Random effect models for repeated measures of zero-inflated count data

Large-scale data decipher children's scale errors: A meta-analytic approach using the zero-inflated Poisson models

Scale errors are intriguing phenomena in which a child tries to perform an object-specific action on a tiny object. Several viewpoints explaining the developmental mechanisms underlying scale errors exist; however, there is no unified account of how different factors interact and affect scale errors, and the statistical approaches used in the previous research do not adequately capture the structure of the data. By conducting a secondary analysis of aggregated datasets across nine different studies (n = 528) and using more appropriate statistical methods, this study provides a more accurate description of the development of scale errors. We implemented the zero-inflated Poisson (ZIP) regression that could directly handle the count data with a stack of zero observations and regarded developmental indices as continuous variables. The results suggested that the developmental trend of scale errors was well documented by an inverted U-shaped curve rather than a simple linear function, although nonlinearity captured different aspects of the scale errors between the laboratory and classroom data. We also found that repeated experiences with scale error tasks reduced the number of scale errors, whereas girls made more scale errors than boys. Furthermore, a model comparison approach revealed that predicate vocabulary size (e.g., adjectives or verbs), predicted developmental changes in scale errors better than noun vocabulary size, particularly in terms of the presence or absence of scale errors. The application of the ZIP model enables researchers to discern how different factors affect scale error production, thereby providing new insights into demystifying the mechanisms underlying these phenomena. A video abstract of this article can be viewed at https://youtu.be/1v1U6CjDZ1Q RESEARCH HIGHLIGHTS: We fit a large dataset by aggregating the existing scale error data to the zero-inflated Poisson (ZIP) model. Scale errors peaked along the different developmental indices, but the underlying statistical structure differed between the in-lab and classroom datasets. Repeated experiences with scale error tasks and the children's gender affected the number of scale errors produced per session. Predicate vocabulary size (e.g., adjectives or verbs) better predicts developmental changes in scale errors than noun vocabulary size.

Assessment of Neonatal Mortality and Associated Hospital-Related Factors in Healthcare Facilities Within Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana

Objectives

Ghana's quest to reduce neonatal mortality, in hospital facilities and communities, continues to be a nightmare. The pursuit of achieving healthy lives and well-being for neonates as enshrined in Sustainable Development Goal three lingered in challenging hospital facilities and communities. Notwithstanding that, there have been increasing efforts in that direction. This study examines the contributing factors that hinder the fight against neonatal mortality in all hospital facilities in the Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana.

Methods

The study utilized neonatal mortality data consisting of neonatal deaths, structural facility related variables, medical human resources, types of hospital facilities and natal care. The data was collected longitudinally from 2014 to 2019. These variables were analysed using the negative binomial hurdle regression (NBH) model to determine factors that contribute to this menace at the facility level. Cause-specific deaths were obtained to determine the leading causes of neonatal deaths within health facilities in the two municipal assemblies.

Results

The study established that the leading causes of neonatal mortality in these districts are birth asphyxia (46%), premature birth (33%), neonatal sepsis (11%) and neonatal jaundice (7%). The NBH showed that neonatal mortality in hospital facilities depend on the number of incubators, monitoring equipment, hand washing facilities, CPAPb machines, radiant warmers, physiotherapy machines, midwives, paediatric doctors and paediatric nurses in the hospital facility.

Conclusions

Early management of neonatal sepsis, birth asphyxia, premature birth and neonatal infections is required to reduce neonatal deaths. The government and all stakeholders in the health sector should provide all hospital facilities with the essential equipment and the medical human resources necessary to eradicate the menace. This will make the realization of Sustainable Development Goal three, which calls for healthy lives and well-being for all, a reality.

