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Kidney obstruction, if untreated in a timely manner, can lead to irreversible loss of renal function. A widely used technology for evaluations of kidneys with suspected obstruction is diuresis renography. However, it is generally very challenging for radiologists who typically interpret renography data in practice to build high level of competency due to the low volume of renography studies and insufficient training. Another challenge is that there is currently no gold standard for detection of kidney obstruction. Seeking to develop a computer-aided diagnostic (CAD) tool that can assist practicing radiologists to reduce errors in the interpretation of kidney obstruction, a recent study collected data from diuresis renography, interpretations on the renography data from highly experienced nuclear medicine experts as well as clinical data. To achieve the objective, we develop a statistical model that can be used as a CAD tool for assisting radiologists in kidney interpretation. We use a Bayesian latent class modeling approach for predicting kidney obstruction through the integrative analysis of time-series renogram data, expert ratings, and clinical variables. A nonparametric Bayesian latent factor regression approach is adopted for modeling renogram curves in which the coefficients of the basis functions are parameterized via the factor loadings dependent on the latent disease status and the extended latent factors that can also adjust for clinical variables. A hierarchical probit model is used for expert ratings, allowing for training with rating data from multiple experts while predicting with at most one expert, which makes the proposed model operable in practice. An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. We demonstrate the superiority of the proposed method over several existing methods through extensive simulations. Analysis of the renal study also lends support to the usefulness of our model as a CAD tool to assist less experienced radiologists in the field. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

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This study uses the choice experiment method with 570 grain family farms located in the Huang-huai-hai Plain and determine various support policy attributes and the attribute levels for the two dimensions of policy measures and policy communication channels. Ordering effects are eliminated by warming up subjects in advance and using information disclosure. This paper uses the inferred attribute non-attendance method to process attributes ignored by the grain family farms and analyzes grain family farms' preferences for different support policies with a mixed logit model and then uses a latent class model to analyze how the characteristics of grain family farms relate to different preference types. We find that grain family farms have a strong preference for agricultural subsidies, credit support, and technical support (the mean coefficient is greater than 0.8). Moreover, the preferences of grain family farms over the policy communication channel (the mean coefficient is greater than 0.5) cannot be ignored. Faced with the same policy attribute combination, grain family farms with high education levels, reasonable scales of operation, and good understanding of support policies are more likely to improve their profit margins. There are four preference types of grain family farms: finance preference (43.2%), knowledge and technology preference (28.5%), land transfer preference (15.4%), and policy information preference (12.9%). [ABSTRACT FROM AUTHOR]

Discrete choice experiments can be used to inform policy makers on people's preferences for landscapes and cultural ecosystem services. Recent studies have shown that the spatial context influences preferences and related willingness to pay values. In this paper we investigate the effect of the landscape surrounding people's places of residence on their willingness to pay using data from a discrete choice experiment on local land-use changes and cultural ecosystem services throughout Germany. For analysis, we apply a latent class logit model and include landscape categories as explanatory variables for class membership. We find that the different landscapes people live in are correlated with preferences. Especially people from urban areas and farm- and grassland landscapes have larger willingness to pay values for improvements in cultural ecosystem services than people from forest landscapes and cultural landscapes. The results are important for policy makers as different willingness to pay values in different landscapes imply different welfare effects for land use changes. Taking this information into account can help in reaching more efficient resource allocations. [ABSTRACT FROM AUTHOR]

Fostering the uptake of heating technologies based on renewable resources is an important part of the EU energy policy. Yet, despite efforts to promote their diffusion, heating systems based on fossil fuels are still predominant. In order to better tailor energy policies to citizens preferences, it is crucial to collect accurate information on their determinants of heating choices. At this purpose, we adopted a choice experiment and a latent class model to analyze preferences of householders in the Veneto region (North-East Italy) for different heating systems and their key features. We focused on three devices based on biomass and three on fossil fuels, and accounted for technical, economic and environmental characteristics of such systems. Model estimates highlight the presence of substantial preference heterogeneity among the population, which can be partially explained by citizens socio-demographics. We also use model outputs to simulate market shares for heating systems under alternative policy scenarios. Results provide interesting suggestions to inform the design of policies aimed at fostering the adoption of biomass-based heating systems. [ABSTRACT FROM AUTHOR]

