In nlmixr2/rxode2: Facilities for Simulating from ODE-Based Models

This basic model specification consists of one or more statements optionally terminated by semi-colons ; and optional comments (comments are delimited by # and an end-of-line).

A block of statements is a set of statements delimited by curly braces, { ... }.

Statements can be either assignments, conditional if/else if/else, while loops (can be exited by break), special statements, or printing statements (for debugging/testing).

Assignment statements can be:

• simple assignments, where the left hand is an identifier (i.e., variable). This includes string assignments

• special time-derivative assignments, where the left hand specifies the change of the amount in the corresponding state variable (compartment) with respect to time e.g., d/dt(depot):

• special initial-condition assignments where the left hand specifies the compartment of the initial condition being specified, e.g. depot(0) = 0

• special model event changes including bioavailability (f(depot)=1), lag time (alag(depot)=0), modeled rate (rate(depot)=2) and modeled duration (dur(depot)=2). An example of these model features and the event specification for the modeled infusions the rxode2 data specification is found in rxode2 events vignette.

• special change point syntax, or model times. These model times are specified by mtime(var)=time

• special Jacobian-derivative assignments, where the left hand specifies the change in the compartment ode with respect to a variable. For example, if d/dt(y) = dy, then a Jacobian for this compartment can be specified as df(y)/dy(dy) = 1. There may be some advantage to obtaining the solution or specifying the Jacobian for very stiff ODE systems. However, for the few stiff systems we tried with LSODA, this actually slightly slowed down the solving.

• Special string value declarations which tell what values a string variable will take within a rxode2 solving structure. These values will then cause a factor to be created for this variable on solving the rxode2 model. As such, they are declared in much the same way as R, that is: labels(a) <- c("a1", "a2").

Note that assignment can be done by =, <- or ~.

When assigning with the ~ operator, the simple assignments and time-derivative assignments will not be output. Note that with the rxode2 model functions assignment with ~ can also be overloaded with a residual distribution specification.

Special statements can be:

• Compartment declaration statements, which can change the default dosing compartment and the assumed compartment number(s) as well as add extra compartment names at the end (useful for multiple-endpoint nlmixr models); These are specified by cmt(compartmentName)

• Parameter declaration statements, which can make sure the input parameters are in a certain order instead of ordering the parameters by the order they are parsed. This is useful for keeping the parameter order the same when using 2 different ODE models. These are specified by param(par1, par2,...)

• Variable interpolation statements, which tells the interpolation for specific covariates. These include locf(cov1, cov2, ...) for last observation carried forward, nocb(cov1, cov2, ...) for next observation carried backward, linear(cov1, cov2, ...) for linear interpolation and midpoint(cov1, cov2, ...) for midpoint interpolation.

An example model is shown below:

   # simple assignment
   C2 <- centr/V2

   # time-derivative assignment
   d/dt(centr) <- F*KA*depot - CL*C2 - Q*C2 + Q*C3;

Expressions in assignment and if statements can be numeric or logical.

Numeric expressions can include the following numeric operators +, -, *, /, ^ and those mathematical functions defined in the C or the R math libraries (e.g., fabs, exp, log, sin, abs).

You may also access the R's functions in the R math libraries, like lgammafn for the log gamma function.

The rxode2 syntax is case-sensitive, i.e., ABC is different than abc, Abc, ABc, etc.

Identifiers

Like R, Identifiers (variable names) may consist of one or more alphanumeric, underscore _ or period . characters, but the first character cannot be a digit or underscore _.

Identifiers in a model specification can refer to:

• State variables in the dynamic system (e.g., compartments in a pharmacokinetics model).
• Implied input variable, t (time), tlast (last time point), and podo (oral dose, in the undocumented case of absorption transit models).
• Special constants like pi or R's predefined constants.
• Model parameters (e.g., ka rate of absorption, CL clearance, etc.)
• Others, as created by assignments as part of the model specification; these are referred as LHS (left-hand side) variable.

Currently, the rxode2 modeling language only recognizes system state variables and "parameters", thus, any values that need to be passed from R to the ODE model (e.g., age) should be either passed in the params argument of the integrator function rxSolve() or be in the supplied event data-set.

