Bayesian modeling of spatial ordinal data from health surveys.

Health surveys allow exploring health indicators that are of great value from a public health point of view and that cannot normally be studied from regular health registries. These indicators are usually coded as ordinal variables and may depend on covariates associated with individuals. In this article, we propose a Bayesian individual-level model for small-area estimation of survey-based health indicators. A categorical likelihood is used at the first level of the model hierarchy to describe the ordinal data, and spatial dependence among small areas is taken into account by using a conditional autoregressive distribution. Post-stratification of the results of the proposed individual-level model allows extrapolating the results to any administrative areal division, even for small areas. We apply this methodology to describe the geographical distribution of a self-perceived health indicator from the Health Survey of the Region of Valencia (Spain) for the year 2016.

Determinants of depressive and alcohol use disorders among female sex workers in Ethiopia: evidence from a national bio-behavioral survey, 2020.

BACKGROUND: Female sex workers (FSWs) face an elevated risk of developing mental health disorders and alcohol use disorders (AUD), which in turn increase their vulnerability to HIV and other sexually transmitted infections (STIs) and other negative outcomes. To effectively address both of these health issues, it is crucial to understand the shared key determinants underlying these illnesses, which is a substantial knowledge gap in Ethiopia and elsewhere in the world. Therefore, this study aimed to identify the common key determinants of depression and AUD among FSWs in Ethiopia using a bivariate multivariable ordinal logistic model. METHODS: We analyzed cross-sectional biobehavioral data collected in 2020 from 16 cities and major towns in Ethiopia using the respondent-driven sampling (RDS) technique, which involved a total of 6,085 FSWs. FSWs who had lived at the study sites for at least a month before the study period were deemed eligible for recruitment. Major depressive disorder (DD) and AUD were screened using the Patient Health Questionnaire (PHQ9) and alcohol use disorder identification test (AUDIT), respectively. We used descriptive statistics to summarize study population characteristics and bivariate multivariable ordinal logistic regression (BMOLR) to identify common determinants of DD and AUD combined and their nonnormal correlation. RESULTS: Among 6085 FSWs screened for DD and AUD, 13.5% and 4.0% have met the criteria for moderate and severe depressive disorder, respectively, and 20.3% and 34.7% have met the AUDIT criteria for harmful or hazardous behavior and alcohol dependence, respectively. FSW with experience of inconsistent condom use, condom failure, violence, mobility, use of any drugs, non-paying partners, abortion, and selling sex for more than five years were associated with an increase in the severity of both disorders. A high average income from selling sex and the number of paying partners reduced the severity of depression and increased the level of alcohol dependence. Being HIV positive and ever having anal sex were associated only with an increase in depression. CONCLUSION: Major DD and AUD are prevalent among FSWs in Ethiopia. The findings revealed that common key determinants, which exacerbated the severity of both disorders, were also risk factors for HIV and other STIs. Consequently, integrated STI strategies are essential in the screening, referral, and treatment of depression and AUD. Intervention packages should encompass determinants of depression and AUD, including condom utilization, drug use, mobility between towns, abortion, violence, and counseling services. Additionally, strategies to ensure economic security should be incorporated.

The Bergen-Yale Sexual Addiction Scale (BYSAS): Longitudinal Measurement Invariance Across a Two-Year Interval.

The Bergen-Yale Sexual Addiction Scale (BYSAS; [1]) is arguably the most popular questionnaire at present for assessing sex addiction. Employing Confirmatory Factor Analysis (CFA) and treating item scores as ordered categorical, we applied Weighted Least Square Mean and Variance Adjusted Chi-Square (WLSMV) extraction to investigate the longitudinal measurement and structural invariance of ratings on the BYSAS among 276 adults (mean = 31.86 years; SD = 9.94 years; 71% male) over a two-year period, with ratings at three yearly intervals. Overall, there was support for configural invariance, full loading, full threshold, the full unique factor invariance; and all structural (latent variances and covariances) components. Additionally, there was no difference in latent mean scores across the three-time points. The psychometric and practical implications of the findings are discussed.

Multivariate disease progression modeling with longitudinal ordinal data.

