Latent Response Research Articles

Decomposing group differences of latent means of ordered categorical variables within a genetic factor model.

A genetic factor model is introduced for decomposition of group differences of the means of phenotypic behavior as well as individual differences when the research variables under consideration are ordered categorical. The model employs the general Genetic Factor Model proposed by Neale and Cardon (Methodology for genetic studies of twins and families, 1992) and, more specifically, the extension proposed by Dolan et al. (Behav Genet 22: 319-335, 1992) which enables decomposition of group differences of the means associated with genetic and environmental factors. Using a latent response variable (LRV) formulation (Muthén and Asparouhov, Latent variable analysis with categorical outcomes: multiple-group and growth modeling in Mplus. Mplus web notes: No. 4, Version 5, 2002), proportional differences of response categories between groups are modeled within the genetic factor model in terms of the distributional differences of latent response variables assumed to underlie the observed ordered categorical variables. Use of the proposed model is illustrated using a measure of conservatism in the data collected from the Australian Twin Registry.

Upregulation of uncoupling protein-3 in skeletal muscle during exercise: a potential antioxidant function

Uncoupling protein-3 (UCP3) expression has been shown to increase dramatically in response to muscular contraction, but the physiological significance of UCP3 upregulation is still elusive. In this study, UCP3 mRNA and protein expression were investigated along with mitochondrial respiratory function, reactive oxygen species (ROS) generation, and antioxidant defense in rat skeletal muscle during and after an acute bout of prolonged exercise. UCP3 mRNA expression was elevated sharply at 45 min of exercise, reaching 7- to 8-fold above resting level at 150 min. The increase in UCP3 protein content showed a latent response but was elevated ∼ 1.9-fold at 120 min of exercise. Both UCP3 mRNA and UCP3 protein gradually returned to resting levels 24 h postexercise. Mitochondrial ROS production was progressively increased during exercise. However, ROS showed a dramatic drop at 150 min although their levels remained severalfold higher during the recovery. Mitochondrial State 4 respiration rate was increased by 46 and 58% ( p < 0.05) at 90 and 120 min, respectively, but returned to resting rate at 150 min, when State 3 respiration and respiratory control index (RCI) were suppressed. ADP-to-oxygen consumption ( P/ O) ratio and ATP synthase activity were lowered at 3 h postexercise, whereas proton motive force and mitochondrial malondialdehyde content were unchanged. Manganese superoxide dismutase gene expression was not affected by exercise except for an increase in mRNA abundance at 3 h postexercise. These data demonstrate that UCP3 expression in rat skeletal muscle can be rapidly upregulated during prolonged exercise, possibly owing to increased ROS generation. Increased UCP3 may partially alleviate the proton gradient across the inner membrane, thereby reducing further ROS production by the electron transport chain. However, prolonged exercise caused a decrease in energy coupling efficiency in muscle mitochondria revealed by an increased respiration rate due to proton leak (State 4/State 3 ratio) and decreased RCI. We thus propose that the compromise of the oxidative phosphorylation efficiency due to UCP3 upregulation may serve an antioxidant function to protect the muscle mitochondria from exercise-induced oxidative stress.

Inference for a skew extension of the Grubbs model

In this paper, we discuss inferential aspects for the Grubbs model when the unknown quantity x (latent response) follows a skew-normal distribution, extending early results given in Arellano-Valle et al. (J Multivar Anal 96:265–281, 2005b). Maximum likelihood parameter estimates are computed via the EM-algorithm. Wald and likelihood ratio type statistics are used for hypothesis testing and we explain the apparent failure of the Wald statistics in detecting skewness via the profile likelihood function. The results and methods developed in this paper are illustrated with a numerical example.

111 Facts and Theories of Organ Regeneration in Adults

Induced organ regeneration is de novo synthesis of a physiological, or nearly physiological, organ at the same anatomical site as the organ that is being replaced. Regeneration of skin, peripheral nerves, and the conjunctiva have been accomplished using nothing more than biologically active scaffolds (regeneration templates) seeded with epithelial cells; devices for regeneration of the first two organs are in clinical use. Templates appear to function by blocking contraction well as by acting as temporary configurational guides for synthesis of new stroma that resembles that of the organ under replacement. The combined evidence supports a theory which predicts that selective blocking of the adult healing response uncovers part, at least, of the latent fetal response to injury and, in the presence of the appropriate scaffold, eventually leads to organ regeneration. An independent theory suggests that loss of regenerative potential, which is observed during the mammalian fetal-adult transition, is associated with simultaneous acquisition of individual immunocompetence.

Means matter, but variance matter too: Decomposing response latency influences on variance heterogeneity in stated preference experiments

Response time latencies have been shown to influence consumer’s choice behaviour in choice-based-conjoint studies. The literature has shown that response time latencies affect the mean outputs of parameter estimates derived from models of discrete choice. In this paper, we add further insight into the influences response time latencies have on such models by modelling latent response information associated with the variance of random parameter distributions through parameterisation of variance heterogeneity (or heteroskedasticity). We demonstrate that response time latencies influence not only the means of random parameter distributions, but also the variances, and that failure to account for both may result in incorrect model inferences being drawn.

Dobutamine stress echocardiography in the evaluation of cardiac haemodynamics after repair of tetralogy of Fallot in children: negative effects of pulmonary regurgitation

Objective — The aims were to evaluate right and left ventricular systolic function and pulmonary regurgitation (PR) at rest and during dobutamine stress echocardiography (DSE) and to assess relationships between PR and cardiac haemodynamics in late postoperative tetralogy of Fallot patients.Methods and results — Eighteen children (postoperative period 10.9 ± 2.9 years) had heart rates, PR volumes and velocities, right ventricle (RV) volumes, ejection fraction, cardiac output and index measured at rest and during DSE. Left ventricular ejection fraction did not significantly increase and the RV volumes did not change significantly during DSE (p > 0.05). RV ejection fraction was significantly lower than that of the left ventricle at rest (p < 0.001), and patients failed to increase RV ejection fraction during DSE (p > 0.05). PR measurements increased significantly during DSE (p < 0.05). PR velocity correlated positively with RV end-diastolic volume both at rest and during DSE (p < 0.01). PR volume was inversely correlated with ejection fraction, cardiac output and index at rest and during DSE (p < 0.05).Conclusion — Latent dysfunction and impaired functional response to stress of both ventricles are detected by DSE. Although many postoperative patients are asymptomatic; assessment of postoperative haemodynamics by exercise or DSE will probably result in early detection of latent ventricular dysfunction.

Latent Class Logistic Regression: Application to Marijuana Use and Attitudes Among High School Seniors

SummaryAnalysing the use of marijuana is challenging in part because there is no widely accepted single measure of individual use. Similarly, there is no single response variable that effectively captures attitudes toward its social and moral acceptability. One approach is to view the joint distribution of multiple use and attitude indicators as a mixture of latent classes. Pooling items from the annual ‘Monitoring the future’ surveys of American high school seniors from 1977 to 2001, we find that marijuana use and attitudes are well summarized by a four-class model. Secular trends in class prevalences over this period reveal major shifts in use and attitudes. Applying a multinomial logistic model to the latent response, we investigate how class membership relates to demographic and life style factors, political beliefs and religiosity over time. Inferences about the parameters of the latent class logistic model are obtained by a combination of maximum likelihood and Bayesian techniques.

Bayesian dynamic modeling of latent trait distributions

Studies of latent traits often collect data for multiple items measuring different aspects of the trait. For such data, it is common to consider models in which the different items are manifestations of a normal latent variable, which depends on covariates through a linear regression model. This article proposes a flexible Bayesian alternative in which the unknown latent variable density can change dynamically in location and shape across levels of a predictor. Scale mixtures of underlying normals are used in order to model flexibly the measurement errors and allow mixed categorical and continuous scales. A dynamic mixture of Dirichlet processes is used to characterize the latent response distributions. Posterior computation proceeds via a Markov chain Monte Carlo algorithm, with predictive densities used as a basis for inferences and evaluation of model fit. The methods are illustrated using data from a study of DNA damage in response to oxidative stress.

