The impact of enhanced Milieu teaching with phonological emphasis (EMT + PE) on the speech and language outcomes for toddlers with cleft palate in Brazil and the United States of America.

PURPOSE: The purpose of this study was to compare the speech and language outcomes of children with cleft palate with or without cleft lip (CP+/-L) in the USA to children with CP+/-L in Brazil who underwent intervention with enhanced Milieu teaching with phonological emphasis (EMT + PE), as there are few cross-country intervention comparisons for children with CP+/-L. METHOD: This is a retrospective analysis of 29 participants from the USA and 24 participants from Brazil who were matched on age. The US participants were between the ages of 13-35 months (M = 23.76), spoke Standard American English in the home, and were recruited from East Tennessee State University and Vanderbilt University. The Brazilian participants were between the ages of 20-34 months (M = 25.04), spoke Brazilian Portuguese in the home, and were recruited from the Hospital de Reabilitação de Anomalias Craniofaciais-Universidade de São Paulo. All treatment participants received EMT + PE from trained speech-language pathologists in hospital-university clinics. RESULT: The treatment groups demonstrated greater gains than comparison groups in percent consonants correct, number of different words, and expressive/receptive vocabulary. There was no main effect nor interaction by country. CONCLUSION: The application of EMT + PE in a second culture and language is a viable early intervention option for participants with CP+/-L.

A novel nonparametric time-dependent precision-recall curve estimator for right-censored survival data.

In order to assess prognostic risk for individuals in precision health research, risk prediction models are increasingly used, in which statistical models are used to estimate the risk of future outcomes based on clinical and nonclinical characteristics. The predictive accuracy of a risk score must be assessed before it can be used in routine clinical decision making, where the receiver operator characteristic curves, precision-recall curves, and their corresponding area under the curves are commonly used metrics to evaluate the discriminatory ability of a continuous risk score. Among these the precision-recall curves have been shown to be more informative when dealing with unbalanced biomarker distribution between classes, which is common in rare event, even though except one, all existing methods are proposed for classic uncensored data. This paper is therefore to propose a novel nonparametric estimation approach for the time-dependent precision-recall curve and its associated area under the curve for right-censored data. A simulation is conducted to show the better finite sample property of the proposed estimator over the existing method and a real-world data from primary biliary cirrhosis trial is used to demonstrate the practical applicability of the proposed estimator.

Performance of diagnostic tests based on continuous bivariate markers.

In medical diagnostic research, it is customary to collect multiple continuous biomarker measures to improve the accuracy of diagnostic tests. A prevalent practice is to combine the measurements of these biomarkers into one single composite score. However, incorporating those biomarker measurements into a single score depends on the combination of methods and may lose vital information needed to make an effective and accurate decision. Furthermore, a diagnostic cut-off is required for such a combined score, and it is difficult to interpret in actual clinical practice. The paper extends the classical biomarkers' accuracy and predictive values from univariate to bivariate markers. Also, we will develop a novel pseudo-measures system to maximize the vital information from multiple biomarkers. We specified these pseudo-and-or classifiers for the true positive rate, true negative rate, false-positive rate, and false-negative rate. We used them to redefine classical measures such as the Youden index, diagnostics odds ratio, likelihood ratios, and predictive values. We provide optimal cut-off point selection based on the modified Youden index with numerical illustrations and real data analysis for this paper's newly developed pseudo measures.

Time-dependent receiver operating characteristic curve estimator for correlated right-censored time-to-event data.

In clinical trials, evaluating the accuracy of risk scores (markers) derived from prognostic models for prediction of survival outcomes is of major concern. The time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve are appealing measures to evaluate the predictive accuracy. Several estimation methods have been proposed in the context of classical right-censored data which assumes the event time of individuals are independent. In many applications, however, this may not hold true if, for example, individuals belong to clusters or experience recurrent events. Estimates may be biased if this correlated nature is not taken into account. This paper is then aimed to fill this knowledge gap to introduce a time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve estimation method for right-censored data that take the correlated nature into account. In the proposed method, the unknown status of censored subjects is imputed using conditional survival functions given the marker and frailty of the subjects. An extensive simulation study is conducted to evaluate and demonstrate the finite sample performance of the proposed method. Finally, the proposed method is illustrated using two real-world examples of lung cancer and kidney disease.