Cystic fibrosis is a chronic lung disease requiring frequent lung-function monitoring to track acute respiratory events (pulmonary exacerbations). The association between lung-function trajectory and time-to-first exacerbation can be characterized using joint longitudinal-survival modeling. Joint models specified through the shared parameter framework quantify the strength of association between such outcomes but do not incorporate latent sub-populations reflective of heterogeneous disease progression. Conversely, latent class joint models explicitly postulate the existence of sub-populations but do not directly quantify the strength of association. Furthermore, choosing the optimal number of classes using established metrics like deviance information criterion is computationally intensive in complex models. To overcome these limitations, we integrate latent classes in the shared parameter joint model through a fully Bayesian approach. To choose the optimal number of classes, we construct a mixture model assuming more latent classes than present in the data, thereby asymptotically "emptying" superfluous latent classes, provided the Dirichlet prior on class proportions is sufficiently uninformative. Model properties are evaluated in simulation studies. Application to data from the US Cystic Fibrosis Registry supports the existence of three sub-populations corresponding to lung-function trajectories with high initial forced expiratory volume in 1 s (FEV1), rapid FEV1 decline, and low but steady FEV1 progression. The association between FEV1 and hazard of exacerbation was negative in each class, but magnitude varied. [ABSTRACT FROM AUTHOR]

The case-control study design is one of the main tools for detecting associations between genetic markers and diseases. It is well known that population substructure can lead to spurious association between disease status and a genetic marker if the prevalence of disease and the marker allele frequency vary across subpopulations. In this paper, we propose a novel statistical method to estimate the association in case-control studies with unmeasured population substructure. The proposed method takes two steps. First, the information on genomic markers and disease status is used to infer the population substructure; second, the association between the disease and the test marker adjusting for the population substructure is modeled and estimated parametrically through polytomous logistic regression. The performance of the proposed method, relative to the existing methods, on bias, coverage probability and computational time, is assessed through simulations. The method is applied to an end-stage renal disease study in African Americans population. [ABSTRACT FROM AUTHOR]

This paper discusses how National Statistical Institutes (NSI’s) can use hidden Markov models (HMMs) to produce consistent official statistics for categorical, longitudinal variables using inconsistent sources. Two main challenges are addressed: first, the reconciliation of inconsistent sources with multi-indicator HMMs requires linking the sources on the micro level. Such linkage might lead to bias due to linkage error. Second, applying and estimating HMMs regularly is a complicated and expensive procedure. Therefore, it is preferable to use the error parameter estimates as a correction factor for a number of years. However, this might lead to biased structural estimates if measurement error changes over time or if the data collection process changes. Our results on these issues are highly encouraging and imply that the suggested method is appropriate for NSI’s. Specifically, linkage error only leads to (substantial) bias in very extreme scenarios. Moreover, measurement error parameters are largely stable over time if no major changes in the data collection process occur. However, when a substantial change in the data collection process occurs, such as a switch from dependent (DI) to independent (INDI) interviewing, re-using measurement error estimates is not advisable. [ABSTRACT FROM AUTHOR]

Farmers' preferences and needs are crucial in improving the development of rice varieties to increase rice production and improve food security. However, research on farmers' preferences for crop attributes is scarce in Africa. This study contributes to bridging this gap by focusing on farmers' preferences for rice variety attributes based on a nationwide survey conducted in Sierra Leone. Results from a Best-Worst Scaling analysis revealed that potential yield, maturity, pest and disease resistance, and seed longevity, were the most preferred attributes of a rice variety. The least preferred attributes were ease of threshing, fertiliser response, and shattering. After applying a latent class model, farmers were found to align with six distinct classes: "majority farmers", "price sensitive", "conservationists", "sustainable farmers", "output maximisers", and "subsistence". These classes showed differences in terms of the farmers' characteristics (e.g. sex, education, income, farm farming experience, and farmland size) and in the importance given to extrinsic factors (e.g. access to market, extension services, and membership in farmers' organisations). Among these classes, the "majority farmers" were more likely to have relatively more farmland, easier access to other farm resources (e.g., chemical fertilisers and market information) and are more likely to prefer potential yield. The "price sensitive" class placed high importance on seed price and consisted of farmers who were low income, and had better access to extension services and membership to farming organisations. Furthermore, preferences were distinct for "conservationists" who have environmental and sustenance concerns. [ABSTRACT FROM AUTHOR]