Modeling Individuals' Frequency and Time Allocation Behavior for Shopping Activities Considering Household-Level Random Effects

A comprehensive frequency and time allocation modeling system for shopping activities is described. The modeling system is person-based but explicitly considers fixed and random household effects. It has three components: a weekly shopping frequency model, a daily shopping frequency model, and a time allocation model for individual shopping episodes. The frequency models consider activity generation as a latent response—the propensity to participate in shopping activities. This latent response is modeled by using an ordinal response model. Both the weekly and daily frequency models are multilevel ordinal logit models, in which the household is the highest level and the individual is the lowest level. The multilevel ordinal logit models incorporate household-level influences on an individual's shopping behavior in terms of fixed effects and a random intercept. The time allocation models are hazard duration models that consider household-level random heterogeneity. The entire modeling system is sequential f...

Modeling Individuals’ Frequency and Time Allocation Behavior for Shopping Activities considering Household-Level Random Effects

A comprehensive frequency and time allocation modeling system for shopping activities is described. The modeling system is person-based but explicitly considers fixed and random household effects. It has three components: a weekly shopping frequency model, a daily shopping frequency model, and a time allocation model for individual shopping episodes. The frequency models consider activity generation as a latent response—the propensity to participate in shopping activities. This latent response is modeled by using an ordinal response model. Both the weekly and daily frequency models are multilevel ordinal logit models, in which the household is the highest level and the individual is the lowest level. The multilevel ordinal logit models incorporate household-level influences on an individual's shopping behavior in terms of fixed effects and a random intercept. The time allocation models are hazard duration models that consider household-level random heterogeneity. The entire modeling system is sequential from the weekly frequency component to the time allocation component. The outputs of the earlier components enter as inputs to the later components: weekly frequency is the input to the daily frequency model; weekly and daily frequencies are input to the time allocation model.

Local influence analysis of multivariate probit latent variable models

The multivariate probit model is very useful for analyzing correlated multivariate dichotomous data. Recently, this model has been generalized with a confirmatory factor analysis structure for accommodating more general covariance structure, and it is called the MPCFA model. The main purpose of this paper is to consider local influence analysis, which is a well-recognized important step of data analysis beyond the maximum likelihood estimation, of the MPCFA model. As the observed-data likelihood associated with the MPCFA model is intractable, the famous Cook's approach cannot be applied to achieve local influence measures. Hence, the local influence measures are developed via Zhu and Lee's [Local influence for incomplete data model, J. Roy. Statist. Soc. Ser. B 63 (2001) 111–126.] approach that is closely related to the EM algorithm. The diagnostic measures are derived from the conformal normal curvature of an appropriate function. The building blocks are computed via a sufficiently large random sample of the latent response strengths and latent variables that are generated by the Gibbs sampler. Some useful perturbation schemes are discussed. Results that are obtained from analyses of an artificial example and a real example are presented to illustrate the newly developed methodology.

Lead (Pb) exposure and its effect on APP proteolysis and Aβ aggregation

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with clinical manifestations appearing in old age, however, the initial stages of this disease may begin early in life. AD is characterized by the presence of excessive deposits of aggregated beta-amyloid (Abeta) peptides, which are derived from the beta-amyloid precursor protein (APP) following processing by beta-secretase and gamma-secretase. Recently, we have reported that developmental exposure of rats to Pb resulted in latent elevation of APP mRNA, APP, and Abeta in old age. Here we examined whether latent up-regulation in APP expression and Abeta levels is exacerbated by concurrent disturbances in APP processing or Abeta aggregation. Among the environmental metals tested, only Abeta solutions containing Pb promoted the formation of Abeta aggregates at nanomolar concentrations. The lifetime profiles of alpha-, beta-, and gamma-secretases remained constant in adult and aging animals, and developmental exposure to Pb did not alter them. Furthermore, the addition of various concentrations of Pb (0.1 to 50 microM) to cerebral cortical extracts derived from control animals also did not affect the proteolytic activities of these enzymes. Therefore, we propose that amyloidogenesis is promoted by a latent response to developmental reprogramming of the expression of the APP gene by early exposure to Pb, as well as enhancement of Abeta aggregation in old age. In rodents, these events occur without Pb-induced disturbances to the enzymatic processing of APP. The aforementioned results provide further evidence for the developmental basis of amyloidogenesis and late-life disturbances in AD-associated proteins by environmental agents.

