Properties of the full random-effect modeling approach with missing covariate data.

During drug development, a key step is the identification of relevant covariates predicting between-subject variations in drug response. The full random effects model (FREM) is one of the full-covariate approaches used to identify relevant covariates in nonlinear mixed effects models. Here we explore the ability of FREM to handle missing (both missing completely at random (MCAR) and missing at random (MAR)) covariate data and compare it to the full fixed-effects model (FFEM) approach, applied either with complete case analysis or mean imputation. A global health dataset (20 421 children) was used to develop a FREM describing the changes of height for age Z-score (HAZ) over time. Simulated datasets (n = 1000) were generated with variable rates of missing (MCAR) covariate data (0%-90%) and different proportions of missing (MAR) data condition on either observed covariates or predicted HAZ. The three methods were used to re-estimate model and compared in terms of bias and precision which showed that FREM had only minor increases in bias and minor loss of precision at increasing percentages of missing (MCAR) covariate data and performed similarly in the MAR scenarios. Conversely, the FFEM approaches either collapsed at ≥ $$ \ge $$ 70% of missing (MCAR) covariate data (FFEM complete case analysis) or had large bias increases and loss of precision (FFEM with mean imputation). Our results suggest that FREM is an appropriate approach to covariate modeling for datasets with missing (MCAR and MAR) covariate data, such as in global health studies.

Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model.

AIMS: Although there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)-based model for predicting VCM trough concentration in adult PJI patients. METHODS: The dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an independent external 32 concentrations were collected as a validation set. A total of 13 covariates and the target variable (VCM trough concentration) were included in the dataset. A covariate model was respectively constructed by support vector regression, random forest regression and gradient boosted regression trees and interpreted by SHapley Additive exPlanation (SHAP). RESULTS: The SHAP plots visualized the weight of the covariates in the models, with estimated glomerular filtration rate and VCM daily dose as the 2 most important factors, which were adopted for the model construction. Random forest regression was the optimal ML algorithm with a relative accuracy of 82.8% and absolute accuracy of 67.2% (R2 =.61, mean absolute error = 2.4, mean square error = 10.1), and its prediction performance was verified in the validation set. CONCLUSION: The proposed ML-based model can satisfactorily predict the VCM trough concentration in adult PJI patients. Its construction can be facilitated with only 2 clinical parameters (estimated glomerular filtration rate and VCM daily dose), and prediction accuracy can be rationalized by SHAP values, which highlights a profound practical value for clinical dosing guidance and timely treatment.

Self-medication Hypothesis and Family Socialization Theory: Examining Independent and Common Mechanisms Responsible for Binge Drinking.

For many, binge drinking behaviors start early and become a persistent pattern of use throughout the lifespan. In an effort to strengthen understanding of etiology, this study considered the mechanisms from the self-medication hypothesis and family socialization theory. The goal was to identify whether emotional distress is a potential shared mechanism that accounts for the development of binge drinking in different developmental periods. This study used the National Longitudinal Study of Adolescent to Adult Health (Add Health) dataset to examine binge drinking across time for n = 9,421 participants ranging in age from 11 to 18 (M = 15.39, SD = 1.62) at Wave I and ranging from 24 to 32 (M = 28.09, SD = 1.61) at Wave IV of the study. Using an autoregressive cross-lagged model, I examined how parent-child closeness, depressive symptoms, and binge drinking were related over three developmental periods. In examining cross-sectional and longitudinal relations, depressive symptoms were significantly related to binge drinking more often than parent-child closeness; however, results indicated the self-medication model may primarily account for concurrent drinking behaviors rather than long-term. The family socialization theory was indicated to account for some variability above and beyond the self-medication hypothesis. No indirect association between binge drinking and the parent-child relationship was detected through depressive symptoms, failing to support a shared mechanism between the two theories. The results provide support for a multifaceted assessment process for substance using clients, and support the use of Multisystemic Family Therapy, Multidimensional Family Therapy, and perhaps Attachment-Based Family Therapy.