Disease modeling is an essential tool to describe disease progression and its heterogeneity across patients. Usual approaches use continuous data such as biomarkers to assess progression. Nevertheless, categorical or ordinal data such as item responses in questionnaires also provide insightful information about disease progression. In this work, we propose a disease progression model for ordinal and categorical data. We built it on the principles of disease course mapping, a technique that uniquely describes the variability in both the dynamics of progression and disease heterogeneity from multivariate longitudinal data. This extension can also be seen as an attempt to bridge the gap between longitudinal multivariate models and the field of item response theory. Application to the Parkinson's progression markers initiative cohort illustrates the benefits of our approach: a fine-grained description of disease progression at the item level, as compared to the aggregated total score, together with improved predictions of the patient's future visits. The analysis of the heterogeneity across individual trajectories highlights known disease trends such as tremor dominant or postural instability and gait difficulties subtypes of Parkinson's disease.

Joint models for the longitudinal analysis of measurement scales in the presence of informative dropout.

In health cohort studies, repeated measures of markers are often used to describe the natural history of a disease. Joint models allow to study their evolution by taking into account the possible informative dropout usually due to clinical events. However, joint modeling developments mostly focused on continuous Gaussian markers while, in an increasing number of studies, the actual quantity of interest is non-directly measurable; it constitutes a latent variable evaluated by a set of observed indicators from questionnaires or measurement scales. Classical examples include anxiety, fatigue, cognition. In this work, we explain how joint models can be extended to the framework of a latent quantity measured over time by indicators of different nature (e.g. continuous, binary, ordinal). The longitudinal submodel describes the evolution over time of the quantity of interest defined as a latent process in a structural mixed model, and links the latent process to each observation of the indicators through appropriate measurement models. Simultaneously, the risk of multi-cause event is modelled via a proportional cause-specific hazard model that includes a function of the mixed model elements as linear predictor to take into account the association between the latent process and the risk of event. Estimation, carried out in the maximum likelihood framework and implemented in the R-package JLPM, has been validated by simulations. The methodology is illustrated in the French cohort on Multiple-System Atrophy (MSA), a rare and fatal neurodegenerative disease, with the study of dysphagia progression over time stopped by the occurrence of death.

Comparing healthcare quality: A common framework for both ordinal and cardinal data with an application to primary care variation in England.

The paper proposes a framework for comparing the quality of healthcare providers and assessing the variation in quality between them, which is directly applicable to both ordinal and cardinal quality data on a comparable basis. The resultant measures are sensitive to the full distribution of quality scores for each provider, not just the mean or the proportion meeting some binary quality threshold, thereby making full use of the multicategory response data increasingly available from patient experience surveys. The measures can also be standardized for factors such as age, sex, ethnicity, health and deprivation using a distribution regression model. We illustrate by measuring the quality of primary care services in England in 2019 using three different sources of publicly available, general practice-level information: multicategory response patient experience data, ordinal inspection ratings and cardinal clinical achievement scores. We find considerable variation at both local and regional levels using all three data sources. However, the correlation between the comparative quality indices calculated using the alternative data sources is weak, suggesting that they capture different aspects of general practice quality.

Robust estimates of regional treatment effects in multiregional randomized clinical trials with ordinal responses.

Global clinical trials involving multiple regions are common in current drug development processes. Determining the regional treatment effects of a new therapy over an existing therapy is important to both the sponsors and the regulatory agencies in the regions. Existing methods are mainly for continuous primary endpoints and use subjectively specified models, which may deviate from the true model. Here, we consider trials that have ordinal responses as the primary endpoint. This article extends the recently developed robust semiparametric ordinal regression model to estimate regional treatment effects, in which the regression coefficients and regional effects are modeled parametrically for ease of interpretation, and the regression link function is specified nonparametrically for robustness. The model parameters are estimated by semiparametric maximum likelihood estimation, and the null hypothesis of no regional effect is tested by the Wald test. Simulation studies are conducted to evaluate the performance of the proposed method and compare it with the commonly used parametric model. The results of the former show an improved overall performance over the latter. In particular, the model yields much higher precision in estimation and prediction than the fixed-link model. This result is especially appealing since our interest is to estimate the treatment effect more efficiently and the estimand is of particular interest in multiregional clinical trials. We then apply the method by analyzing real multiregional clinical trials with ordinal responses as their primary endpoint.

The importance of using ordinal scores for patient classification based on health-related quality of life trajectories.