There are certain variable names that are in the rxode2 event tables. To avoid confusion, the following event table-related items cannot be assigned, or used as a state but can be accessed in the rxode2 code:

• cmt
• dvid
• addl
• ss
• amt
• dur
• rate
• Rprintf
• print
• printf
• id

However the following variables are cannot be used in a model specification:

• evid
• ii

Sometimes rxode2 generates variables that are fed back to rxode2. Similarly, nlmixr2 generates some variables that are used in nlmixr estimation and simulation. These variables start with the either the rx or nlmixr prefixes. To avoid any problems, it is suggested to not use these variables starting with either the rx or nlmixr prefixes.

Logical Operators

Logical operators support the standard R operators ==, != >= <= > and <. Like R these can be in if() or while() statements, ifelse() expressions. Additionally they can be in a standard assignment. For instance, the following is valid:

cov1 = covm*(sexf == "female") + covm*(sexf != "female")

Notice that you can also use character expressions in comparisons. This convenience comes at a cost since character comparisons are slower than numeric expressions. Unlike R, as.numeric or as.integer for these logical statements is not only not needed, but will cause an syntax error if you try to use the function.

Supported functions

All the supported functions in rxode2 can be seen with the rxSupportedFuns().

A brief description of the built-in functions are in the following table:

.rxDocTable(rxSyntaxFunctions, caption="rxode2 supported functions")

Note that lag(cmt) = is equivalent to alag(cmt) = and not the same as = lag(wt)

Reserved keywords

There are a few reserved keywords in a rxode2 model. They are in the following table:

.rxDocTable(rxReservedKeywords, caption="rxode2 supported keywords")

Note that rxFlag will always output 11 or calc_lhs since that is where the final variables are calculated, though you can tweak or test certain parts of rxode2 by using this flag.

Residual functions when using rxode2 functions

In addition to ~ hiding output for certain types of output, it also is used to specify a residual output or endpoint when the input is an rxode2 model function (that includes the residual in the model({}) block).

These specifications are of the form:

var ~ add(add.sd)

Indicating the variable var is the variable that represents the individual central tendencies of the model and it also represents the compartment specification in the data-set.

You can also change the compartment name using the | syntax, that is:

var ~ add(add.sd) | cmt

In the above case var represents the central tendency and cmt represents the compartment or dvid specification.

Transformations

For normal and related distributions, you can apply the transformation on both sides by using some keywords/functions to apply these transformations.

.rxDocTable(dplyr::tribble(~"Transformation", ~"rxode2/nlmixr2 code",
                           "Box-Cox", "+boxCox(lambda)",
                           "Yeo-Johnson", "+yeoJohnson(lambda)",
                           "logit-normal", "+logitNorm(logit.sd, low, hi)",
                           "probit-normal", "+probitNorm(probid.sd, low, hi)",
                           "log-normal","+lnorm(lnorm.sd)"),
            caption="rxode2/nlmixr2 normal/t/cauchy transformations")

By default for the likelihood for all of these transformations is calculated on the untransformed scale.

For bounded variables like logit-normal or probit-normal the low and high values are defaulted to 0 and 1 if missing.

For models where you wish to have a proportional model on one of these transformation you can replace the standard deviation with NA

To allow for more transformations, lnorm(), probitNorm() and logitNorm() can be combined the variance stabilizing yeoJohnson() transformation.

Normal and t-related distributions

For the normal and t-related distributions, we wanted to keep the ability to use skewed distributions additive and proportional in the t/cauchy-space, so these distributions are specified differently in comparison to the other supported distributions within nlmixr2:

.rxDocTable(dplyr::tribble(
  ~"Distribution", ~"How to Add", ~"Example",
  "Normal (log-likelihood)", "+dnorm()", "cc ~ add(add.sd) + dnorm()",
  "T-distribution", "+dt(df)", "cc ~a dd(add.sd) + dt(df)",
  "Cauchy (t with df=1)", "+dcauchy()", "cc ~ add(add.sd) + dcauchy()"),
            caption="rxode2/nlmixr2 normal/t/cauchy log-liklihood specification")

Note that with the normal and t-related distributions nlmixr2 will calculate cwres and npde under the normal assumption to help assess the goodness of the fit of the model.

