Use Of Latent Variable Models Research Articles

Measurement and Uncertainty Preserving Parametric Modeling for Continuous Latent Variables With Discrete Indicators and External Variables

Research in education and behavioral sciences often involves the use of latent variable models that are related to indicators, as well as related to covariates or outcomes. Such models are subject to interpretational confounding, which occurs when fitting the model with covariates or outcomes alters the results for the measurement model. This has received attention in models for continuous observable variables but to date has not been examined in the context of discrete variables. This work demonstrates that interpretational confounding can occur in models for discrete variables, and develops a multistage Bayesian estimation approach to deal with this problem. The key features of this approach are that it is (a) measurement preserving, in that it precludes the possibility of interpretational confounding, and (b) uncertainty preserving, in that the uncertainty from the earlier stage of estimating the measurement model is propagated to the second stage of estimating the relations between the latent variable(s) and any covariates or outcomes. Previous work on these methods had only considered models for continuous observed variables, and software was limited to models with a single latent variable and either covariates or outcomes. This work extends the approach and software to a more general class of solutions, including discrete variables, illustrating the procedures with analyses of real data. Functions for conducting the analyses in widely available software are provided.

Probabilistic machine learning based soft-sensors for product quality prediction in batch processes

The major promise of the 4th industrial revolution is encompassed by the utilization of real-time process data to inform operational decision-making. Research focus relating to the monitoring of batch process end product qualities has typically been dominated by the use of latent variable models. In this work, we combined latent variable modeling with probabilistic machine learning methods to construct novel soft-sensors, which are able to synchronously capture nonlinearities expressed within process data, as well as identify accurate uncertainty estimates for predictions. Specifically, we explored the combination of multiway projection to latent structures (MPLS) with Gaussian processes (GPs), Bayesian neural networks (BNNs) and heteroscedastic noise neural networks (HNNs). We demonstrated performance of the soft-sensors for industrial batch process quality control, which involves estimating the end viscosity of different variants of a non-newtonian liquid product manufactured over different periods. Through experimental validation, it is concluded that the use of the MPLS-HNN soft-sensor provides particular promise for industrial batch process monitoring given its high accuracy and reliability, as well as ease for practical implementation.

Do-calculus enables estimation of causal effects in partially observed biomolecular pathways.

MotivationEstimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways.ResultsIn this article, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl’s do-calculus and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation.Availability and implementationThe code and the data documenting all the case studies are available at https://github.com/srtaheri/LVMwithDoCalculus.Supplementary information Supplementary data are available at Bioinformatics online.

Network models can help focus research on the role of culture and context in psychopathology, but don't discount latent variable models.

Borsboom et al. correctly note that the use of latent variable models in cross-cultural research has resulted in a futile search for universal, biological causes of psychopathology; however, this is not an inevitable outcome of such models. While network analytic approaches require further development, network models have the potential to better elucidate the role of cultural and contextual variables related to psychopathology.

Measuring behaviours for escaping from house fires: use of latent variable models to summarise multiple behaviours.

BackgroundThis paper reports the development and testing of a construct measuring parental fire safety behaviours for planning escape from a house fire.MethodsLatent variable modelling of data on parental-reported fire safety behaviours and plans for escaping from a house fire and multivariable logistic regression to quantify the association between groups defined by the latent variable modelling and parental-report of having a plan for escaping from a house fire. Data comes from 1112 participants in a cluster randomised controlled trial set in children’s centres in 4 study centres in the UK.ResultsA two class model provided the best fit to the data, combining responses to five fire safety planning behaviours. The first group (‘more behaviours for escaping from a house fire’) comprised 86 % of participants who were most likely to have a torch, be aware of how their smoke alarm sounds, to have external door and window keys accessible, and exits clear. The second group (‘fewer behaviours for escaping from a house fire’) comprised 14 % of participants who were less likely to report these five behaviours. After adjusting for potential confounders, participants allocated to the ‘more behaviours for escaping from a house fire group were 2.5 times more likely to report having an escape plan (OR 2.48; 95 % CI 1.59–3.86) than those in the “fewer behaviours for escaping from a house fire” group.ConclusionsMultiple fire safety behaviour questions can be combined into a single binary summary measure of fire safety behaviours for escaping from a house fire. Our findings will be useful to future studies wishing to use a single measure of fire safety planning behaviour as measures of outcome or exposure.Trial registration number: NCT 01452191. Date of registration 13/10/2011