Role of gender in explaining metabolic syndrome risk factors in an Iranian rural population using structural equation modelling.

Many factors can lead to an increase in the prevalence of metabolic syndrome (MetS) in different populations. Using an advanced structural equation model (SEM), this study is aimed to determine the most important risk factors of MetS, as a continuous latent variable, using a large number of males and females. We also aimed to evaluate the interrelations among the associated factors involved in the development of MetS. This study used data derived from the Fasa PERSIAN cohort study, a branch of the PERSIAN cohort study, for participants aged 35 to 70 years with 10,138 males and females. SEM was used to evaluate the direct and indirect effects, as well as gender effects of influencing factors. Results from the SEM showed that in females most changes in MetS are described by waist circumference (WC), followed by hypertension (HP) and triglyceride (TG), while in males most changes in MetS are described by WC, followed by TG then fasting blood glucose (FBG). Results from the SEM confirmed the gender effects of social status on MetS, mediated by sleep and controlled by age, BMI, ethnicity and physical activity. This study also shows that the integration of TG and WC within genders could be useful as a screening criterion for MetS in our study population.

Correlates of illness uncertainty in cancer survivors and family caregivers: a systematic review and meta-analysis.

PURPOSE: Illness uncertainty is widely recognized as a psychosocial stressor for cancer survivors and their family caregivers. This systematic review and meta-analysis aimed to identify the sociodemographic, physical, and psychosocial correlates that are associated with illness uncertainty in adult cancer survivors and their family caregivers. METHODS: Six scholarly databases were searched. Data synthesis was based on Mishel's Uncertainty in Illness Theory. Person's r was used as the effect size metric in the meta-analysis. Risk of bias was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. RESULTS: Of 1116 articles, 21 articles met the inclusion criteria. Of 21 reviewed studies, 18 focused on cancer survivors, one focused on family caregivers, and 2 included survivors and family caregivers. Findings identified distinct correlates for illness uncertainty in cancer survivors, including sociodemographic factors (e.g., age, gender, race), stimuli frame (e.g., symptom, family history of cancer), structure providers (e.g., education), coping, and adaptation. Notable effect sizes were observed in the correlations between illness uncertainty and social support, quality of life, depression, and anxiety. Caregivers' illness uncertainty was associated with their race, general health, perception of influence, social support, quality of life, and survivors' prostate-specific antigen levels. Insufficient data precluded examining effect size of correlates of illness uncertainty among family caregivers. CONCLUSION: This is the first systematic review and meta-analysis to summarize the literature on illness uncertainty among adult cancer survivors and family caregivers. Findings contribute to the growing literature on managing illness uncertainty among cancer survivors and family caregivers.

Semiparametric inference for the scale-mixture of normal partial linear regression model with censored data.

In the censored data exploration, the classical linear regression model which assumes normally distributed random errors is perhaps one of the commonly used frameworks. However, practical studies have often criticized the classical linear regression model because of its sensitivity to departure from the normality and partial nonlinearity. This paper proposes to solve these potential issues simultaneously in the context of the partial linear regression model by assuming that the random errors follow a scale-mixture of normal (SMN) family of distributions. The postulated method allows us to model data with great flexibility, accommodating heavy tails and outliers. By implementing the B-spline approximation and using the convenient hierarchical representation of the SMN distributions, a computationally analytical EM-type algorithm is developed for obtaining maximum likelihood (ML) parameter estimates. Various simulation studies are conducted to investigate the finite sample properties, as well as the robustness of the model in dealing with the heavy tails distributed datasets. Real-world data examples are finally analyzed for illustrating the usefulness of the proposed methodology.

Measurement invariance of the Kessler Psychological Distress Scale (K10) among children of Chinese rural-to-urban migrant workers.