Multilevel modeling in single-case studies with zero-inflated and overdispersed count data

Count outcomes are frequently encountered in single-case experimental designs (SCEDs). Generalized linear mixed models (GLMMs) have shown promise in handling overdispersed count data. However, the presence of excessive zeros in the baseline phase of SCEDs introduces a more complex issue known as zero-inflation, often overlooked by researchers. This study aimed to deal with zero-inflated and overdispersed count data within a multiple-baseline design (MBD) in single-case studies. It examined the performance of various GLMMs (Poisson, negative binomial [NB], zero-inflated Poisson [ZIP], and zero-inflated negative binomial [ZINB] models) in estimating treatment effects and generating inferential statistics. Additionally, a real example was used to demonstrate the analysis of zero-inflated and overdispersed count data. The simulation results indicated that the ZINB model provided accurate estimates for treatment effects, while the other three models yielded biased estimates. The inferential statistics obtained from the ZINB model were reliable when the baseline rate was low. However, when the data were overdispersed but not zero-inflated, both the ZINB and ZIP models exhibited poor performance in accurately estimating treatment effects. These findings contribute to our understanding of using GLMMs to handle zero-inflated and overdispersed count data in SCEDs. The implications, limitations, and future research directions are also discussed.

Effect of modified income assistance payment schedules on substance use service access: Evidence from an experimental study

BACKGROUND

Despite being critical to reducing the impacts of poverty internationally, synchronized monthly government income assistance payments are linked to intensified drug use and associated harms, including disrupted access to substance use-related services. This study evaluates whether alternative income assistance distribution schedules improve harm reduction (HR), pharmacotherapy and substance use service utilization.

METHODS

This exploratory, parallel group, unblinded, randomized controlled trial analyzed data from adults (n = 192) in Vancouver, Canada receiving income assistance, and reporting active, regular illicit drug use. Participants were randomly assigned on a 1:2:2 basis for six income assistance payment cycles to: (1) existing government schedules (control); (2) a "staggered" single monthly payment; or (3) "split & staggered" twice-monthly payments. Generalized linear mixed models analyzed secondary outcomes of HR, pharmacotherapy and substance use service utilization as well as barriers accessing these services.

RESULTS

Forty-five control, 71 staggered, and 76 split & staggered volunteers participated between 2015 and 2019. Multivariable modified per-protocol analyses demonstrate increased access to substance use services (Adjusted Odds Ratio [AOR] 1.64, 95% Confidence Interval [CI] 1.02-2.64) for split & staggered arm participants, and, conversely, increased barriers to HR for participants in the staggered (AOR 2.34, 95% CI 1.24-4.41) and split & staggered (AOR 2.16, 95% CI 1.08-4.35) arms. Results also showed decreased barriers to pharmacotherapy around government payments (AOR 0.23, 95% CI 0.06-0.90), pharmacotherapy around individual payments (AOR 0.12, 95% CI 0.02-0.58), and HR around individual payments (AOR 0.11, 95% CI 0.02-0.63) for staggered arm participants.

CONCLUSION

Modifying payments schedules demonstrate improved access to overall substance use services, and reduced barriers to HR and pharmacotherapy around income assistance payments. However, increased overall barriers to HR access were also shown. These complex, predominantly beneficial findings support the exploration of offering alternative payment schedules to support service access.

Factors determining antenatal care utilization among mothers of deceased perinates in Ethiopia

Introduction

Receiving adequate antenatal care (ANC) had an integral role in improving maternal and child health outcomes. However, several factors influence the utilization of ANC from the individual level up to the community level factors. Thus, this study aims to investigate factors that determine ANC service utilization among mothers of deceased perinate using the proper count regression model.

Method

Secondary data analysis was performed on perinatal death surveillance data. A total of 3,814 mothers of deceased perinates were included in this study. Hurdle Poisson regression with a random intercept at both count-and zero-part (MHPR.ERE) model was selected as a best-fitted model. The result of the model was presented in two ways, the first part of the count segment of the model was presented using the incidence rate ratio (IRR), while the zero parts of the model utilized the adjusted odds ratio (AOR).

