| Build on established knowledge |
The established knowledge model encourages discovery of new phenomena by building on what is known. For example, addition of body weight on parameters of CL and V in the base model based on the principles of allometric scaling. This allows identification of the influence of other covariates after adjusting for known associations with body size. Lack of standard reporting of parameter values makes it harder to determine what has been established.
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| Graphical exploration method |
Simple to use. Provide a quick overview of correlation between covariates and parameters included in the model. Assessment is only possible for model parameters where between subject variability is included in the base model. Subjective judgment based on visualization. Dependent on quality of individual estimates. Unreliable results for EBEs in presence of (>20%) shrinkage.
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Only one parameter‐covariate relationship can be identified at a time, like univariate selection. Cannot handle time‐varying covariates. Does not provide an explicit functional representation of the relationship between parameters and covariates and the shape of the relation is tainted by shrinkage. Does not consider correlation between covariates.
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| Stepwise GAM |
Simple to use. Provides explicit functional representation of the parameters‐covariates relationship. Short running time. Stability of covariates selection can be assessed using Bootstrap GAM. Implemented in software (Xpose). Assessment is only possible for model parameters where between subject variability is included in the base model. Highly depends on the quality of the EBEs. The selection of covariates and the functional form may be tainted by shrinkage, and not yield the optimal model for implementation. Selection bias in presence of shrinkage. Does not handle time‐varying covariates. Does not handle cross‐parameter correlations. Limited utility with development of more sophisticated tools.
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| SCM |
Handles selection of multiple covariates within the population model. Stable search methods (based on Greedy algorithm). Handles time‐varying covariates. Allows investigating covariates on parameters without a separate random effect. Does not depend on posterior Bayes estimates. p‐values are pre‐specified and easy to communicate. Implemented in software (PsN). Time‐consuming unless linearization methods are used. Selection bias and reduction of predictive performance. Allows selection among highly correlated covariates, but in many instances not powered to distinguish. Small dataset may result in low power and poor predictive performance. Only statistically significant covariates are selected. Final models from the current data may not be predictive to external data. Difficult to adjust p value for multiple testing.
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| LASSO |
Increase predictive performance in particular for small sample size. Shorter run‐time if many covariate relations are investigated. Allows investigating covariates on parameters without a separate random effect. Correlated covariates are handled. Does not depend on posterior Bayes estimates. No need to specify p value for covariate selection. Cross‐validation is challenging on unstable models. Not designed for hypothesis testing. Little experience of this method in pharmacometrics.
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| WAM |
Requires fewer model runs than stepwise procedures. Provides rapid indication of candidate covariates for inclusion. WAM provides a competing set of parsimonious models while stepwise procedures lead to a single parsimonious model. Requires a stable full model with covariances. Ill‐conditioning of the full model can lead to irregularities in the likelihood surface and a poor approximation. Sensitive to parameterization. Limited experience.
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| Prespecification methods |
| FFEM |
Direct assessment of all covariate relations of interest. Provides a fixed framework for inference, avoiding downward bias in standard errors. Useful for assessing clinical relevance. Reduces risk of overfitting to observed data. Correct inference assumes covariate relations have been captured correctly. Not true if covariate act on additional parameter in the model. Pre‐selection of correlated covariates may result in omission of important covariate relationships. Problematic if predefined full model is unstable. Not a completely prespecifiable method.
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| FREM |
Correlated covariates present no problems. No covariate‐parameter relation is assumed to be zero, among the parameters and covariates of interest. Handles missing data implicitly. Covariates are truly prespecifiable. Choose covariates at the level of labeling not at the level of modeling. Useful for assessing clinical relevance. No covariate‐parameter relation is fixed to zero, even if it is mechanistically implausible to act on the specific parameter. Assessment is only possible for model parameters where between subject variability is included in the base model. Time‐varying covariates are difficult to handle. Complex to explain and interpret. Similar limitations as FFEM method, except that correlated covariates are well handled and thereby preselection is possible, and the method can be completely prespecified
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