SAMBA: A novel method for fast automatic model building in nonlinear mixed-effects models

Abstract

The success of correctly identifying all the components of a nonlinear mixed-effects model is far from straightforward: it is a question of finding the best structural model, determining the type of relationship between covariates and individual parameters, detecting possible correlations between random effects, or also modeling residual errors. We present the Stochastic Approximation for Model Building Algorithm (SAMBA) procedure and show how this algorithm can be used to speed up this process of model building by identifying at each step how best to improve some of the model components. The principle of this algorithm basically consists in "learning something" about the "best model," even when a "poor model" is used to fit the data. A comparison study of the SAMBA procedure with Stepwise Covariate Modeling (SCM) and COnditional Sampling use for Stepwise Approach (COSSAC) show similar performances on several real data examples but with a much reduced computing time. This algorithm is now implemented in Monolix and in the R package Rsmlx.

FIGURE 1

Scheme of the Stochastic Approximation for Model Building Algorithm (SAMBA)

FIGURE 2

Step‐by‐step Stochastic Approximation for Model Building Algorithm (SAMBA) procedure on the remifentanil example with six covariates (SEX, logAGE, logBSA, logHT, logLBM, and logWT) and six model parameters (Cl, Q2, Q3, V1, V2 and V3). For each selection (covariate, correlation, and error model), the three best models in term of corrected Bayesian Information Criteria (BICc) are displayed. Non selected models are in white, newly accepted models are in darker grey, and models which have been already accepted at previous run are in lighter grey