Result

This study revealed that 33.0% of mothers of deceased perinates had four ANC visits. Being in advanced maternal age [IRR = 1.03; 95CI: (1.01-1.09)], attending primary level education [IRR = 1.08; 95 CI: (1.02-1.15)], having an advanced education (secondary and above) [IRR = 1.14; 95 CI: (1.07-1.21)] and being resident of a city administration [IRR = 1.17; 95 CI: (1.05-1.31)] were associated with a significantly higher frequency of ANC visits. On the other hand, women with secondary and above education [AOR = 0.37; 95CI: (0.26-0.53)] and women who live in urban areas [AOR = 0.42; 95 CI: (0.33-0.54)] were less likely to have unbooked ANC visit, while women who resided in pastoralist regions [AOR = 2.63; 95 CI: (1.02-6.81)] were more likely to have no ANC visit.

Conclusion

The uptake of ANC service among mothers having a deceased perinate was determined by both individual (maternal age and educational status) and community (residence and type of region) level factors. Thus, a concerted effort is needed to improve community awareness through various means of communication by targeting younger women. Furthermore, efforts should be intensified to narrow down inequalities observed in ANC service provision due to the residence of the mothers by availing necessary personnel and improving the accessibility of service in rural areas.

Comparison of Hospitalization for Nonaffective Psychotic Disorders Among Refugee, Migrant, and Native-Born Adults in Sweden and Denmark

Importance

Determining whether migrants with nonaffective psychotic disorders (NAPDs) experience poorer outcomes after illness onset is essential to ensure adequate health care provision to these disadvantaged populations.

Objective

To compare cumulative hospital days for NAPDs during the first 5 years of illness among refugee, nonrefugee, and second-generation migrants and their Swedish and Danish peers.

Design, Setting, and Participants

This was a prospective cohort study of individuals treated for incident NAPDs in inpatient or outpatient settings between January 1, 2006, and December 31, 2013, and followed up for 5 years. This population-based study used Swedish and Danish national registries. Included participants were individuals in Sweden and Denmark, aged 18 to 35 years, treated for incident NAPDs. Data analyses were conducted from November 2022 to August 2023.

Exposures

Population group (determined according to residency in either country, not both countries), categorized as refugee (migrants whose residence in Sweden or Denmark was registered as refugee status or family reunification with a refugee), nonrefugee (all other individuals born outside Sweden and Denmark), second generation (individuals born in Sweden or Denmark with at least 1 parent born abroad), or native born (individuals born in Sweden or Denmark with both parents born in these countries).

Main Outcome and Measures

Total hospital days for NAPDs during the first 5 years of illness, analyzed using a hurdle model. Among those ever admitted, total number of admissions and mean admission length were examined.

Results

In total, 7733 individuals in Sweden (mean [SD] age, 26.0 [5.1] years; 4919 male [63.6%]) and 8747 in Denmark (mean [SD] age 24.8 [5.0] years; 5324 male [60.9%]) were followed up for 5 years or until death or emigration. After adjusting for a range of sociodemographic and clinical factors, the odds of experiencing any hospital days for NAPD were significantly higher among migrant groups compared with their native-born peers (Sweden: second generation, odds ratio [OR], 1.17; 95% CI, 1.03-1.33; P = .01; nonrefugee migrant, OR, 1.45; 95% CI, 1.21-1.73; P < .001; refugee, OR, 1.25; 95% CI, 1.06-1.47; P = .009; Denmark: second generation, OR, 1.21; 95% CI, 1.05-1.40; P = .01; nonrefugee migrant, OR, 1.33; 95% CI, 1.14-1.55; P < .001). These odds were highest among nonrefugee (Sweden: OR, 2.53; 95% CI, 1.59-4.03; P < .001; Denmark: OR, 2.61; 95% CI, 1.70-4.01; P < .001) and refugee (Sweden: OR, 1.96; 95% CI, 1.43-2.69; P < .001; Denmark: OR, 2.14; 95% CI, 1.42-3.21; P < .001) migrants from Africa and those who had arrived within 3 to 5 years (Sweden: nonrefugee migrants, OR, 1.93; 95% CI, 1.26-2.95; P = .002; refugees, OR, 2.38; 95% CI, 1.46-3.88; P < .001; Denmark: nonrefugee migrants, OR, 1.66; 95% CI, 0.96-2.85; P = .07; refugees, OR, 3.40; 95% CI, 1.13-10.17; P = .03). Among those ever hospitalized, refugees in both countries (Sweden, incidence rate ratio [IRR], 1.30; 95% CI, 1.12-1.51; P < .001; Denmark, IRR, 1.47; 95% CI, 1.24-1.75; P < .001) and second-generation migrants in Denmark (IRR, 1.22; 95% CI, 1.07-1.39; P = .003) experienced more days hospitalized for NAPDs than native-born individuals.