Para muchos, las conductas de consumo de alcohol compulsivo comienzan temprano y se convierten en un patrón de uso persistente durante toda la vida. En un esfuerzo para fortalecer el entendimiento de la etiología, este estudio consideró los mecanismos desde el punto de vista de la hipótesis de automedicación y la teoría de socialización familiar. La meta fue identificar si la angustia emocional es un posible mecanismo compartido que explica el desarrollo de consumo de alcohol compulsivo en periodos de desarrollo distintos. Este estudio empleó el conjunto de datos del Estudio Longitudinal Nacional de Salud de Adolescentes a Adultos (conocido como Add Health) para examinar el consumo de alcohol compulsivo a lo largo del tiempo para n = 9.421 participantes con edades de 11 a 18 (M = 15.39, DE = 1.62) en la Fase I y de 24 a 32 (M = 28.09, DE = 1.61) en la Fase IV del estudio. Usando un modelo autorregresivo de correlaciones cruzadas, examiné como la cercanía padre-hijo, los síntomas depresivos y el consumo de alcohol compulsivo se relacionaban a lo largo de tres periodos de desarrollo. En un examen de relaciones transversales y longitudinales, los síntomas depresivos se asociaron significativamente al consumo de alcohol compulsivo con mayor frecuencia que la cercanía padre-hijo; sin embargo, los resultados indicaron que el modelo de automedicación podría ser una explicación principal de conductas concurrentes de consumo de alcohol en vez de a largo plazo. La teoría de socialización familiar se indicó con miras a explicar cierta variabilidad más allá de la hipótesis de automedicación. No se detectó ninguna asociación indirecta entre el consumo de alcohol compulsivo y la relación padre-hijo a través de síntomas depresivos, lo que no proporciona apoyo a un mecanismo compartido entre las dos teorías. Los resultados proporcionan apoyo a un proceso de evaluación multifacética para clientes consumidores de sustancias, y apoya el uso de Terapia Familiar Multisistémica, Terapia Familiar Multidimensional, y quizás Terapia Familiar Basada en Apegos.

Adjusted adaptive Lasso for covariate model-building in nonlinear mixed-effect pharmacokinetic models.

One important aim in population pharmacokinetics (PK) and pharmacodynamics is identification and quantification of the relationships between the parameters and covariates. Lasso has been suggested as a technique for simultaneous estimation and covariate selection. In linear regression, it has been shown that Lasso possesses no oracle properties, which means it asymptotically performs as though the true underlying model was given in advance. Adaptive Lasso (ALasso) with appropriate initial weights is claimed to possess oracle properties; however, it can lead to poor predictive performance when there is multicollinearity between covariates. This simulation study implemented a new version of ALasso, called adjusted ALasso (AALasso), to take into account the ratio of the standard error of the maximum likelihood (ML) estimator to the ML coefficient as the initial weight in ALasso to deal with multicollinearity in non-linear mixed-effect models. The performance of AALasso was compared with that of ALasso and Lasso. PK data was simulated in four set-ups from a one-compartment bolus input model. Covariates were created by sampling from a multivariate standard normal distribution with no, low (0.2), moderate (0.5) or high (0.7) correlation. The true covariates influenced only clearance at different magnitudes. AALasso, ALasso and Lasso were compared in terms of mean absolute prediction error and error of the estimated covariate coefficient. The results show that AALasso performed better in small data sets, even in those in which a high correlation existed between covariates. This makes AALasso a promising method for covariate selection in nonlinear mixed-effect models.

A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study.

BACKGROUND: Typical survival studies follow individuals to an event and measure explanatory variables for that event, sometimes repeatedly over the course of follow up. The Cox regression model has been used widely in the analyses of time to diagnosis or death from disease. The associations between the survival outcome and time dependent measures may be biased unless they are modeled appropriately. METHODS: In this paper we explore the Time Dependent Cox Regression Model (TDCM), which quantifies the effect of repeated measures of covariates in the analysis of time to event data. This model is commonly used in biomedical research but sometimes does not explicitly adjust for the times at which time dependent explanatory variables are measured. This approach can yield different estimates of association compared to a model that adjusts for these times. In order to address the question of how different these estimates are from a statistical perspective, we compare the TDCM to Pooled Logistic Regression (PLR) and Cross Sectional Pooling (CSP), considering models that adjust and do not adjust for time in PLR and CSP. RESULTS: In a series of simulations we found that time adjusted CSP provided identical results to the TDCM while the PLR showed larger parameter estimates compared to the time adjusted CSP and the TDCM in scenarios with high event rates. We also observed upwardly biased estimates in the unadjusted CSP and unadjusted PLR methods. The time adjusted PLR had a positive bias in the time dependent Age effect with reduced bias when the event rate is low. The PLR methods showed a negative bias in the Sex effect, a subject level covariate, when compared to the other methods. The Cox models yielded reliable estimates for the Sex effect in all scenarios considered. CONCLUSIONS: We conclude that survival analyses that explicitly account in the statistical model for the times at which time dependent covariates are measured provide more reliable estimates compared to unadjusted analyses. We present results from the Framingham Heart Study in which lipid measurements and myocardial infarction data events were collected over a period of 26 years.

A Bayesian Approach to More Stable Estimates of Group-Level Effects in Contextual Studies.

