Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Exposure-response modeling of clinical end points using latent variable indirect response models.

ABSTRACT: Exposure-response modeling facilitates effective dosing regimen selection in clinical drug development, where the end points are often disease scores and not physiological variables. Appropriate models need to be consistent with pharmacology and identifiable from the time courses of available data. This article describes a general framework of applying mechanism-based models to various types of clinical end points. Placebo and drug model parameterization, interpretation, and assessment are discussed with a focus on the indirect response models.

SUBMITTER: Hu C

PROVIDER: S-EPMC4076802 | biostudies-other | 2014

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

Exposure-response modeling of clinical end points using latent variable indirect response models.

Hu C C

CPT: pharmacometrics & systems pharmacology 20140604

Exposure-response modeling facilitates effective dosing regimen selection in clinical drug development, where the end points are often disease scores and not physiological variables. Appropriate models need to be consistent with pharmacology and identifiable from the time courses of available data. This article describes a general framework of applying mechanism-based models to various types of clinical end points. Placebo and drug model parameterization, interpretation, and assessment are discu ...[more]

PMID: 24897307

Similar Datasets

Latent variable modeling for the microbiome.

Project description:The human microbiome is a complex ecological system, and describing its structure and function under different environmental conditions is important from both basic scientific and medical perspectives. Viewed through a biostatistical lens, many microbiome analysis goals can be formulated as latent variable modeling problems. However, although probabilistic latent variable models are a cornerstone of modern unsupervised learning, they are rarely applied in the context of microbiome data analysis, in spite of the evolutionary, temporal, and count structure that could be directly incorporated through such models. We explore the application of probabilistic latent variable models to microbiome data, with a focus on Latent Dirichlet allocation, Non-negative matrix factorization, and Dynamic Unigram models. To develop guidelines for when different methods are appropriate, we perform a simulation study. We further illustrate and compare these techniques using the data of Dethlefsen and Relman (2011, Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proceedings of the National Academy of Sciences108, 4554-4561), a study on the effects of antibiotics on bacterial community composition. Code and data for all simulations and case studies are available publicly.

| S-EPMC6797058 | biostudies-literature

Variable Assessment in Latent Class Models.

Project description:The latent class model provides an important platform for jointly modeling mixed-mode data - i.e., discrete and continuous data with various parametric distributions. Multiple mixed-mode variables are used to cluster subjects into latent classes. While the mixed-mode latent class analysis is a powerful tool for statisticians, few studies are focused on assessing the contribution of mixed-mode variables in discriminating latent classes. Novel measures are derived for assessing both absolute and relative impacts of mixed-mode variables in latent class analysis. Specifically, the expected posterior gradient and the Kolmogorov variation of the posterior distribution, as well as related properties are studied. Numerical results are presented to illustrate the measures.

| S-EPMC4043346 | biostudies-other

On latent-variable model misspecification in structural measurement error models for binary response.

Project description:We consider structural measurement error models for a binary response. We show that likelihood-based estimators obtained from fitting structural measurement error models with pooled binary responses can be far more robust to covariate measurement error in the presence of latent-variable model misspecification than the corresponding estimators from individual responses. Furthermore, despite the loss in information, pooling can provide improved parameter estimators in terms of mean-squared error. Based on these and other findings, we create a new diagnostic method to detect latent-variable model misspecification in structural measurement error models with individual binary response. We use simulation and data from the Framingham Heart Study to illustrate our methods.

| S-EPMC3229040 | biostudies-literature

Bayesian variable selection for latent class models.

Project description:In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.

| S-EPMC3035762 | biostudies-literature

A cautionary note on testing latent variable models.

Project description:The article tackles the practice of testing latent variable models. The analysis covered recently published studies from 11 psychology journals varying in orientation and impact. Seventy-five studies that matched the criterion of applying some of the latent modeling techniques were reviewed. Results indicate the presence of a general tendency to ignore the model test (?(2)) followed by the acceptance of approximate fit hypothesis without detailed model examination yielding relevant empirical evidence. Due to reduced sensitivity of such a procedure to confront theory with data, there is an almost invariable tendency to accept the theoretical model. This absence of model test consequences, manifested in frequently unsubstantiated neglect of evidence speaking against the model, thus implies the perilous question of whether such empirical testing of latent structures (the way it is widely applied) makes sense at all.

| S-EPMC4635201 | biostudies-literature

Efficient estimation of generalized linear latent variable models.

Project description:Generalized linear latent variable models (GLLVM) are popular tools for modeling multivariate, correlated responses. Such data are often encountered, for instance, in ecological studies, where presence-absences, counts, or biomass of interacting species are collected from a set of sites. Until very recently, the main challenge in fitting GLLVMs has been the lack of computationally efficient estimation methods. For likelihood based estimation, several closed form approximations for the marginal likelihood of GLLVMs have been proposed, but their efficient implementations have been lacking in the literature. To fill this gap, we show in this paper how to obtain computationally convenient estimation algorithms based on a combination of either the Laplace approximation method or variational approximation method, and automatic optimization techniques implemented in R software. An extensive set of simulation studies is used to assess the performances of different methods, from which it is shown that the variational approximation method used in conjunction with automatic optimization offers a powerful tool for estimation.

| S-EPMC6493759 | biostudies-literature

Neuroimaging of individual differences: A latent variable modeling perspective.

Project description:Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

| S-EPMC6980382 | biostudies-literature

Latent variable modeling paradigms for genotype-trait association studies.

Project description:Characterizing associations among multiple single-nucleotide polymorphisms (SNPs) within and across genes, and measures of disease progression or disease status will potentially offer new insight into disease etiology and disease progression. However, this presents a significant analytic challenge due to the existence of multiple potentially informative genetic loci, as well as environmental and demographic factors, and the generally uncharacterized and complex relationships among them. Latent variable modeling approaches offer a natural framework for analysis of data arising from these population-based genetic association investigations of complex diseases as they are well-suited to uncover simultaneous effects of multiple markers. In this manuscript we describe application and performance of two such latent variable methods, namely structural equation models (SEMs) and mixed effects models (MEMs), and highlight their theoretical overlap. The relative advantages of each paradigm are investigated through simulation studies and, finally, an application to data arising from a study of anti-retroviral-associated dyslipidemia in HIV-infected individuals is provided for illustration.

| S-EPMC3312474 | biostudies-literature

Studying Latent Criterion Validity for Complex Structure Measuring Instruments Using Latent Variable Modeling.

Project description:Validity coefficients for multicomponent measuring instruments are known to be affected by measurement error that attenuates them, affects associated standard errors, and influences results of statistical tests with respect to population parameter values. To account for measurement error, a latent variable modeling approach is discussed that allows point and interval estimation of the relationship of an underlying latent factor to a criterion variable in a setting that is more general than the commonly considered homogeneous psychometric test case. The method is particularly helpful in validity studies for scales with a second-order factorial structure, by allowing evaluation of the relationship between the second-order factor and a criterion variable. The procedure is similarly useful in studies of discriminant, convergent, concurrent, and predictive validity of measuring instruments with complex latent structure, and is readily applicable when measuring interrelated traits that share a common variance source. The outlined approach is illustrated using data from an authoritarianism study.

| S-EPMC7328229 | biostudies-literature

Hidden Markov latent variable models with multivariate longitudinal data.

Project description:Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use.

| S-EPMC5097710 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data