covariateSearch {Rsmlx}

Covariate model building

Description

Automatic search of the best covariate model. Two methods for covariate model building are proposed

• SCM: stepwise covariate modeling method In the forward selection, at each step, each of the remaining (i.e not yet included) parameter-covariate relationships are added to the model in an univariate model (one model per relationship), and run. Among all models, the model that improves some criteria (LRT, BIC or AIC) most is selected and taken forward to the next step. During backward elimination, parameter-covariate relationships are removed in an univariate manner.

• COSSAC: COnditional Sampling for Stepwise Approach based on Correlation tests method COSSAC makes use of the information contained in the base model run to choose which covariate to try first (instead of trying all covariates "blindly" as in SCM). Indeed, the correlation between the individual parameters (or random effects) and the covariates hints at possibly relevant parameter-covariate relationships. If the EBEs (empirical Bayes estimates) are used, shrinkage may bias the result. COSSAC instead uses samples from the a posteriori conditional distribution (available as "conditional distribution" task in MonolixSuite2018) to calculate the correlation between the random effects and covariates. A p-value can be derived using the Pearson's correlation test for continuous covariate and ANOVA for categorical covariate. The p-values are used to sort all the random effect-covariate relationships. Relationships with the lowest p-value are added first, run and confirmed using a likelihood ratio test, AIC or BIC criteria.

Usage

covariateSearch(
  project,
  final.project = NULL,
  method = NULL,
  covToTest = NULL,
  covToTransform = NULL,
  paramToUse = NULL,
  testRelations = NULL,
  settings = NULL
)

Arguments

project

a Monolix project

final.project

[optional] string corresponding to the final Monolix project (default: 'runFinal.mlxtran' in covariate search output folder)

method

[optional] string correspondig to the method. It can be 'COSSAC' or 'SCM'. By default, COSSAC' is used.

covToTest

[optional] vector of covariates to test. Cannot be used if testRelations is defined. By default, all covariates are tested.

covToTransform

[optional] vector of covariates to transform. The transformation consists in a log transform of the covariate with centering by the mean value (ex: WT is transformed into log(WT/mean) with mean the mean WT value over the individuals of the data set). Both the transformed and untransformed covariate are tested by the algorithm. By default, no covariate is transformed. Note: adding a non-transformed covariate on a lognormally distributed parameter results in an exponential relationship: log(V) = log(Vpop) + beta*WT + eta <=> V = Vpop * exp(beta*WT) * exp(eta) adding a log-transformed covariate on a lognormally distributed parameter results in a power law relationship: log(V) = log(Vpop) + beta*log(WT/70) + eta <=> V = Vpop * (WT/70)^beta * exp(eta)

paramToUse

[optional] vector of parameters which may be function of covariates. Cannot be used if testRelations is defined. By default, all parameters are tested.

testRelations

[optional] list of parameter-covariate relationships to test, ex: list(V=c("WT","SEX"),Cl=c("CRCL")). Cannot be used if covToTest or paramToUse is defined. By default, all parameter-covariate relationships are tested.

settings

[optional] list of settings for the covariate search:

• pInclusion [positive double] threshold on the LRT p-value to accept the model with the added parameter-covariate relationship during forward selection (default = .1). Only used if criteria="LRT".

• pElimination [positive double] threshold on the LRT p-value to accept the model without the removed parameter-covariate relationship during the backward elimination (default = .05). Only used if criteria="LRT".

• criteriaThreshold [positive double] the threshold on the AIC or BIC difference to accept the model with added/removed parameter-covariate relationship (default = 0). Only used if criteria="BIC" or "AIC.

• linearization [boolean] whether the computation of the likelihood is based on a linearization of the model (default = FALSE).

• criteria [string] criteria to optimize. It can be the "BIC", "AIC", or "LRT" (default="LRT").

• direction [string] method for covariate search. It can be "backward", "forward", or "both" (default = "both").

• updateInit [boolean] whether to update or not the initial parameters using the estimates of the parent model (default = FALSE)

• saveRun [boolean] whether to save or not each run (default = TRUE)

Examples

# RsmlxDemo1.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin 
# using a PK model with parameters ka, V, Cl.

# In this example, three covariates (wt, age, sex) are available with the data
# covariatesearch will compute the best covariate model, in term of BIC, 
# for the three PK parameters using the three covariates. 
r1 <- covariateSearch(project="RsmlxDemo1.mlxtran")
  
# Instead of using the COSSAC method, we can use the SCM method:
r2 <- covariateSearch(project="RsmlxDemo1.mlxtran", method = 'SCM')

# Here, the covariate model is built using age and wt only, for V and Cl only:
r3 <- covariateSearch(project    = "RsmlxDemo1.mlxtran", 
                      paramToUse = c("V","Cl"), 
                      covToTest  = c("age","wt"))

# See http://monolix.lixoft.com/rsmlx/covariatesearch/ for detailed examples of covariatesearch
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation