Dataset Information

Cross-validation for nonlinear mixed effects models.

ABSTRACT: Cross-validation is frequently used for model selection in a variety of applications. However, it is difficult to apply cross-validation to mixed effects models (including nonlinear mixed effects models or NLME models) due to the fact that cross-validation requires "out-of-sample" predictions of the outcome variable, which cannot be easily calculated when random effects are present. We describe two novel variants of cross-validation that can be applied to NLME models. One variant, where out-of-sample predictions are based on post hoc estimates of the random effects, can be used to select the overall structural model. Another variant, where cross-validation seeks to minimize the estimated random effects rather than the estimated residuals, can be used to select covariates to include in the model. We show that these methods produce accurate results in a variety of simulated data sets and apply them to two publicly available population pharmacokinetic data sets.

SUBMITTER: Colby E

PROVIDER: S-EPMC3668859 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Publications

Cross-validation for nonlinear mixed effects models.

Colby Emily E Bair Eric E

Journal of pharmacokinetics and pharmacodynamics 20130327 2

Cross-validation is frequently used for model selection in a variety of applications. However, it is difficult to apply cross-validation to mixed effects models (including nonlinear mixed effects models or NLME models) due to the fact that cross-validation requires "out-of-sample" predictions of the outcome variable, which cannot be easily calculated when random effects are present. We describe two novel variants of cross-validation that can be applied to NLME models. One variant, where out-of-s ...[more]

PMID: 23532511

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Project description:Taking parameter uncertainty into account is key to make drug development decisions such as testing whether trial endpoints meet defined criteria. Currently used methods for assessing parameter uncertainty in NLMEM have limitations, and there is a lack of diagnostics for when these limitations occur. In this work, a method based on sampling importance resampling (SIR) is proposed, which has the advantage of being free of distributional assumptions and does not require repeated parameter estimation. To perform SIR, a high number of parameter vectors are simulated from a given proposal uncertainty distribution. Their likelihood given the true uncertainty is then approximated by the ratio between the likelihood of the data given each vector and the likelihood of each vector given the proposal distribution, called the importance ratio. Non-parametric uncertainty distributions are obtained by resampling parameter vectors according to probabilities proportional to their importance ratios. Two simulation examples and three real data examples were used to define how SIR should be performed with NLMEM and to investigate the performance of the method. The simulation examples showed that SIR was able to recover the true parameter uncertainty. The real data examples showed that parameter 95 % confidence intervals (CI) obtained with SIR, the covariance matrix, bootstrap and log-likelihood profiling were generally in agreement when 95 % CI were symmetric. For parameters showing asymmetric 95 % CI, SIR 95 % CI provided a close agreement with log-likelihood profiling but often differed from bootstrap 95 % CI which had been shown to be suboptimal for the chosen examples. This work also provides guidance towards the SIR workflow, i.e.,which proposal distribution to choose and how many parameter vectors to sample when performing SIR, using diagnostics developed for this purpose. SIR is a promising approach for assessing parameter uncertainty as it is applicable in many situations where other methods for assessing parameter uncertainty fail, such as in the presence of small datasets, highly nonlinear models or meta-analysis.

Dataset Information

Cross-validation for nonlinear mixed effects models.

Publications

Cross-validation for nonlinear mixed effects models.

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