Conclusions and Relevance

In this prospective cohort study of individuals with NAPDs, results suggest that refugee, nonrefugee, and second-generation migrants experience more days hospitalized for these disorders than their native-born peers. Patterns were consistent across 2 countries with different models of psychosis care and immigration and integration policies.

Selective serotonin reuptake inhibitor use, age-related neuropathology and cognition in late-life

The objective of this study was to evaluate an association of selective serotonin reuptake inhibitor (SSRI) use with late life cognitive decline and further investigate the association with brain pathology. Using the data are from two harmonized clinical-pathologic cohort studies with annual cognitive testing we found that SSRI use was associated with significantly faster global cognitive decline and this association was present in those with and without pre-existing cognitive impairment at the time of SSRI initiation. In separate analyses of persons who died during the study and underwent neuropathologic examination, SSRI use was related to higher level of paired helical filament tau tangles and faster rate of global cognitive decline. However, when SSRI use and tangles were included in the same model, the association of SSRI use with rate of global cognitive decline was reduced by more than 50% and no longer statistically significant. SSRI use was associated with higher postmortem level of tau tangles, possibly because SSRI are being used to treat neurobehavioral symptoms associated with dementia, and this relationship appears to partly account for the association of SSRI use with more rapid cognitive decline.

Improving performance of hurdle models using rare-event weighted logistic regression: an application to maternal mortality data

In this paper, performance of hurdle models in rare events data is improved by modifying their binary component. The rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. This research aimed to develop and assess the performance of the Poisson and Negative Binomial (NB) Hurdle Rare Event Weighted Logistic Regression (REWLR) models, applied to simulated data with various degrees of zero inflation and to Nairobi county's maternal mortality data. The study data on maternal mortality were pulled from JPHES. The data contain the number of maternal deaths, which is the outcome variable, and other obstetric and demographic factors recorded in MNCH facilities in Nairobi between October 2021 and January 2022. The models were also fit and evaluated based on simulated data with varying degrees of zero inflation. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best performing model for zero inflated count data due to rare events.

Bayesian clustering of multiple zero-inflated outcomes

Several applications involving counts present a large proportion of zeros (excess-of-zeros data). A popular model for such data is the hurdle model, which explicitly models the probability of a zero count, while assuming a sampling distribution on the positive integers. We consider data from multiple count processes. In this context, it is of interest to study the patterns of counts and cluster the subjects accordingly. We introduce a novel Bayesian approach to cluster multiple, possibly related, zero-inflated processes. We propose a joint model for zero-inflated counts, specifying a hurdle model for each process with a shifted Negative Binomial sampling distribution. Conditionally on the model parameters, the different processes are assumed independent, leading to a substantial reduction in the number of parameters as compared with traditional multivariate approaches. The subject-specific probabilities of zero-inflation and the parameters of the sampling distribution are flexibly modelled via an enriched finite mixture with random number of components. This induces a two-level clustering of the subjects based on the zero/non-zero patterns (outer clustering) and on the sampling distribution (inner clustering). Posterior inference is performed through tailored Markov chain Monte Carlo schemes. We demonstrate the proposed approach on an application involving the use of the messaging service WhatsApp. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Mastery-oriented motor competence intervention improves behavioral but not cognitive self-regulation in head start preschoolers: Randomized controlled trial results