Modelling escalation in crime seriousness: a latent variable approach

This paper investigates the use of latent variable models in assessing escalation in crime seriousness. It has two aims. The first is to contrast a mixed-effects approach to modelling crime escalation with a latent variable approach. The paper therefore examines whether there are specific subgroups of offenders with distinct seriousness trajectory shapes. The second is methodological—to compare mixed-effects modelling used in previous work on escalation with group-based trajectory modelling and growth mixture modelling (mixture of mixed-effects models). The availability of software is an issue, and comparisons of fit across software packages is not straightforward. We suggest that mixture models are necessary in modelling crime seriousness, that growth mixture models rather than group-based trajectory models provide the best fit to the data, and that R gives the best software environment for comparing models. Substantively, we identify three latent groups, with the largest group showing crime seriousness increases with criminal justice experience (measured through number of conviction occasions) and decreases with increasing age. The other two groups show more dramatic non-linear effects with age, and non-significant effects of criminal justice experience. Policy considerations of these results are briefly discussed.

Unfolding Physiological State: Mortality Modelling in Intensive Care Units.

Accurate knowledge of a patient's disease state and trajectory is critical in a clinical setting. Modern electronic healthcare records contain an increasingly large amount of data, and the ability to automatically identify the factors that influence patient outcomes stand to greatly improve the efficiency and quality of care. We examined the use of latent variable models (viz. Latent Dirichlet Allocation) to decompose free-text hospital notes into meaningful features, and the predictive power of these features for patient mortality. We considered three prediction regimes: (1) baseline prediction, (2) dynamic (time-varying) outcome prediction, and (3) retrospective outcome prediction. In each, our prediction task differs from the familiar time-varying situation whereby data accumulates; since fewer patients have long ICU stays, as we move forward in time fewer patients are available and the prediction task becomes increasingly difficult. We found that latent topic-derived features were effective in determining patient mortality under three timelines: inhospital, 30 day post-discharge, and 1 year post-discharge mortality. Our results demonstrated that the latent topic features important in predicting hospital mortality are very different from those that are important in post-discharge mortality. In general, latent topic features were more predictive than structured features, and a combination of the two performed best. The time-varying models that combined latent topic features and baseline features had AUCs that reached 0.85, 0.80, and 0.77 for in-hospital, 30 day post-discharge and 1 year post-discharge mortality respectively. Our results agreed with other work suggesting that the first 24 hours of patient information are often the most predictive of hospital mortality. Retrospective models that used a combination of latent topic features and structured features achieved AUCs of 0.96, 0.82, and 0.81 for in-hospital, 30 day, and 1-year mortality prediction. Our work focuses on the dynamic (time-varying) setting because models from this regime could facilitate an on-going severity stratification system that helps direct care-staff resources and inform treatment strategies.

Dysfunctions of decision‐making and cognitive control as transdiagnostic mechanisms of mental disorders: advances, gaps, and needs in current research