Multilevel analyses are often used to estimate the effects of group-level constructs. However, when using aggregated individual data (e.g., student ratings) to assess a group-level construct (e.g., classroom climate), the observed group mean might not provide a reliable measure of the unobserved latent group mean. In the present article, we propose a Bayesian approach that can be used to estimate a multilevel latent covariate model, which corrects for the unreliable assessment of the latent group mean when estimating the group-level effect. A simulation study was conducted to evaluate the choice of different priors for the group-level variance of the predictor variable and to compare the Bayesian approach with the maximum likelihood approach implemented in the software Mplus. Results showed that, under problematic conditions (i.e., small number of groups, predictor variable with a small ICC), the Bayesian approach produced more accurate estimates of the group-level effect than the maximum likelihood approach did.

A Modified Hybrid Wald's Approximation Method for Efficient Covariate Selection in Population Pharmacokinetic Analysis.

One important objective of population pharmacokinetic (PPK) analyses is to identify and quantify relationships between covariates and model parameters such as clearance and volume. To improve upon existing covariate model development methods including stepwise procedures and Wald's approximation method (WAM), this paper introduces an innovative method named the hybrid first-order conditional estimation (FOCE)/Monte-Carlo parametric expectation maximization (MCPEM)-based Wald's approximation method with backward elimination (BE), or H-WAM-BE. Compared with WAM, this new method uses MCPEM to obtain full covariance matrix after running FOCE to obtain full model parameter estimates, followed by BE to select the final covariate model. Two groups of datasets (simulation datasets and rituximab datasets) were used to compare the performance of H-WAM-BE with two other methods, likelihood ratio test (LRT)-based stepwise covariate method (SCM) and H-WAM with full subset approach (H-WAM-F) in NONMEM. Different scenarios with different sample sizes and sampling schemes were used for simulating datasets. The nominal model was used as the reference to evaluate the three methods for their ability to accurately identify parameter-covariate relationships. The methods were compared using the number of true and false positive covariates identified, number of times that they identified the reference model, computation times, and predictive performance. Best-performing H-WAM-BE methods (M2 and M4) showed comparable results with LRT-based SCM. H-WAM-BE required shorter or comparable computation times than LRT-based SCM and H-WAM-F regardless of the model structure, sample size, or sampling design used in this study.

The Utility of Pharmacometric Models in Clinical Pharmacology Research in Infants.

PURPOSE OF COMMENTARY: Acquiring knowledge on drug disposition and action in infant is challenging because of the problem of sparse and unbalanced data obtained for each individual infant due to the limited blood volume as well as the issue of extensive inter-subject and intra-subject variability in drug exposure and response due to the fast growth and dynamic maturation changes in infants. This commentary highlights the importance of using population-based pharmacometric models to improve knowledge on drug disposition and action in infants. RECENT FINDINGS: Pharmacometric modeling remains to be critical in clinical pharmacology research in infants. Many pediatric covariate models developed for scaling of drug clearance use a combination of allometric weight scaling to account for size change and a sigmoid function of antenatal development and postnatal maturation to characterize the age-related maturation. To expedite the development of safe and effective dosing regimens in infants, a number of strategies have been proposed recently, including the use of pediatric covariate model obtained from one drug for extrapolation to other drugs undergoing similar elimination pathways, as well as the combination of opportunistic clinical studies and population-based pharmacometrics models. SUMMARY: Population-based pharmacometric modeling plays a pivotal role in clinical pharmacology research in infants. Most of the covariate models reported so far focus on antibiotics undergoing renal elimination. Novel modeling strategies have been proposed recently to facilitate clinical pharmacology research and expedite the dose optimization process in infants.

Covariates in Pharmacometric Repeated Time-to-Event Models: Old and New (Pre)Selection Tools.

During covariate modeling in pharmacometrics, computational time can be reduced by using a fast preselection tool to identify a subset of promising covariates that are to be tested with the more computationally demanding likelihood ratio test (LRT), which is considered to be the standard for covariate selection. There is however a lack of knowledge on best practices for covariate (pre)selection in pharmacometric repeated time-to-event (RTTE) models. Therefore, we aimed to systematically evaluate the performance of three covariate (pre)selection tools for RTTE models: the likelihood ratio test (LRT), the empirical Bayes estimates (EBE) test, and a novel Schoenfeld-like residual test. This was done in simulated datasets with and without a "true" time-constant covariate, and both in the presence and absence of high EBE shrinkage. In scenarios with a "true" covariate effect, all tools had comparable power to detect this effect. In scenarios without a "true" covariate effect, the false positive rates of the LRT and the Schoenfeld-like residual test were slightly inflated to 5.7% and 7.2% respectively, while the EBE test had no inflated false positive rate. The presence of high EBE shrinkage (> 40%) did not affect the performance of any of the covariate (pre)selection tools. We found the EBE test to be a fast and accurate tool for covariate preselection in RTTE models. The novel Schoenfeld-like residual test proposed here had a similar performance in the tested scenarios and might be applied more readily to time-varying covariates, such as drug concentration and dynamic biomarkers.