Changes in health-related quality of life (HRQoL) over time are not necessarily homogeneous within a population of interest. Our study aim was twofold: to determine homogeneous patient subpopulations distinguished by HRQoL trajectories, and to identify the particular patient profile associated with each subpopulation. To classify patients according to HRQoL dimension scores, we compared mixtures of linear mixed models (LMMs) classically applied to scores defined by the EORTC procedure, and mixtures of random effect cumulative models (CMs) applied to scores treated as ordinal variables. A simulation study showed that the mixture of LMMs overestimated the number of subpopulations and was less able to correctly classify patients than the mixture of CMs. Considering HRQoL scores as ordinal rather than continuous variables is relevant when classifying patients. The mixture of CMs for ordinal scores is able to identify homogeneous subpopulations and their associated trajectories. The application focused on changes over time in HRQoL data (collected using the EORTC QLQ-C30 questionnaire) from 132 breast cancer patients from the Moral study. Once the classification is obtained only from HRQoL scores, class membership was then explained through a logistic regression model, given a large panel of variables collected at baseline. Analysis of data revealed that deterioration over time of role functioning and insomnia was closely related to patient anxiety: anxiety at baseline is a prognostic factor for a poor level and/or a deterioration over time of HRQoL. For functional dimensions, large tumor size and high education level were associated with worse HRQoL scores.

A hybrid approach for the analysis of complex categorical data structures: assessment of latent distance learning perception in higher education.

A long tradition of analysing ordinal response data deals with parametric models, which started with the seminal approach of cumulative models. When data are collected by means of Likert scale survey questions in which several scored items measure one or more latent traits, one of the sore topics is how to deal with the ordered categories. A stacked ensemble (or hybrid) model is introduced in the proposal to tackle the limitations of summing up the items. In particular, multiple items responses are synthesised into a single meta-item, defined via a joint data reduction approach; the meta-item is then modelled according to regression approaches for ordered polytomous variables accounting for potential scaling effects. Finally, a recursive partitioning method yielding trees provides automatic variable selection. The performance of the method is evaluated empirically by using a survey on Distance Learning perception.

Best Practices for Binary and Ordinal Data Analyses.

The measurement of many human traits, states, and disorders begins with a set of items on a questionnaire. The response format for these questions is often simply binary (e.g., yes/no) or ordered (e.g., high, medium or low). During data analysis, these items are frequently summed or used to estimate factor scores. In clinical applications, such assessments are often non-normally distributed in the general population because many respondents are unaffected, and therefore asymptomatic. As a result, in many cases these measures violate the statistical assumptions required for subsequent analyses. To reduce the influence of the non-normality and quasi-continuous assessment, variables are frequently recoded into binary (affected-unaffected) or ordinal (mild-moderate-severe) diagnoses. Ordinal data therefore present challenges at multiple levels of analysis. Categorizing continuous variables into ordered categories typically results in a loss of statistical power, which represents an incentive to the data analyst to assume that the data are normally distributed, even when they are not. Despite prior zeitgeists suggesting that, e.g., variables with more than 10 ordered categories may be regarded as continuous and analyzed as if they were, we show via simulation studies that this is not generally the case. In particular, using Pearson product-moment correlations instead of maximum likelihood estimates of polychoric correlations biases the estimated correlations towards zero. This bias is especially severe when a plurality of the observations fall into a single observed category, such as a score of zero. By contrast, estimating the ordinal correlation by maximum likelihood yields no estimation bias, although standard errors are (appropriately) larger. We also illustrate how odds ratios depend critically on the proportion or prevalence of affected individuals in the population, and therefore are sub-optimal for studies where comparisons of association metrics are needed. Finally, we extend these analyses to the classical twin model and demonstrate that treating binary data as continuous will underestimate genetic and common environmental variance components, and overestimate unique environment (residual) variance. These biases increase as prevalence declines. While modeling ordinal data appropriately may be more computationally intensive and time consuming, failing to do so will likely yield biased correlations and biased parameter estimates from modeling them.

Upper bound estimators of the population size based on ordinal models for capture-recapture experiments.

Capture-recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health, and biodiversity are just a few examples. The estimation of the number of unseen units has been a challenge for theoretical statisticians, and considerable progress has been made in providing lower bound estimators for the population size. In fact, it is well known that consistent estimators for this cannot be provided in the very general case. Considering a case where capture-recapture studies are summarized by a frequency of frequencies distribution, we derive a simple upper bound of the population size based on the cumulative distribution function. We introduce two estimators of this bound, without any specific parametric assumption on the distribution of the observed frequency counts. The behavior of the proposed estimators is investigated using several benchmark datasets and a large-scale simulation experiment based on the scheme discussed by Pledger.

Intuitionistic Fuzzy Synthetic Measure on the Basis of Survey Responses and Aggregated Ordinal Data.

