A mixed hidden Markov model for multivariate monotone disease processes in the presence of measurement errors

Motivated by a longitudinal oral health study, the Signal-Tandmobiel® study, an inhomogeneous mixed hidden Markov model with continuous state-space is proposed to explain the caries disease process in children between 6 and 12 years of age. The binary caries experience outcomes are subject to misclassification. We modelled this misclassification process via a longitudinal latent continuous response subject to a measurement error process and showing a monotone behaviour. The baseline distributions of the unobservable continuous processes are defined as a function of the covariates through the specification of conditional distributions making use of the Markov property. In addition, random effects are considered to model the relationships among the multivariate responses. Our approach is in contrast with a previous approach working on the binary outcome scale. This method requires conditional independence of the possibly corrupted binary outcomes on the true binary outcomes. We assumed conditional independence on the latent scale, which is a weaker assumption than conditional independence on the binary scale. The aim of this article is therefore to show the properties of a model for a progressive longitudinal response with misclassification on the manifest scale but modelled on the latent scale. The model parameters are estimated in a Bayesian way using an efficient Markov chain Monte Carlo method. The model performance is shown through a simulation-based example, and the analysis of the motivating dataset is presented.

Assessing Cognitive and Social Attitudes Toward Environmental Conservation in Coral Reef Social-Ecological Systems

This study addresses the latent construct of attitudes towards environmental conservation based on study participant’s responses. We measured and evaluated the latent scale based on an 18-item scale instrument, over four experimental strata (N=945) in the US Virgin Islands and the Caribbean. We estimated the latent scale reliability and validity. We further fitted multiple alternative two-parameter logistic (2PL) and graded response models (GRM) from Item-Response Theory. We finally constructed and fitted equivalent structural and generalized structural equation models (SEM/GSEM) for the attitudinal latent scale. All scale measures (composite, alpha-based, IRT-based and SEM-based) were consistently and reliably valid measures of the study participants’ latent attitudes toward conservation. We found statistically significant differences among participant’s attributes relating to socio-demographic, physical and core environmental characteristics of participants. We assert that the nature of relationship between cognitive attitudes and individual as well as social behavior related to environmental conservation.

General methods for evolutionary quantitative genetic inference from generalised mixed models

Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioural traits, have inherently non-normal distributions. The generalised linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for non-normal traits. However, whereas GLMMs provide inference on a statistically-convenient latent scale, it will often be desirable to estimate quantitative genetic parameters on the scale upon which traits are expressed. The parameters of a fitted GLMM, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulae for quantities such as heritability of traits with Binomial and Poisson distributions are special cases of our expressions. Additionally, we show how a fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation, and apply our approach to data from a pedigreed vertebrate population.

A validity assessment of consumer satisfaction to measure performance of water services in Guatemala

This article studies the validity of consumer satisfaction reports as a tool for assessing the performance of water services in Guatemala. Using survey data from 500 households, item-total correlations and Cronbach's alpha coefficients were computed and a factor analysis was conducted to investigate whether qualitative measures of consumer satisfaction with service hours, water treatment, pressure and prices are associated with a single latent scale. In addition, probit and regression models were estimated to identify underlying factors that influence consumer satisfaction. Results show that a considerable percentage of respondents are dissatisfied with current water services, particularly with the number of daily hours of water provision and pressure levels. Findings also indicate that consumer satisfaction is related to service attributes (i.e. frequency of service interruptions and pressure levels) and some personal characteristics. This lends some support to using consumer satisfaction reports, with due caution, to assess service performance in contexts where performance indicators are lacking.