Assessing Treatment Effect Heterogeneity in Clinical Trials with Blocked Binary Outcomes

This paper addresses treatment effect heterogeneity (also referred to, more compactly, as 'treatment heterogeneity') in the context of a controlled clinical trial with binary endpoints. Treatment heterogeneity, variation in the true (causal) individual treatment effects, is explored using the concept of the potential outcome. This framework supposes the existance of latent responses for each subject corresponding to each possible treatment. In the context of a binary endpoint, treatment heterogeniety may be represented by the parameter, pi2, the probability that an individual would have a failure on the experimental treatment, if received, and would have a success on control, if received. Previous research derived bounds for pi2 based on matched pairs data. The present research extends this method to the blocked data context. Estimates (and their variances) and confidence intervals for the bounds are derived. We apply the new method to data from a renal disease clinical trial. In this example, bounds based on the blocked data are narrower than the corresponding bounds based only on the marginal success proportions. Some remaining challenges (including the possibility of further reducing bound widths) are discussed.

Bounds on average causal effects in studies with a latent response variable

Consider a case where cause-effect relationships between variables can be described as a directed acylic graph and the corresponding recursive factorization of a joint distribution. In order to provide the bounds on average causal effects in studies with a latent response variable, this paper proposes a graphical criterion for selecting covariates and variables caused by the response variable. The result enables us not only to judge from the graph structure whether the bounds on an average causal effect can be expressed through the observed quantities, but also to provide their closed-form expressions in case where its answer is affirmative. The graphical criterion of this paper is helpful to evaluate the bounds on average causal effects when it is difficult to observe a response variable.

The Fetal Basis of Amyloidogenesis: Exposure to Lead and Latent Overexpression of Amyloid Precursor Protein and β-Amyloid in the Aging Brain

The fetal basis of adult disease (FeBAD) hypothesis states that many adult diseases have a fetal origin. According to FeBAD, injury or environmental influences occurring at critical periods of organ development could result in "programmatic" changes via alterations in gene expression or gene imprinting that may result in functional deficits that become apparent later in life. Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is characterized by excessive deposits of aggregated beta-amyloid (Abeta) peptides, which are snippets of the beta-amyloid precursor protein (APP). The predominantly sporadic nature of AD suggests that the environment must play a role in neurodegeneration. To examine latent responses to an environmental agent, we exposed rodents to lead and monitored the lifetime expression of the APP gene. We observed that APP mRNA expression was transiently induced in neonates, but exhibited a delayed overexpression 20 months after exposure to Pb had ceased. This upregulation in APP mRNA expression was commensurate with a rise in activity of the transcription factor Sp1, one of the regulators of the APP gene. Furthermore, the increase in APP gene expression in old age was accompanied by an elevation in APP and its amyloidogenic Abeta product. In contrast, APP expression, Sp1 activity, as well as APP and Abeta protein levels were unresponsive to Pb exposure during old age. These data suggested that environmental influences occurring during brain development predetermined the expression and regulation of APP later in life, potentially altering the course of amyloidogenesis.

Application of Stochastic Measurement Models to Visual Function Rating Scale Questionnaires