Motor competence and self-regulation develop rapidly in early childhood; emerging work suggests motor competence interventions as a promising way to promote self-regulation (e.g., behavioral inhibition; cognitive flexibility) in young children. We tested the impact of a mastery-focused motor competence intervention (Children's Health Activity Motor Program [CHAMP])1 on behavioral and cognitive aspects of self-regulation among children attending Head Start. Grounded in Achievement Goal Theory, CHAMP encourages children's autonomy to navigate a mastery-oriented motor skill learning environment. Children (M age = 53.4 months, SD = 3.2) were cluster-randomized by classroom (6 per condition) to an intervention (n = 67) or control condition (n = 45). Behavioral self-regulation skills were assessed using the Head-Toes-Knees-Shoulders task (HTKS). Cognitive self-regulation skills were assessed using working memory and dimensional card-sorting executive function tasks. Random-effects hurdle models accounting for zero-inflated distributions indicated that children receiving CHAMP, versus not, were almost 3 times more likely to have non-zero HTKS scores at post-test; OR: 2.98 (CI 1.53, 5.81); however, there were no effects on any cognitive aspects of self-regulation (all p's > 0.05). Mastery climate motor competence interventions are an ecologically valid strategy that may have a greater impact on preschoolers' behavioral than cognitive aspects of self-regulation.

Alterations to unionid transformation during agricultural and urban contaminants of emerging concern exposures

Highly imperiled unionids have a complex life cycle including the metamorphosis of an obligate parasite life stage, larval glochidia, to the juvenile stage. Despite the known vulnerabilities of both glochidia and juveniles to pollutants, little is known on how metamorphosis success may be affected by chemical stress. Disruption of the transformation process in which glochidia encyst on the gills of a host fish, could lead to lowered recruitment and population declines. Transformation rates of Lampsilis cardium on host fish Micropterus salmoides were empirically derived from experimental exposures to low, medium, or high concentrations of an agricultural or urban mixture of contaminants of emerging concern (CECs) over two exposure durations. Transformation was characterized by: (1) a zero-inflated Poisson general linear mixed effects model to compare difference in transformation between exposure durations and (2) time response curves to describe the transformation curve using long-term exposure data. Lampsilis cardium transformation was similar between exposure durations. When compared to controls, CEC stress significantly reduced juvenile production (p « 0.05) except for the agricultural medium treatment and tended to increased encapsulation duration which while statistically insignificant (p = 0.16) may have ecological relevancy. Combining the empirically derived reduction of transformation rates with parameters values from the literature, a Lefkovich stage-based population model predicted strong declines in population size of L. cardium for all treatments if these results hold in nature. Management focus on urban CECs may lead to best conservation efforts though agricultural CECs may also have a concentration dependent impact on transformation and therefore overall recruitment and conservation success.

Symptom Presence and Symptom Severity as Unique Indicators of Psychopathology: An Application of Multidimensional Zero-Inflated and Hurdle Graded Response Models

Questionnaires inquiring about psychopathology symptoms often produce data with excess zeros or the equivalent (e.g., none, never, and not at all). This type of zero inflation is especially common in nonclinical samples in which many people do not exhibit psychopathology, and if unaccounted for, can result in biased parameter estimates when fitting latent variable models. In the present research, we adopt a maximum likelihood approach in fitting multidimensional zero-inflated and hurdle graded response models to data from a psychological distress measure. These models include two latent variables: susceptibility, which relates to the probability of endorsing the symptom at all, and severity, which relates to the frequency of the symptom, given its presence. After estimating model parameters, we compute susceptibility and severity scale scores and include them as explanatory variables in modeling health-related criterion measures (e.g., suicide attempts, diagnosis of major depressive disorder). Results indicate that susceptibility and severity uniquely and differentially predict other health outcomes, which suggests that symptom presence and symptom severity are unique indicators of psychopathology and both may be clinically useful. Psychometric and clinical implications are discussed, including scale score reliability.