Disadvantageous decision-making and impaired volitional control over actions, thoughts, and emotions are characteristics of a wide range of mental disorders such as addiction, eating disorders, depression, and anxiety disorders and may reflect transdiagnostic core mechanisms and possibly vulnerability factors. Elucidating the underlying neurocognitive mechanisms is a precondition for moving from symptom-based to mechanism-based disorder classifications and ultimately mechanism-targeted interventions. However, despite substantial advances in basic research on decision-making and cognitive control, there are still profound gaps in our current understanding of dysfunctions of these processes in mental disorders. Central unresolved questions are: (i) to which degree such dysfunctions reflect transdiagnostic mechanisms or disorder-specific patterns of impairment; (ii) how phenotypical features of mental disorders relate to dysfunctional control parameter settings and aberrant interactions between large-scale brain systems involved in habit and reward-based learning, performance monitoring, emotion regulation, and cognitive control; (iii) whether cognitive control impairments are consequences or antecedent vulnerability factors of mental disorders; (iv) whether they reflect generalized competence impairments or context-specific performance failures; (v) whether not only impaired but also chronic over-control contributes to mental disorders. In the light of these gaps, needs for future research are: (i) an increased focus on basic cognitive-affective mechanisms underlying decision and control dysfunctions across disorders; (ii) longitudinal-prospective studies systematically incorporating theory-driven behavioural tasks and neuroimaging protocols to assess decision-making and control dysfunctions and aberrant interactions between underlying large-scale brain systems; (iii) use of latent-variable models of cognitive control rather than single tasks; (iv) increased focus on the interplay of implicit and explicit cognitive-affective processes; (v) stronger focus on computational models specifying neurocognitive mechanisms underlying phenotypical expressions of mental disorders.

Latent variable modeling to assist the implementation of Quality-by-Design paradigms in pharmaceutical development and manufacturing: A review

The introduction of the Quality-by-Design (QbD) initiative and of the Process Analytical Technology (PAT) framework by the Food and Drug Administration has opened the route to the use of systematic and science-based approaches to support pharmaceutical development and manufacturing activities. In this review we discuss the role that latent variable models (LVMs) can play in the practical implementation of QbD paradigms in the pharmaceutical industry, and the potential they may have in assisting the development and manufacturing of new products. The ultimate scope is to provide practitioners with a perspective on the effectiveness of the use of LVMs in any phase of the development of a pharmaceutical product, from its design up to its commercial production. After an overview of the main regulatory paradigms the QbD initiative is founded on, we show how LVMs can be feasibly used to support pharmaceutical development and manufacturing activities while matching the regulatory Agencies' requirements. Three main areas are identified, wherein the use of LVMs can provide significant benefits: (i) process understanding, (ii) product and process design, and (iii) process monitoring and control. For each of them, the main contributions recently appeared in the literature are reviewed. Issues open for further research are also identified.

On the Use of Latent Variable Models to Detect Differences in the Interpretation of Vague Quantifiers

This study explores whether latent variable models may be used to detect individual variability in the interpretation of vague quantifiers of behavioral frequency. Using data from an experiment embedded in the 2006 National Survey of Student Engagement, the current study finds that differential interpretation may be best represented as variability between latent interpretation classes. These analyses illustrate how a researcher might use certain latent variable models to extract a methodological artifact—differential interpretation—from measurement models intended to be purely substantive.

Use of Latent Variable Modeling to Delineate Psychiatric and Cognitive Profiles in Parkinson Disease

A range of psychiatric symptoms and cognitive deficits occur in Parkinson disease (PD), and symptom overlap and comorbidity complicate the classification of nonmotor symptoms. The objective of this study was to use analytic-based approaches to classify psychiatric and cognitive symptoms in PD. Cross-sectional evaluation of a convenience sample of patients in specialty care. Two outpatient movement disorders centers at the University of Pennsylvania and Philadelphia Veterans Affairs Medical Center. One hundred seventy-seven patients with mild-moderate idiopathic PD and without significant global cognitive impairment. Subjects were assessed with an extensive psychiatric, neuropsychological, and neurological battery. Latent class analysis (LCA) was used to statistically delineate group-level symptom profiles across measures of psychiatric and cognitive functioning. Predictors of class membership were also examined. Results from the LCA indicated that a four-class solution best fit the data. The 32.3% of the sample had good psychiatric and normal cognitive functioning, 17.5% had significant psychiatric comorbidity but normal cognition, 26.0% had few psychiatric symptoms but had poorer cognitive functioning across a range of cognitive domains, and 24.3% had both significant psychiatric comorbidity and poorer cognitive functioning. Age, disease severity, and medication use predicted class membership. LCA delineates four classes of patients in mild-moderate PD, three of which experience significant nonmotor impairments and comprise over two thirds of patients. Neuropsychiatric symptoms and cognitive deficits follow distinct patterns in PD, and further study is needed to determine whether these classes are generalizable, stable, predict function, quality of life, and long-term outcomes and are amenable to treatment at a class level.