INTRODUCTION: Kessler Psychological Distress Scale (K10) is a 10-item screening tool designed for nonspecific psychological distress. The current study aims to identify a best-fitting factor structure of the K10, and to test its cross-gender measurement invariance based on the structure. METHODS: Using convenience sampling, we included 339 (n = 192 for boys and 135 for girls) children of Chinese rural-to-urban migrant workers in Hangzhou, China. RESULTS: Confirmatory factor analysis for ordered-categorical measures revealed a two-factor structure as the best-fitting model, in which five items (hopeless, depressed, effort, severely depressed, and worthless) loaded on depression and the other five items loaded on anxiety (tired, nervous, severely nervous, restless, and severely restless). The model held at different levels of the measurement invariance testing, that is, full measurement invariance was not rejected in our sample, suggesting that gender differences as assessed with K10 reflect true differences. Structural invariance testing showed that girls in our sample showed significantly higher levels of depression and anxiety than boys. CONCLUSION: These findings support that the K10 is suitable for gender-comparative research among children of Chinese migrant workers. Using the K10 as a screening tool among this population should be promoted. Limitations and directions for future research were discussed.

Risk of bias assessments in systematic reviews and meta-analyses of behavioral interventions for substance use outcomes.

OBJECTIVE: This study aims to explore the current practice of risk of bias assessment in systematic reviews of behavioral clinical trials published in substance use journals and how assessment results were incorporated into meta-analysis. STUDY DESIGN AND SETTING: The authors searched for systematic reviews and meta-analyses of behavioral interventions published from 2016 to 2020 in 40 substance use journals. Two authors independently screened and extracted relevant information from each review. Different tools for risk of bias assessment and approaches of incorporating the risk of bias assessment results into meta-analysis were summarized. RESULTS: The study identified 35 systematic reviews and meta-analyses of behavioral clinical trials. Among the 35 reviews, 31 (89%) assessed the risk of bias of their included studies. Twelve (39%) of the 31 reviews incorporated these assessment findings into their meta-analysis of intervention effects (e.g., conducted meta-regression or subgroup analysis, sensitivity analysis, limited the synthesis only to the "high quality" studies). CONCLUSION: Performing and reporting risk of bias assessment remain inconsistent in published systematic reviews. Future systematic reviews and meta-analyses are encouraged to connect their risk of bias assessment findings with meta-analysis and follow the most updated PRISMA guidelines in reporting the methods and results of risk of bias assessment.

Accelerated failure-time model with weighted least-squares estimation: application on survival of HIV positives.

BACKGROUND: Survival analysis is the most appropriate method of analysis for time-to-event data. The classical accelerated failure-time model is a more powerful and interpretable model than the Cox proportional hazards model, provided that model imposed distribution and homoscedasticity assumptions satisfied. However, most of the real data are heteroscedastic which violates the fundamental assumption and consequently, the statistical inference could be erroneous in accelerated failure-time modeling. The weighted least-squares estimation for the accelerated failure-time model is an efficient semi-parametric approach for time-to-event data without the homoscedasticity assumption, which is developed recently and not often utilized for real data analysis. Thus, this study was conducted to ascertain the better performance of the weighted least-squares estimation method over the classical methods. METHODS: We analyzed a REAL dataset on Antiretroviral Therapy patients we recently collected. We compared the results from classical methods of estimation for the accelerated failure-time model with the results revealed from the weighted least-squares estimation. RESULTS: We found that the data are heteroscedastic and indicated that the weighted least-square method should be used to analyze this data. The weighted least-squares estimation revealed more accurate, and efficient estimates of covariates effect since its confidence intervals were shorter and it identified more significant covariates. Accordingly, the survival of HIV positives was found to be significantly linked with age, weight, functional status, CD4 (Cluster of Differentiation agent 4 glycoproteins), and clinical stages. CONCLUSIONS: The weighted least-squares estimation performed the best in providing more significant effects and precise estimates than the classical accelerated failure-time methods of estimation if data are heteroscedastic. Thus, we recommend future researchers should utilize weighted least-squares estimation rather than the classical methods when the homoscedasticity assumption is violated.

Influencing Factors of Inflammatory Bowel Disease-Fatigue: A Path Analysis Model.