Purpose: To test hypotheses that low vision patient responses to visual function rating scale questionnaires conform to an additive conjoint structure and that the Likert score is a sufficient statistic for the latent patient trait; to compare results for two competing stochastic measurement models; and to determine if different questionnaires measure the same construct in low vision patients. Methods: Visual function rating scale questionnaires were administered to 284 low vision subjects by telephone. Each subject was administered two of four questionnaires: ADVS, NEI VFQ-25 plus supplement, expanded VAQ, and VF-14. Results: Data were analyzed with the Muraki item response model and the Andrich measurement model. The estimates of latent person, item, and response threshold measures from the two models are linearly related. The Muraki model produced a better overall fit to the item response data, the Andrich model produced a better fit to the average ratings for each person and item. Fit statistics for the Andrich model were proportional to the item-dependent discrimination parameter in the Muraki model. The ADVS was the most accurate measure and the NEI VFQ was the least. Reliability was similar for all four instruments. Person measures for each pair of instruments were linearly related indicating that all four instruments measured the same construct. The person measure estimate from the Andrich model is monotonic with the average rating. That relationship suggests a transformation of the Likert score that can correct the floor and ceiling effects in rating scale data.Conclusions: Patient responses to all four questionnaires conform to varying degrees to an additive conjoint structure. The Likert score is a sufficient statistic for the ADVS and the VAQ, but not for the NEI VFQ or VF-14. All four instruments measure the same construct in the low vision population, but they differ in measurement accuracy and precision.

A Latent-Change Scaling Model for Longitudinal Multiple Choice Data

A latent-change scaling model for the analysis of repeated-measures multiple-choice data is presented. The model extends previous work by combining latent class analysis and low dimensional scaling techniques in a longitudinal framework where subjects may change their preferences for the response categories over time. The latent structural component of the model characterizes both the cross-sectional heterogeneity of the population and an underlying change process over time; the measurement component of the model uses a scaling procedure to produce a joint representation of latent classes and response categories in a low dimensional space that represents individual differences in the utilities of the categories. An analysis of a national panel data set is used to illustrate both aspects of the model. A hypothetical example illustrates additional features of the model that can be tested when multiple indicators are collected at each time point.

Structural Mean Effects of Noncompliance

A randomized, placebo-controlled blood pressure (BP) reduction trial has recorded compliance measures by electronic monitoring. Causal questions regarding dose effects are of interest, but one often wants to avoid the assumption that compliance quantiles on both randomized arms are comparable. Structural mean models (SMMs) do avoid this assumption; however, they do not allow for interaction effects between variables observed on different randomized arms. Such an interaction between observed exposure on the treatment arm and latent treatmentfree response (i. e., placebo response) was suggested by the data. Building on structural models, we propose an approach that makes this possible. To allow for a structural interaction effect with potential placebo response, we invoke a compliance selection model. It turns out that SMMs imply identifiable selection models; hence the secondary model is testable. Next, to assess the amount of variation explained by the structural model, we identify separate variance components for different error types. This yields a measure of the variation in treatment effects over individuals. In addition, it allows us to compare the fit of different plausible structural models. On the BP data, we find a significant interaction effect: Higher exposure levels are estimated to have more effect in subgroups with poorer placebo response. Finite-sample properties of the proposed estimators are verified through simulation.

How Important is Methodology for the Estimates of the Determinants of Happiness?

Psychologists and sociologists usually interpret happiness scores as cardinal and comparable across respondents, and thus run OLS regressions on happiness and changes in happiness. Economists usually assume only ordinality and have mainly used ordered latent response models, thereby not taking satisfactory account of fixed individual traits. We address this problem by developing a conditional estimator for the fixed-effect ordered logit model. We find that assuming ordinality or cardinality of happiness scores makes little difference, whilst allowing for fixed-effects does change results substantially. We call for more research into the determinants of the personality traits making up these fixed-effects.

Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models

Unfolding response models, a class of item response theory (IRT) models that assume a unimodal item response function (IRF), are often used for the measurement of attitudes. Verhelst and Verstralen (1993) and Andrich and Luo (1993) independently developed unfolding response models by relating the observed responses to a more common monotone IRT model using a latent response model (LRM; Maris, 1995 ). This article generalizes their approach, and suggests a data augmentation scheme for the estimation of any unfolding response model. The article introduces two Markov chain Monte Carlo (MCMC) estimation procedures for the Bayesian estimation of unfolding model parameters; one is a direct implementation of MCMC, and the second utilizes the data augmentation method. We use the estimation procedure to analyze three data sets, one simulated, and two from real attitudinal surveys.