Latent class models are now widely applied in health economics to analyse heterogeneity in multiple outcomes generated by subgroups of individuals who vary in unobservable characteristics, such as genetic information or latent traits. These models rely on the underlying assumption that associations between observed outcomes are due to their relationship to underlying subgroups, captured in these models by conditioning on a set of latent classes. This implies that outcomes are locally independent within a class. Local independence assumption, however, is sometimes violated in practical applications when there is uncaptured unobserved heterogeneity resulting in residual associations between classes. While several approaches have been proposed in the case of binary and continuous outcomes, little attention has been directed to the case of multiple ordered categorical outcome variables often used in health economics. In this paper, we develop an approach to test for the violation of the local independence assumption in the case of multiple ordered categorical outcomes. The approach provides a detailed decomposition of identified residual association by allowing it to vary across latent classes and between levels of the ordered categorical outcomes within a class. We show how this level of decomposition is important in the case of ordered categorical outcomes. We illustrate our approach in the context of health insurance and healthcare utilization in the US Medigap market. [ABSTRACT FROM AUTHOR]

When analysing drivers affecting the farm performance, the presence of different technologies should be taken into account. We assume that the technology used by crop farms is not the same for all producers and therefore we use latent class model to identify technological classes at first. Class definition is based on multidimensional classification and determination of indices given by the values of individual components. The principal components analysis is applied to estimate significant and robust weights for the index components. FADN (Farm Accountancy Data Network) database, Czech crop farms data from 2005 to 2017 were used and three groups of technology classes of farms were identified with a determinant influence of the structure index and localisation. The other indices characterise sustainability, innovation, technology, diversification, and individual characteristics. Three distinct classes of crop farms were found, one major class and two minor classes. Family driven farms are usually smaller farms in terms of acreage. Highly sustainable crop farms are most likely located in lower altitudes and not in less-favoured areas. Innovative farms are also likely to be more productive. The results indicate that agricultural production farms with a more sustainable way of farming are most likely to be more productive. [ABSTRACT FROM AUTHOR]

• Estimating travel mode and airline choice using air and non-air passengers' stated preference data. • Accommodating passengers' preference heterogeneity using latent class models. • Investigating the differences in travel mode choice, socio-demographics and trip characteristics among passenger segments. • Examining willingness to pay (WTP) of the travellers. This paper investigates travel mode and airline choice using stated-preference data. Both air and non-air passengers' data were collected in Western Australia, which allows easier generalisability of the findings to a wider population. A market segmentation approach, using latent class modelling, was applied to identify passenger segments. The results shown a significant difference between identified segments in terms of demographics, economics and trip characteristics. One segment, comprising travellers with higher income and more frequent business travellers, was sensitive to service-factors. The other, comprising personal travellers with lower incomes, placed higher sensitivity on price. The findings highlight the importance of understanding mode choice behaviour based on market segmentation and provides insights to policy-makers and airlines for developing more effective polices and strategies. [ABSTRACT FROM AUTHOR]

• Analysis of the transfer time for bus users with data collected from stated preference (SP) experiment. • Preference heterogeneity studied by estimating mixed logit and latent class models. • Willingness to pay for reducing transfer time and other attributes. • Elasticity values of current bus services. • Policy analysis of current bus services and their impact on demand of current bus service. This paper analyses travelers' preferences for the main attributes defining public bus transport services when evaluating connecting versus direct services in the island of Gran Canaria (Spain). The information is gathered through a Stated Preference experiment for bus users who make a transfer during their journey. In order to study the preference heterogeneity mixed logit and latent class models are estimated. The preference heterogeneity identified is different according to the estimated model. For Mixed Logit model, it is defined by type of transfer interacting with the transfer waiting time and for mandatory trips interacting with in-vehicle travel time. For Latent Class model, this preference heterogeneity is identified by three classes where the perception of the modal attributes is different in each class and non-compensatory behavior is identified. Moreover, the transfer waiting time is significant in all three classes. Thus, the results depend on the estimated model and a comparison is made with those results. The findings obtained seem to indicate that the improvement opportunities for transport systems should focus on the reduction in the transfer cost with the exception of individuals of class 2 and the improvement in the level-of-service. [ABSTRACT FROM AUTHOR]

