In a block EQUATION: | |
errorModel | It corresponds to the label to define an error model for a continuous observation model | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is continuous | |
event | Type of observation model. | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: after a type= | |
eventType | Label to define the event type for an event observation model. It allows to define if the exact time of the events or censored per interval is observed. | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is an event | |
from | Label of the source compartment for the transfer. | In argument of the following macro: transfer | |
hazard | Hazard function | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is an event | |
if | It is part of a conditional statement. A conditional statment can be built by combining the keywords if, elseif, else and end. | In a block EQUATION: | |
inftDose | The keyword inftDose defines the infusion time of the last administered dose. This is a step function. The rate of the final absorption can be different from the rate of the administration process. It is null before the first dose. | In the longitudinal model, in subsection [LONGITUDINAL], in a block EQUATION: | current page |
input | It corresponds to the definition of the inputs in a subsection | In subsections [LONGITUDINAL], [INDIVIDUAL], and [COVARIATE] | |
intervalCensored | Argument of eventType. | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is an event | |
intervalLength | Label to length of censoring intervals in an event observation model. It is useful for simulation only, | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is an event | |
iv | The macro iv defines an absorption for intravenous doses. Doses without an administration rate or infusion time are instantaneously absorbed within the associated compartment, as an IV bolus. | In subsection [LONGITUDINAL] after PK: | |
linear | It is an argument of odeType= and allows to define the dynamical system as linear | In the longitudinal model, in subsection [LONGITUDINAL], in a block EQUATION: | |
Markov | It corresponds to the dependence of a categorical observation model. It allows to define that the observation model is based on a Markov chain | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: after a dependence= | |
maxEventNumber | Maximum number of events in an event observation model. It is useful for simulation only | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is an event | |
mean | Mean of the associated normal distribution. | In any distribution definition in a block DEFINITION: | |
nonStiff | It is an argument of odeType= and allows to define the dynamical system is nonStiff and thus yields to an adapted numerical scheme for the resolution | In the longitudinal model, in subsection [LONGITUDINAL], in a block EQUATION: | |
odeType | It is the label that allows to define the type of ODE. | In the longitudinal model, in subsection [LONGITUDINAL], in a block EQUATION: | |
oral | It is a macro used for defining the absorption process in a longitudinal model | In subsection [LONGITUDINAL] after PK: | |
output | It allows to define the outputs of a structural model | In the longitudinal model, in subsection [LONGITUDINAL], in a block OUTPUT: | |
P | The majuscule P corresponds to the definition of a probability | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: | |
p | It corresponds to the bio availability in an absorption process | In argument of the following macro: absorption, depot, iv, and oral | |
peripheral | The macro peripheral defines a peripheral compartment. It is equivalent to a simple compartment with two transfers of amount towards and from another compartment. | In subsection [LONGITUDINAL] after PK: | |
PK | It defines where the macros are defined | In subsection [LONGITUDINAL] | |
pkmodel | It defines the macro pkmodel | In subsection [LONGITUDINAL], in a block PK: or in a block EQUATION: | |
prediction | It corresponds to the name of the base prediction variable | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is continuous | |
proportional | Corresponds to proportional error model | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is continuous | |
proportionalc | Corresponds to proportional error model | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is continuous | |
regressor | It declares the input regression values. | In the longitudinal model, in subsection [LONGITUDINAL], after the input definition | |
rightCensoringTime | Right censoring time of events. It is useful for simulation only | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: when the type of observation is an event | |
sd | Standard deviation of the associated normal distribution (standardDeviation and sd are synonymous keywords) | In any distribution definition in a block DEFINITION: | |
standardDeviation | Standard deviation of the associated normal distribution (standardDeviation and sd are synonymous keywords) | In any distribution definition in a block DEFINITION: | |
stiff | It is an argument of odeType= and allows to define the dynamical system is nonStiff and thus yields to an adapted numerical scheme for the resolution | In the longitudinal model, in subsection [LONGITUDINAL], in a block EQUATION: | |
t | time | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: | Current page |
t_0 | Initialization time for the equations | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: | |
t0 | Initialization time for the equations | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: | |
table | This keywords declares the variables to record in tables. | In the longitudinal model, in subsection [LONGITUDINAL], in a block OUTPUT: | |
target | Name of the component of an ODE system that is shifted by the absorption. | In argument of the following macro: depot | |
tDose | The keyword tDose defines the time of the last administered dose. This is a step function. Its value is unaffected by any lag time Tlag. Indeed, a lag time targets the dose absorption. It is null before the first dose. | In the longitudinal model, in subsection [LONGITUDINAL], in a block EQUATION: | Current page |
to | Label of the target compartment for the transfer | In argument of the following macro: transfer | |
transfer | The macro transfer defines a transfer of amount from a first compartment to a second one. | In subsection [LONGITUDINAL] after PK: | |
transitionRate | Transition rate departing from a given category to another one. It is used in the definition of a categorical observation model. | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: | |
type | It allows the type of observation model. It can be continuous, count, categorical, or event. | In the longitudinal model, in subsection [LONGITUDINAL], in a block DEFINITION: | |
typical | It corresponds to the typical value during a distribution definition | In any distribution definition in a block DEFINITION: | |
use | It allows to define the regressor in the longitudinal subsection. Regressors are defined as inputs at the beginning and thus are specified as regressors using use= | In the longitudinal model, in subsection [LONGITUDINAL], after the input definition | |
var | Variance of the associated normal distribution (variance and var are synonymous keywords) | In any distribution definition in a block DEFINITION: | |
variance | Variance of the associated normal distribution (variance and var are synonymous keywords) | In any distribution definition in a block DEFINITION: | |
volume | It corresponds to the name of predefined variable to use as the volume of the compartment | In argument of the following macros: compartment, effect, peripheral | |
wsmm | Mixture of continuous observations. It is a mixture within subjects. | In the longitudinal model, in subsection [LONGITUDINAL], in a block EQUATION: |