BACKGROUND: Fatigue is a common symptom in adults with inflammatory bowel disease (IBD) and is influenced by many physiological, psychological, and situational factors. However, the influencing factors of fatigue associated with IBD have not been evaluated. OBJECTIVE: This study aims to examine factors associated with fatigue during IBD and develop a parsimonious model that describes the influencing factors of fatigue. METHODS: The study was a secondary analysis of cross-sectional data obtained from IBD Partners, an online cohort of adults with the disease, including 12,053 eligible participants. Data were collected using the Patient-Reported Outcomes Measurement Information System short-form scales measuring fatigue, sleep disturbances, pain interference, anxiety, depression, and satisfaction with social roles. Physical activity was measured using a single question. Demographic and clinical variables were collected. Path analysis was computed to identify the direct and indirect effects of situational, physiological, and psychological factors on IBD-fatigue based on the middle range theory of unpleasant symptoms' conceptual framework. RESULTS: Most of the participants were White females. The data best fit a model with situational factors (physical activity and satisfaction with social roles as the mediators). The direct effect of IBD activity, age, sleep disturbances, pain interference, anxiety, and depression on IBD-fatigue was significant. Significant indirect effects were noted on IBD-fatigue from sleep disturbances, pain interference, and depression via physical activity and satisfaction with social roles. DISCUSSION: The study identified two important intervening variables from the tested model. In addition, other symptoms such as sleep, pain, anxiety, and depression are essential and also influence IBD-fatigue.

Identifying treatment moderators of a trauma-informed parenting intervention with children in foster care: Using model-based recursive partitioning.

BACKGROUND: Trauma-informed parenting interventions have been used in child welfare to help caregivers respond to children in trauma-informed ways that can mitigate the effects of maltreatment and build strong caregiver-child relationships. Existing studies support their effectiveness with children and youth involved in the child welfare system. However, to further advance the effectiveness of evidenced-based intervention for child welfare populations, one key step is to identify subgroups of individuals who have different intervention responses or outcomes. OBJECTIVE: To identify pre-treatment moderators that can distinguish subgroups of caregivers and children who benefit differently from an intervention. PARTICIPANTS AND SETTING: 414 children in foster care (age 3 or younger) and their caregivers (birth, adoptive, kin, and nonkin) were randomly assigned to receive a trauma-informed parenting intervention in the Illinois Birth through Three Title IV-E waiver demonstration or foster care services as usual. METHODS: Model-based Recursive Partitioning (MOB) was used to identify treatment moderators and moderator interactions. MOB fits a parametric model and uses a data-driven method to find subgroups for which the specified parametric model has different parameters. Two parametric models (logistic and linear regression) were used in accordance with two outcomes: reunification (binary) and caregiver-child attachment (continuous). We examined 21 potential pre-treatment moderators in both models. RESULTS: For the reunification outcome, the MOB produced the following three treatment moderators, which identified subgroups of participants who responded differently to the intervention: (a) caregivers' relationship with the child (kin vs. non-kin/permanent caregivers), (b) caregiver-child attachment, and (c) case history of physical abuse. For the attachment outcome, caregivers' age was found to be a treatment moderator. Future developments of trauma-informed interventions should consider these moderators.

Stochastic cusp catastrophe model and its Bayesian computations.

This paper revitalizes the investigation of the classical cusp catastrophe model in catastrophe theory and tackles the unsolved statistical inference problem concerning stochastic cusp differential equation. This model is challenging because its associated transition density hence the likelihood function is analytically intractable. We propose a novel Bayesian approach combining Hamiltonian Monte Carlo with two likelihood approximation methods, namely, Euler approximation and Hermite expansion. We validate this novel approach through a series of simulation studies. We further demonstrate potential application of this novel approach using the real USD/EUR exchange rate.

Illness Uncertainty and Its Antecedents for Patients With Prostate Cancer and Their Partners.

OBJECTIVES: Guided by Mishel's uncertainty in illness theory, patterns of change in uncertainty were explored over time for patients with prostate cancer and their partners. In addition, the relationships between uncertainty and its antecedents were examined, and the role effects (patient versus partner) on these relationships were assessed. SAMPLE & SETTING: This study is a secondary analysis of the longitudinal data collected from a randomized clinical trial. METHODS & VARIABLES: The current authors fitted multiple-level models that included time-invariant baseline variables (sociodemographics and cancer factors) and time-varying variables (uncertainty, symptoms, and social support) measured at baseline and at 4, 8, and 12 months thereafter. RESULTS: No statistically significant patterns of change in uncertainty over time were detected. Partners reported greater uncertainty than patients. Higher uncertainty was associated with more general and prostate cancer-specific symptoms, recurrent and advanced prostate cancer, higher prostate-specific antigen level, and lower social support. More urinary symptoms were associated with greater uncertainty in patients than in partners. IMPLICATIONS FOR NURSING: Uncertainty management can be tailored for and target symptom management and social support.