This paper presents Hotelling T² as a procedure for the testing of significance difference between the item response probabilities (ωij's) of classes in a Latent Class Model (LCM). Parametric bootstrap technique is used in order to generate samples for ωij's. These samples are based on the estimated parameters of 2-class latent model. The estimation of parameters in either situation is done using the Expectation Maximization (EM) algorithm through Maximum likelihood method. The hypothesis under consideration is whether the response probabilities (ωij's) are equal against each item in both the classes. {H0:ωi1 = ωi1 against H1. If the test exhibits significant difference between response probabilities in both classes, it will be a clear indication of a presence of latent variable. We consider both training and testing data sets to develop the test. In order to apply Hotelling T² test the basic assumptions of normality and homogeneity of variance are also checked. Chi-square goodness of fit test is used for assessing normal distribution to be good fitted on the hypothesized (bootstrap samples) based on 2-class latent model parameters for each data and Bartlett test to check heterogeneity of variances in ωij's. Moreover, our procedure produces a minimum standard error of estimates as compared to those obtained through the package in R.Gui environment. [ABSTRACT FROM AUTHOR]

Whereas many studies on Internet gaming disorder (IGD) have used self-report questionnaires, only a few have adopted clinical interviews and samples. The current study aimed at using data from face-to-face diagnostic interviews, based on the criteria for IGD in the DSM-5, to determine the Internet, gaming, and smartphone usage patterns of children and adolescents. A latent class analysis was conducted using data collected through diagnostic interviews for Internet, gaming, and smartphone addiction with 190 participants (M = 13.14 years, SD = 2.46; 143 boys, 47 girls) who were part of a multicenter clinical cohort study. Participants were classified into four groups: pleasure-seeking (Class 1), internal-use (Class 2), problematic-use (Class 3), and pathological-use (Class 4). The pleasure-seeking group (8.11%) showed low tendencies in general and proper control. The internal-use group (17.63%) showed significant increases in "cognitive salience" and "craving," with strong internal desires. The problematic-use group (37.28%) had no "interference with role performance"; however, they displayed "difficulty regulating use" and "persistent use despite negative consequences," with a slight functional impairment. The pathological-use group (36.98%) scored the highest on all these items, revealing a severe functional impairment. Compared to the other groups, the pathological-use group had the highest depression and daily stress levels and displayed the lowest levels of happiness. This study provides basic data to elucidate Internet, gaming, and smartphone overuse patterns among children and adolescents, which could be used to develop differentiated intervention strategies for each group. [ABSTRACT FROM AUTHOR]

In this article, I describe the lclogit2 command, an enhanced version of lclogit (Pacifico and Yoo, 2013, Stata Journal 13: 625–639). Like its predecessor, lclogit2 uses the expectation-maximization algorithm to fit latent class conditional logit (LCL) models. But it executes the expectation-maximization algorithm's core algebraic operations in Mata, so it runs considerably faster as a result. It also allows linear constraints on parameters to be imposed more conveniently and flexibly. It comes with the parallel command lclogitml2, a new stand-alone command that uses gradient-based algorithms to fit LCL models. Both lclogit2 and lclogitml2 are supported by a new postestimation command, lclogitwtp2, that evaluates willingness-to-pay measures implied by fitted LCL models. [ABSTRACT FROM AUTHOR]