The paper addresses the problem of complex socio-economic phenomena assessment using questionnaire surveys. The data are represented on an ordinal scale; the object assessments may contain positive, negative, no answers, a "difficult to say" or "no opinion" answers. The general framework for Intuitionistic Fuzzy Synthetic Measure (IFSM) based on distances to the pattern object (ideal solution) is used to analyze the survey data. First, Euclidean and Hamming distances are applied in the procedure. Second, two pattern object constructions are proposed in the procedure: one based on maximum values from the survey data, and the second on maximum intuitionistic values. Third, the method for criteria comparison with the Intuitionistic Fuzzy Synthetic Measure is presented. Finally, a case study solving the problem of rank-ordering of the cities in terms of satisfaction from local public administration obtained using different variants of the proposed method is discussed. Additionally, the comparative analysis results using the Intuitionistic Fuzzy Synthetic Measure and the Intuitionistic Fuzzy TOPSIS (IFT) framework are presented.

Bayesian joint modeling of ordinal longitudinal measurements and competing risks survival data for analysing Tehran Lipid and Glucose Study.

In this paper, joint modeling of longitudinal ordinal measurements and time to some events of interest as competing risks is discussed. For this purpose, a latent variable sub-model under linear mixed-effects assumption is considered for modeling ordinal longitudinal measurements. Also, a Weibull cause-specific sub-model is used to model competing risks data. These two sub-models are simultaneously considered in a unique model by a shared parameter model framework. Some simulation studies are performed for illustration of the proposed approaches; also, the proposed approaches are used for analyzing 15 years of lipid and glucose follow-up study in Tehran.

Testing Measurement Invariance with Ordinal Missing Data: A Comparison of Estimators and Missing Data Techniques.

Ordinal missing data are common in measurement equivalence/invariance (ME/I) testing studies. However, there is a lack of guidance on the appropriate method to deal with ordinal missing data in ME/I testing. Five methods may be used to deal with ordinal missing data in ME/I testing, including the continuous full information maximum likelihood estimation method (FIML), continuous robust FIML (rFIML), FIML with probit links (pFIML), FIML with logit links (lFIML), and mean and variance adjusted weight least squared estimation method combined with pairwise deletion (WLSMV_PD). The current study evaluates the relative performance of these methods in producing valid chi-square difference tests ([Formula: see text]) and accurate parameter estimates. The result suggests that all methods except for WLSMV_PD can reasonably control the type I error rates of [Formula: see text] tests and maintain sufficient power to detect noninvariance in most conditions. Only pFIML and lFIML yield accurate factor loading estimates and standard errors across all the conditions. Recommendations are provided to researchers based on the results.

Validity of a questionnaire developed to measure the impact of a high-fidelity simulation intervention: A feasibility study.

AIM: To evaluate the validity and responsiveness of a questionnaire developed to measure the impact of a high-fidelity simulation intervention. DESIGN: A pre- and postintervention design. METHODS: In August 2017, 107 participants completed a questionnaire measuring knowledge and perceived self-confidence pre- and postintervention. Validity of the questionnaire was determined by expert reviews, individual interviews and estimates of the changes in knowledge and perceived self-confidence. The changes were estimated by the differences between paired proportions of participants. The responsiveness of the ordered categorical item scores on self-confidence was evaluated by the measure of systematic group change and individual variations. RESULTS: The analysis of the interviews resulted in three themes: item content, item style and the administration of the questionnaire. An intervention effect on knowledge assessments was shown by the changes in paired proportions of participants with increased or decreased correct assessments (ranging from -25.5 - 24.8 percentage units). The responsiveness of the self-confidence scale was confirmed by evidence of post-intervention systematic group changes towards higher levels. CONCLUSION: This study provides useful experience for a forthcoming randomized controlled study to evaluate the effect of high-fidelity simulation on undergraduate nursing students' knowledge and self-confidence when assessing patient deterioration. IMPACT: Cause-and-effect relationship between simulation and learning is required to improve nursing education. A statistically significant rise in students' knowledge and levels of self-confidence after simulation were identified in this study. The study provided important aspects of future research study designs.

Evaluating methods for handling missing ordinal data in structural equation modeling.

Missing ordinal data are common in studies using structural equation modeling (SEM). Although several methods for dealing with missing ordinal data have been available, these methods often have not been systematically evaluated in SEM. In this study, we used Monte Carlo simulation to evaluate and compare five existing methods, including one direct robust estimation method and four multiple imputation methods, to deal with missing ordinal data. On the basis of the simulation results, we provide practical guidance to researchers in terms of the best way to deal with missing ordinal data in SEM.

Robustness of Parameter Estimation to Assumptions of Normality in the Multidimensional Graded Response Model.