Analysis of correlated data with feedback for time-dependent covariates in psychiatry research.

In studies on psychiatry and neurodegenerative diseases, it is common to have data that are correlated due to the hierarchical structure in data collection or to repeated measures on the subject longitudinally. However, the feedback effect created due to time-dependent covariates in these studies is often overlooked and seldom modelled. This article reviews the methodological development of feedback effects with marginal models for longitudinal data and discusses their implementation.

Understanding the relationship between features associated with pain-related disability in people with painful temporomandibular disorder: an exploratory structural equation modeling approach.

Pain-related disability is a multifaceted construct that refers to the impact of pain on an individual's capacity to fulfill their self-defined and social roles. This research examined the relationship between clinical, psychological, and pain sensitivity factors and pain-related disability among adults with chronic temporomandibular disorder (TMD). We analyzed data from a cross-sectional community-based sample of 1088 men and women with chronic TMD. We first constructed and tested a measure of pain-related disability (ie, pain impact), including a variable assessing presenteeism, created measurement models of jaw limitation, psychological unease (negative affect, somatic symptoms, and catastrophizing), and experimental pain sensitivity (eg, pressure pain threshold, thermal tolerance, and mechanical pressure pain threshold). Subsequently, latent variables were combined in a structural equation model. Participants (n = 1088) were 18 to 44 years old (mean 29.2, SD ± 7.8) whose chronic TMD had persisted, on average, for 6.9 years (SD ± 6.4). A model of pain-related disability, jaw limitation, and psychological unease was created and refined with exploratory model revisions to account for correlation among variables. Estimation of the final model indicated excellent fit with the data (root-mean-square error of approximation = 0.048, root-mean-square error of approximation 90% confidence interval [CI] 0.043-0.053, comparative fit index = 0.956, standardized root-mean-square residual = 0.040). Jaw functional limitation and psychological unease was strongly related to pain-related disability. Experimental pain sensitivity was removed from our model because of weak direct effect and the burden of performing experimental pain sensitivity testing in a clinical setting. The final model explained 78% of the variance in pain-related disability.

Understanding alcohol-specific antecedents among Chinese vocational school adolescents.

BACKGROUND: Alcohol use among Chinese vocational school students is widespread and associated with many negative consequences. However, alcohol-specific antecedents for this population are understudied. OBJECTIVES: The current study explored: (a) which alcohol-specific antecedents are the most salient predictors for alcohol use intentions, (b) whether any mediational relationships exist among these alcohol-specific antecedents, and (c) whether gender-based differences exist among these relationships. METHODS: This study analyzed data from 1,230 vocational school adolescents in three Chinese cities. Survey data were analyzed using dominance analysis and structural equation modeling. RESULTS: Personal norms were the most salient antecedents for alcohol use intentions, followed by injunctive norms from friends and parents, descriptive norms from friends and classmates, and positive belief about drinking. We observed a statistically significant mediational chain from descriptive norms to injunctive norms, and in turn to personal norms and positive beliefs, and finally to alcohol use intentions. Gender moderated some of the paths. CONCLUSIONS: Alcohol use norms and beliefs among Chinese vocational school students have distinct predictive relationships with alcohol use intentions. Alcohol use prevention programs designed for this population need to address normative beliefs (descriptive, injunctive, and personal norms) and the perceived benefit of alcohol use.

Reconstructing and forecasting the COVID-19 epidemic in the United States using a 5-parameter logistic growth model.