Annual vehicle kilometres travelled (VKT) is a long used index of car use. Usually, the annual VKT, as reported by respondents, is used for the analysis. But the reported values almost systematically contain approximations such as rounding and heaping. We apply a latent class approach in modelling VKT to account for this problem developed by Heitjan and Rubin (J Am Stat Assoc 85(410):304–314, 1990; Ann Stat 19(4):2244–2253, 1991). Our model takes the form of a mixture of ordered probit models. The level of coarseness in reporting is considered as a latent variable that determines a category the respondent may belong to. Ordered response probit models of VKT are developed for each category. Thresholds are predetermined and model the level of coarseness that relates to the category. Annual VKT is itself assumed to affect the level of coarseness in reporting, thus included as an explanatory variable of the latent coarseness model. It is also modelled by an ordered probit model. The data set used in this study is a panel data of French households' vehicle ownership (Parc-Auto panel survey). The results confirm that the longer VKT results in a larger coarseness in the report. The results also suggest that the coarseness in the report of VKT is larger for commuting car than others. The coefficient estimates on the VKT function are not statistically different from those estimated by conventional regression model of VKT. However, the estimated variance of the error term and the standard errors of the coefficient estimates in the VKT function for the proposed model are smaller than those for conventional regression model, implying that the proposed model is more efficient to investigate the effect of the explanatory variables on VKT than the conventional regression model. [ABSTRACT FROM AUTHOR]

The aim of the study is to present some discrete choice models that are less discussed in the Hungarian literature, using the database of an experiment on margarine consumption preferences as an example, and then compare their results. The estimated models include, in addition to the multinomial and random parameter logit models, a „semi-parametric" solution, the latent class model and its extension, the random parameter latent class models. Based on the results, it can be concluded that there are significant differences between the parameter and willingness-to-pay estimates of the models. [ABSTRACT FROM AUTHOR]

The diagnostic accuracy of a test or rater has a crucial impact on clinical decision making. The assessment of diagnostic accuracy for multiple tests or raters also merits much attention. A Bayesian hierarchical conditional independence latent class model for estimating sensitivities and specificities for a large group of tests or raters is proposed, which is applicable to both with-gold-standard and without-gold-standard situations. Through the hierarchical structure, not only are the sensitivities and specificities of individual tests estimated, but also the diagnostic performance of the whole group of tests. For a small group of tests or raters, the proposed model is further extended by introducing pairwise covariances between tests to improve the fitting and to allow for more modeling flexibility. Correlation residual analysis is applied to detect any significant covariance between multiple tests. Just Another Gibbs Sampler (JAGS) implementation is efficiently adopted for both models. Three real data sets from literature are analyzed to explicitly illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

We examine heterogeneous consumer preferences in Chinese milk markets. Using a discrete choice experiment, we examine how the brand, quality certification, traceability label and price influence consumers' milk choices. We identify four consumer segments using a latent class model: price conscious (9.8%), balanced thinking (19.8%), health conscious (57.5%), and environment conscious (12.9%) consumers. These four segments have distinct preferences: price conscious consumers prefer green certification; balanced thinking consumers have the highest willingness to pay for traceability labels; health conscious consumers have strong brand awareness; and environment conscious consumers prefer organic certification and traceability labels and use price as a quality signal. Such diversity of consumer preference can be explained by four psychological factors: price consciousness, food safety concerns, health consciousness and environmental concerns. [ABSTRACT FROM AUTHOR]

This study uses latent class modeling (LCM) to explore the effects of channel-specific perceptions, along with other variables, on purchase channel intention. Using data on book purchases collected from an Internet-based survey of two university towns in Northern California, we develop a latent class model with two segments (final N=373). Age turns out to be the only observed determinant of class membership, and in the intention model, the mostly-younger segment is more cost-sensitive and the mostly-older segment appears to be more convenience-sensitive. The results clearly demonstrate the effects on purchase intention of channel-specific perceptions, purchase experience, context and sociodemographics. Comparing the LCM to the unsegmented model and to models deterministically segmented on age indicates that the LCM is slightly better from the statistical perspective, but arguably weaker from the conceptual perspective. However, a model that interacts age with the explanatory variables in the conventional unsegmented model outperforms all the others (though not overwhelmingly so), including the LCM. Thus, our results suggest that using LCM as an initial stage in model exploration allows us to more intelligently specify a model where the taste heterogeneity is (potentially) specified deterministically in the end, which often yields a more parsimonious model, and may in fact fit the data better.