A comparison of different methods to estimate prediction uncertainty using Partial Least Squares (PLS): A practitioner's perspective

There is an increase in the use of latent variable modeling (LVM) techniques, such as Projection to Latent Structure (PLS), in the pharmaceutical industry. Thus, there exists a practical need to estimate prediction uncertainty for PLS models. Metrics such as standard error of prediction (SEP) and standard error of calibration (SEC) do not truly reflect prediction reliability. Several proposals exist in the literature to tackle the problem. This paper describes a comparison exercise for selected uncertainty estimation algorithms by testing representative pharmaceutical industrial data sets. Algorithms evaluated include linearization-based methods, Ordinary Least Squares (OLS) type method, re-sampling based method and empirical method. Algorithm performance was measured using “coverage probability”. Additionally, different approaches to estimate degrees of freedom consumed by PLS model were evaluated for uncertainty estimation purpose. These methods include the Naïve approach, the pseudo degree of freedom (PDF) approach and the generalized degrees of freedom (GDF) approach. Results from this study suggest that none of these algorithms generates accurate coverage rates for all cases considered. Thus, further development in this area is needed. Among all the evaluated algorithms, the simple Faber 96 method seems to perform slightly better under appropriate handling of the degrees of freedom. Different ways to estimate degrees of freedom were shown to have crucial effect on the performance of uncertainty estimation. The results show that the Naïve approach should be discouraged for use in uncertainty estimation in practice and the GDF approach should be preferred.

Dynamic contrast‐enhanced MRI diagnostics in oncology via principal component analysis

AbstractThis paper proposes the use of latent variable models based on principal component analysis (PCA) as a robust alternative to pharmacokinetic modeling for the analysis of the dynamic contrast‐enhanced magnetic resonance images (DCE‐MRI) often obtained during oncological studies. Theoretical and practical justifications are provided for the advantages of the PCA approach. A pilot study using clinical DCE‐MRI data on prostate patients is presented to demonstrate the method. Copyright © 2008 John Wiley & Sons, Ltd.

Product transfer between sites using Joint-Y PLS

The product transfer problem refers to that of estimating the operating conditions in a target plant in order for it to yield a final product with the same set of final characteristics as is yielded in another source plant. The estimation of such new operating conditions in the target plant often involves a combination of expertise, rules of thumb and in some cases some fundamental modeling. The use of latent variable modeling using historical data from both sites has been proposed in the past as an alternative approach (C.M. Jaeckle and J.F. MacGregor, Product transfer between plants using historical process data, AICHE J., 46 (2000) 1989–1997). This paper presents a new latent variable regression method: Joint-Y PLS (JYPLS) that is ideally suited for modeling the common latent variable structure in multiple plants. The mathematical foundations for the JYPLS model are presented and several parameter estimation approaches are given. A technique is proposed to solve the product transfer problem using the JYPLS model and two examples are given including an industrial scale-up case for a batch pulp digester from the pulp and paper industry.

Using Parcels to Convert Path Analysis Models Into Latent Variable Models

The biasing effects of measurement error in path analysis models can be overcome by the use of latent variable models. In cases where path analysis is used in practice, it is often possible to use parcels as indicators of a latent variable. The purpose of the current study was to compare latent variable models in which parcels were used as indicators of the latent variables, path analysis models of the aggregated variables, and models in which reliability estimates were used to correct for measurement error in path analysis models. Results showed that point estimates of path coefficients were smallest for the path analysis models and largest for the latent variable models. It is concluded that, whenever possible, it is better to use a latent variable model in which parcels are used as indicators than a path analysis model using total scale scores.