Also note that the +dnorm() is mostly for testing purposes and will slow down the estimation procedure in nlmixr2. We suggest not adding it (except for explicit testing). When there are multiple endpoint models that mix non-normal and normal distributions, the whole problem is shifted to a log-likelihood method for estimation in nlmixr2.

Notes on additive + proportional models

There are two different ways to specify additive and proportional models, which we will call combined1 and combined2, the same way that Monolix calls the two distributions (to avoid between software differences in naming).

The first, combined1, assumes that the additive and proportional differences are on the standard deviation scale, or:

y=f+(a+b f^c)err

The second, combined2, assumes that the additive and proportional differences are combined on a variance scale:

y=f+[sqrt(a^2+b^2 f^(2c))]err

The default in nlmixr2/rxode2 if not otherwise specified is combined2 since it mirrors how adding 2 normal distributions in statistics will add their variances (not the standard deviations). However, the combined1 can describe the data possibly even better than combined2 so both are possible options in rxode2/nlmixr2.

Distributions of known likelihoods

For residuals that are not related to normal, t-distribution or cauchy, often the residual specification is of the form:

cmt ~ dbeta(alpha, beta)

Where the compartment specification is on the left handed side of the specification.

For generalized likelihood you can specify:

ll(cmt) ~ llik specification

Ordinal likelihoods

Finally, ordinal likelihoods/simulations can be specified in 2 ways. The first is:

err ~ c(p0, p1, p2)

Here err represents the compartment and p0 is the probability of being in a specific category:

| Category | Probability | |----------|-------------| | 1 | p0 | | 2 | p1 | | 3 | p2 | | 4 | 1-p0-p1-p2 |

It is up to the model to ensure that the sum of the p values are less than 1. Additionally you can write an arbitrary number of categories in the ordinal model described above.

It seems a little off that p0 is the probability for category 1 and sometimes scores are in non-whole numbers. This can be modeled as follows:

err ~ c(p0=0, p1=1, p2=2, 3)

Here the numeric categories are specified explicitly, and the probabilities remain the same:

| Category | Probability | |----------|-------------| | 0 | p0 | | 1 | p1 | | 2 | p2 | | 3 | 1-p0-p1-p2 |

General table of supported residual distributions

In general all the that are supported are in the following table (available in rxode2::rxResidualError)

.rxDocTable(rxResidualError, "rxode2/nlmixr2 esidual error specification")

Note on strings in rxode2

Strings are converted to double values inside of rxode2, hence you can refer to them as an integer corresponding to the string value or the string value itself. For covariates these are calculated on the fly based on your data and you should likely not try this, though you should be aware. For strings defined in the model, this is fixed and both could be used.

For example:

if (APGAR == 10 || APGAR == 8 || APGAR == 9) {
    tAPGAR <- "High"
  } else if (APGAR == 1 || APGAR == 2 || APGAR == 3) {
    tAPGAR <- "Low"
  } else if (APGAR == 4 || APGAR == 5 || APGAR == 6 || APGAR == 7) {
    tAPGAR <- "Med"
  } else {
    tAPGAR<- "Med"
  }

Could also be replaced by:

if (APGAR == 10 || APGAR == 8 || APGAR == 9) {
    tAPGAR <- "High"
  } else if (APGAR == 1 || APGAR == 2 || APGAR == 3) {
    tAPGAR <- "Low"
  } else if (APGAR == 4 || APGAR == 5 || APGAR == 6 || APGAR == 7) {
    tAPGAR <- "Med"
  } else {
    tAPGAR<- 3
  }

Since "Med" is already defined

If you wanted you can pre-declare what levels it has (and the order) to give you better control of this:

levels(tAPGAR) <- c("Med", "Low", "High")
if (APGAR == 10 || APGAR == 8 || APGAR == 9) {
    tAPGAR <- 3
  } else if (APGAR == 1 || APGAR == 2 || APGAR == 3) {
    tAPGAR <- 2
  } else if (APGAR == 4 || APGAR == 5 || APGAR == 6 || APGAR == 7) {
    tAPGAR <- 1
  } else {
    tAPGAR<- 1
  }

You can see that the number changed since the declaration change the numbers in each variable for tAPGAR. These levels() statements need to be declared before the variable occurs to ensure the numbering is consistent with what is declared.

nlmixr2/rxode2 documentation built on Jan. 11, 2025, 8:48 a.m.