This paper uses a market segmentation approach to examine the effects of digital disruption in the book industry. A Bayesian efficient experimental design is employed in order to develop a stated preference discrete choice experiment that incorporates an array of book formats and attributes. The experiment was conducted on a panel of Australian readers. The results were analysed using a latent class model, leading to the identification of three distinct classes of book readers. The largest class of readers is comprised of 'technological adopters' who demonstrate a similar willingness to read on both traditional printed book formats and newer digital ones. Members of this class rely on critical review scores to make purchasing decisions and are the youngest of the three classes. Second is a class of 'popular readers.' This group consists of price-sensitive readers who clearly favour reading popular fiction on traditional paper-based book formats only. The third group are 'avid readers' who exhibit the highest willingness to pay for books and show a desire to read books of all genres. The identification of discrete segments of readers, along with their associated price elasticities and willingness to pay figures, can assist book industry stakeholders (such as book publishers and sellers) with the development of effective strategies to guide them through the various stages of digital book formats technology adoption life cycle. Such results can also be used to help steer cultural policy makers during a period of rapid technological change and uncertainty. [ABSTRACT FROM AUTHOR]

• The paper compares choice models for products that are mixtures. • The paper demonstrates how preference heterogeneity can be studied. • The paper demonstrates modern approaches to identify consumer segments. • The paper determines mixture formulations that optimize consumer preferences. • The paper also shows that multiple objectives can be optimized simultaneously. Data from choice experiments are analyzed using choice models. Proper modeling allows a good understanding of the consumer preferences, the identification of consumer segments and the optimization of products, and, eventually, offers opportunities for competitive advantage. However, in the analysis of choice data, products that are mixtures of ingredients have been largely overlooked. In this paper, we combine traditional choice models and traditional mixture models. We apply the resulting mixture choice models to data from a real-life experiment in which consumers made pair-wise comparisons involving seven cocktails. For the choice models, we first assume consumer homogeneity. Next, we allow for heterogeneity among individuals. Therefore, we discuss the multinomial logit model, the mixed logit model and the latent class model. For identifying segments, besides the latent class model, we explore various two-stage segmentation approaches in which forces, hierarchical Bayes estimates and Firth individual-level estimates are used as input for a cluster analysis. The results show that the mixed logit model and the latent class model describe the data better than the multinomial logit model. The optimal cocktails for the segments we identified using the various methods are not identical, but show many similarities. [ABSTRACT FROM AUTHOR]

Summary: We propose a latent class mixture growth model with concomitant variables to study the time profiles of international remittances sent by first‐generation migrants in Germany from 1996 to 2012. The latent class approach enables us to identify homogeneous subgroups of migrants associated with different trajectories for their remitting behaviour, which can be interpreted in the light of the theoretical economic background. In addition, the inclusion of concomitant covariates allows us to uncover whether the assignment of migrants to a specific subgroup can be ascribed to their observable characteristics (e.g. their intention to return home), as conjectured by the theoretical models. The model proposed is easily estimated through an expectation–maximization algorithm. Results show that migrants can be clustered in three groups, two of which reflect the evolution of remittances predicted by economic theory. [ABSTRACT FROM AUTHOR]

Governments worldwide have embraced mandatory environmental information disclosure as a vital and indispensable component of their strategies to improve environmental conditions. However, the pathways through which environmental information disclosure can effectively mobilize public support for environmental management initiatives is not well understood. On the basis of latent class modelling of discrete choice experiment survey data in Shaoguan city (Guangdong province, south China), we investigate the impacts of passive disclosure (public simply receive environmental information) and active disclosure (public purposefully interact with environmental information providers) on residents' heterogeneous preferences pertaining to an urban river restoration project. We find that passive environmental information transparency could successfully motivate public participation and support, while active environmental information accessibility tends to hinder public participation. Respondents either act as non-supporters, or fail to lucidly convey their preferences, if they have directly contacted local authorities or attended environmental events to purposefully collect environmental information via two-way interactive communications. The positive and favourable effect of passive environmental information disclosure on public environmental participation is notable. Yet, if we fail to enhance the effectiveness of active environmental information disclosure, we will miss a unique opportunity to transform public environmental awareness and interest into a useful catalyst for aligning public support with governmental environmental management goals. • It explores the impacts of environmental information disclosure on residents' environmental decisions. • Latent class modelling reveals the source of societal preferences for urban river restoration. • Passive environmental information accessibility can motivate public participation and support for urban river restoration. • The function of active environmental information accessibility tends to be unsatisfactory. • Prompt and cooperative responses of governments to public environmental information requests should be enhanced. [ABSTRACT FROM AUTHOR]