Accounting for Dependence in a Flexible Multivariate Receptor Model

Formulation and evaluation of environmental policy depends on receptor models that are used to assess the number and nature of pollution sources affecting the air quality for a region of interest. Different approaches have been developed for situations in which no information is available about the number and nature of these sources (e.g., exploratory factor analysis) and the composition of these sources is assumed known (e.g., regression and measurement error models). We propose a flexible approach for fitting the receptor model when only partial pollution source information is available. The use of latent variable modeling allows the direct incorporation of subject matter knowledge into the model, including known physical constraints and partial pollution source information obtained from laboratory measurements or past studies. Because air quality data often exhibit temporal and/or spatial dependence, we consider the importance of accounting for such correlation in estimating model parameters and making valid statistical inferences. We propose an approach for incorporating dependence structure directly into estimation and inference procedures via a new nested block bootstrap method that adjusts for bias in estimating moment matrices. A goodness-of-fit test that is valid in the presence of such dependence is proposed. The application of the approach is facilitated by a new multivariate extension of an existing block size determination algorithm. The proposed approaches are evaluated by simulation and illustrated with an analysis of hourly measurements of volatile organic compounds in the El Paso, Texas/Ciudad Juarez, Mexico area.

Process analysis and abnormal situation detection: from theory to practice

The article discusses the use of latent variable models based on historical data and examines their potential and limitations for improving operations for both batch and continuous processes. The use of these models for multivariate statistical process monitoring, abnormal situation detection, and fault diagnosis is demonstrated. Examples from state-of-the-art major industrial applications currently running online illustrate the tremendous potential of these methods. In this context, an industrial application for abnormal situation detection is defined as state of the art if it has been operational several years after it was commissioned, has generated large savings, has been operating safely and/or has improved safety conditions in the plant, and is accepted enthusiastically by the operators. Such an application could be based entirely on known theory, but frequently it includes company proprietary modifications to suit the particular operating characteristics of the process. The article contains an extensive literature review on the subject and practical considerations for the user, as well as warnings about potential pitfalls in areas ranging from data acquisition to modeling to online application.

Weighting for Item Non-Response in Attitude Scales by Using Latent Variable Models with Covariates

Summary We discuss the use of latent variable models with observed covariates for computing response propensities for sample respondents. A response propensity score is often used to weight item and unit responders to account for item and unit non-response and to obtain adjusted means and proportions. In the context of attitude scaling, we discuss computing response propensity scores by using latent variable models for binary or nominal polytomous manifest items with covariates. Our models allow the response propensity scores to be found for several different items without refitting. They allow any pattern of missing responses for the items. If one prefers, it is possible to estimate population proportions directly from the latent variable models, so avoiding the use of propensity scores. Artificial data sets and a real data set extracted from the 1996 British Social Attitudes Survey are used to compare the various methods proposed.

Analytic Methods for Constructing Cross-Sectional Profiles of Health Care Providers

National effort is currently directed toward developing and disseminating comparative information involving both outcomes and processes of care for health care providers. Univariate provider-specific comparative indices based on posterior summaries as well as indices based on maximum likelihood estimates have been developed for use in the cross-sectional setting. A remaining issue in the dissemination of cross-sectional profiles relates to the multivariate nature of the indices: often many performance measures are used to assess quality for a particular provider. Because this information can often be contradictory and overwhelming, there is a need for measures that summarize quality at a provider level. This article proposes the use of latent variable models for comparing health care providers in the cross-sectional setting when each provider is measured on more than one dimension of care. By combining information across dimensions of care within a provider, an integrated analysis can produce a composite measure of quality and has more statistical power to detect differences among providers. As the number of individual performance measures grows over time, composite measures will become increasingly important tools to support decision making by consumers, payors, and providers.

Use of latent variable models for detecting discrimination in salaries.

Use of latent variable models for detecting discrimination in salaries.