A central assumption that is implicit in estimating item parameters in item response theory (IRT) models is the normality of the latent trait distribution, whereas a similar assumption made in categorical confirmatory factor analysis (CCFA) models is the multivariate normality of the latent response variables. Violation of the normality assumption can lead to biased parameter estimates. Although previous studies have focused primarily on unidimensional IRT models, this study extended the literature by considering a multidimensional IRT model for polytomous responses, namely the multidimensional graded response model. Moreover, this study is one of few studies that specifically compared the performance of full-information maximum likelihood (FIML) estimation versus robust weighted least squares (WLS) estimation when the normality assumption is violated. The research also manipulated the number of nonnormal latent trait dimensions. Results showed that FIML consistently outperformed WLS when there were one or multiple skewed latent trait distributions. More interestingly, the bias of the discrimination parameters was non-ignorable only when the corresponding factor was skewed. Having other skewed factors did not further exacerbate the bias, whereas biases of boundary parameters increased as more nonnormal factors were added. The item parameter standard errors recovered well with both estimation algorithms regardless of the number of nonnormal dimensions.

Item response models for the longitudinal analysis of health-related quality of life in cancer clinical trials.

BACKGROUND: The use of health-related quality of life (HRQoL) as an endpoint in cancer clinical trials is growing rapidly. Hence, research into the statistical approaches used to analyze HRQoL data is of major importance, and could lead to a better understanding of the impact of treatments on the everyday life and care of patients. Amongst the models that are used for the longitudinal analysis of HRQoL, we focused on the mixed models from item response theory, to directly analyze raw data from questionnaires. METHODS: We reviewed the different item response models for ordinal responses, using a recent classification of generalized linear models for categorical data. Based on methodological and practical arguments, we then proposed a conceptual selection of these models for the longitudinal analysis of HRQoL in cancer clinical trials. RESULTS: To complete comparison studies already present in the literature, we performed a simulation study based on random part of the mixed models, so to compare the linear mixed model classically used to the selected item response models. As expected, the sensitivity of the item response models to detect random effects with lower variance is better than that of the linear mixed model. We then used a cumulative item response model to perform a longitudinal analysis of HRQoL data from a cancer clinical trial. CONCLUSIONS: Adjacent and cumulative item response models seem particularly suitable for HRQoL analysis. In the specific context of cancer clinical trials and the comparison between two groups of HRQoL data over time, the cumulative model seems to be the most suitable, given that it is able to generate a more complete set of results and gives an intuitive illustration of the data.

Correlated probit analysis of repeatedly measured ordinal and continuous outcomes with application to the Health and Retirement Study.

The Health and Retirement Study was designed to evaluate changes in health and labor force participation during and after the transition from working to retirement. Every 2 years, participants provided information about their self-rated health (SRH), body mass index (BMI), smoking status, and other characteristics. Our goal was to assess the effects of smoking and gender on trajectories of change in BMI and SRH over time. Joint longitudinal analysis of outcome measures is preferable to separate analyses because it allows to account for the correlation between the measures, to test the effects of predictors while controlling type I error, and potentially to improve efficiency. However, because SRH is an ordinal measure while BMI is continuous, formulating a joint model and parameter estimation is challenging. A joint correlated probit model allowed us to seamlessly account for the correlations between the measures over time. Established estimating procedures for such models are based on quasi-likelihood or numerical approximations that may be biased or fail to converge. Therefore, we proposed a novel expectation-maximization algorithm for parameter estimation and a Monte Carlo bootstrap approach for standard errors approximation. Expectation-maximization algorithms have been previously considered for combinations of binary and/or continuous repeated measures; however, modifications were needed to handle combinations of ordinal and continuous responses. A simulation study demonstrated that the algorithm converged and provided approximately unbiased estimates with sufficiently large sample sizes. In the Health and Retirement Study, male gender and smoking were independently associated with steeper deterioration in self-rated health and with lower average BMI. Copyright © 2016 John Wiley & Sons, Ltd.

A goodness-of-fit test for the ordered stereotype model.

This paper presents a new goodness-of-fit test for an ordered stereotype model used for an ordinal response variable. The proposed test is based on the well-known Hosmer-Lemeshow test and its version for the proportional odds regression model. The latter test statistic is calculated from a grouping scheme assuming that the levels of the ordinal response are equally spaced which might be not true. One of the main advantages of the ordered stereotype model is that it allows us to determine a new uneven spacing of the ordinal response categories, dictated by the data. The proposed test takes the use of this new adjusted spacing to partition data. A simulation study shows good performance of the proposed test under a variety of scenarios. Finally, the results of the application in two examples are presented. Copyright © 2016 John Wiley & Sons, Ltd.