Background: Many studies have modeled and predicted the spread of COVID-19 (coronavirus disease 2019) in the U.S. using data that begins with the first reported cases. However, the shortage of testing services to detect infected persons makes this approach subject to error due to its underdetection of early cases in the U.S. Our new approach overcomes this limitation and provides data supporting the public policy decisions intended to combat the spread of COVID-19 epidemic. Methods: We used Centers for Disease Control and Prevention data documenting the daily new and cumulative cases of confirmed COVID-19 in the U.S. from January 22 to April 6, 2020, and reconstructed the epidemic using a 5-parameter logistic growth model. We fitted our model to data from a 2-week window (i.e., from March 21 to April 4, approximately one incubation period) during which large-scale testing was being conducted. With parameters obtained from this modeling, we reconstructed and predicted the growth of the epidemic and evaluated the extent and potential effects of underdetection. Results: The data fit the model satisfactorily. The estimated daily growth rate was 16.8% overall with 95% CI: [15.95, 17.76%], suggesting a doubling period of 4 days. Based on the modeling result, the tipping point at which new cases will begin to decline will be on April 7th, 2020, with a peak of 32,860 new cases on that day. By the end of the epidemic, at least 792,548 (95% CI: [789,162, 795,934]) will be infected in the U.S. Based on our model, a total of 12,029 cases were not detected between January 22 (when the first case was detected in the U.S.) and April 4. Conclusions: Our findings demonstrate the utility of a 5-parameter logistic growth model with reliable data that comes from a specified period during which governmental interventions were appropriately implemented. Beyond informing public health decision-making, our model adds a tool for more faithfully capturing the spread of the COVID-19 epidemic.

Illness uncertainty, coping, and quality of life among patients with prostate cancer.

OBJECTIVE: Illness uncertainty is a significant source of psychological distress that affects cancer patients' quality of life (QOL). Mishel's uncertainty in illness theory (UIT) proposes that illness uncertainty influences an individual's use of coping strategies, and directly and indirectly influences their QOL. This study tested the relationships depicted in the adapted UIT in cancer patients. METHODS: This cross-sectional study is a secondary analysis of the baseline data from a randomized clinical trial (N = 263 prostate cancer patients). Patients were diagnosed with localized (64.6%), biochemical recurrent (12.6%), or advanced (22.8%) prostate cancer. Uncertainty, coping (avoidant and active coping strategies), and QOL (physical and mental well-being) were measured using the Mishel's uncertainty of illness scale, Brief COPE, and the Medical Outcomes Study 12-item short form (SF-12), respectively. We used path analysis to achieve the research aim. RESULTS: Patients' illness uncertainty directly, negatively influenced their physical well-being (P < .001) and mental well-being (P < .05). Patients' illness uncertainty was positively related to their avoidant coping strategies (P < .001). Patients' active and avoidant coping strategies influenced their mental well-being (P < .001). Uncertainty also negatively influenced mental well-being through avoidant coping strategies. The model had excellent fit to the data. CONCLUSIONS: Our findings have indicated the potential of improving QOL by decreasing illness uncertainty and reducing avoidant coping strategies. Future research is needed to better understand the complex relationships between illness uncertainty, coping strategies, and domains of QOL among patients with different types of cancer using longitudinal research.

A robust Bayesian mixed effects approach for zero inflated and highly skewed longitudinal count data emanating from the zero inflated discrete Weibull distribution.

This article proposes a Bayesian mixed effects zero inflated discrete Weibull (ZIDW) regression model for zero inflated and highly skewed longitudinal count data, as an alternative to mixed effects regression models that are based on the negative binomial, zero inflated negative binomial, and conventional discrete Weibull (DW) distributions. The mixed effects ZIDW regression model is an extension of a recently introduced model based on the DW distribution and uses the log-link function to specify the relationship between the linear predictors and the median counts. The ZIDW approach offers a more robust characteristic of central tendency, compared to the mean count, when there is skewness in the data. A matrix generalized half-t (MGH-t) prior distribution is specified for the random effects covariance matrix as an alternative to the widely used Wishart prior distribution. The methodology is applied to a longitudinal dataset from an epilepsy clinical trial. In a data contamination simulation study, we show that the mixed effect ZIDW regression model is more robust than the competing mixed effects regression models when the data contain excess zeros or outliers. The performance of the ZIDW regression model is also assessed in a simulation study under the specification of, respectively, the MGH-t and Wishart prior distributions for the random effects covariance matrix. It turns out that the highest posterior density intervals under the MGH-t prior for the fixed effects maintain nominal coverage when the true variability between random slopes over time is small, whereas those under the Wishart prior are generally conservative.