Long-Term Care Home Size Association with COVID-19 Infection and Mortality in Catalonia in March and April 2020

We aim to assess how COVID-19 infection and mortality varied according to facility size in 965 long-term care homes (LTCHs) in Catalonia during March and April 2020. We measured LTCH size by the number of authorised beds. Outcomes were COVID-19 infection (at least one COVID-19 case in an LTCH) and COVID-19 mortality. Risks of these were estimated with logistic regression and hurdle models. Models were adjusted for county COVID-19 incidence and population, and LTCH types. Sixty-five per cent of the LTCHs were infected by COVID-19. We found a strong association between COVID-19 infection and LTCH size in the adjusted analysis (from 45% in 10-bed homes to 97.5% in those with over 150 places). The average COVID-19 mortality in all LTCHs was 6.8% (3887 deaths) and 9.2% among the COVID-19-infected LTCHs. Very small and large homes had higher COVID-19 mortality, whereas LTCHs with 30 to 70 places had the lowest level. COVID-19 mortality sharply increased with LTCH size in counties with a cumulative incidence of COVID-19 which was higher than 250/100,000, except for very small homes, but slightly decreased with LTCH size when the cumulative incidence of COVID-19 was lower. To prevent infection and preserve life, the optimal size of an LTCH should be between 30 and 70 places.

Predictors of engagement with remote sensing technologies for symptom measurement in Major Depressive Disorder

BACKGROUND

Remote sensing for the measurement and management of long-term conditions such as Major Depressive Disorder (MDD) is becoming more prevalent. User-engagement is essential to yield any benefits. We tested three hypotheses examining associations between clinical characteristics, perceptions of remote sensing, and objective user engagement metrics.

METHODS

The Remote Assessment of Disease and Relapse - Major Depressive Disorder (RADAR-MDD) study is a multicentre longitudinal observational cohort study in people with recurrent MDD. Participants wore a FitBit and completed app-based assessments every two weeks for a median of 18 months. Multivariable random effects regression models pooling data across timepoints were used to examine associations between variables.

RESULTS

A total of 547 participants (87.8% of the total sample) were included in the current analysis. Higher levels of anxiety were associated with lower levels of perceived technology ease of use; increased functional disability was associated with small differences in perceptions of technology usefulness and usability. Participants who reported higher system ease of use, usefulness, and acceptability subsequently completed more app-based questionnaires and tended to wear their FitBit activity tracker for longer. All effect sizes were small and unlikely to be of practical significance.

LIMITATIONS

Symptoms of depression, anxiety, functional disability, and perceptions of system usability are measured at the same time. These therefore represent cross-sectional associations rather than predictions of future perceptions.

CONCLUSIONS

These findings suggest that perceived usability and actual use of remote measurement technologies in people with MDD are robust across differences in severity of depression, anxiety, and functional impairment.

Addressing overdispersion and zero-inflation for clustered count data via new multilevel heterogenous hurdle models

Unobserved heterogeneity causing overdispersion and the excessive number of zeros take a prominent place in the methodological development on count modeling. An insight into the mechanisms that induce heterogeneity is required for better understanding of the phenomenon of overdispersion. When the heterogeneity is sourced by the stochastic component of the model, the use of a heterogenous Poisson distribution for this part encounters as an elegant solution. Hierarchical design of the study is also responsible for the heterogeneity as the unobservable effects at various levels also contribute to the overdispersion. Zero-inflation, heterogeneity and multilevel nature in the count data present special challenges in their own respect, however the presence of all in one study adds more challenges to the modeling strategies. This study therefore is designed to merge the attractive features of the separate strand of the solutions in order to face such a comprehensive challenge. This study differs from the previous attempts by the choice of two recently developed heterogeneous distributions, namely Poisson-Lindley (PL) and Poisson-Ailamujia (PA) for the truncated part. Using generalized linear mixed modeling settings, predictive performances of the multilevel PL and PA models and their hurdle counterparts were assessed within a comprehensive simulation study in terms of bias, precision and accuracy measures. Multilevel models were applied to two separate real world examples for the assessment of practical implications of the new models proposed in this study.

Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates

In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals' data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals' log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.

Home monitoring of lung function, symptoms and quality of life after admission with COVID-19 infection: The HOMECOMIN' study

Background and objective

To develop targeted and efficient follow‐up programmes for patients hospitalized with coronavirus disease 2019 (COVID‐19), structured and detailed insights in recovery trajectory are required. We aimed to gain detailed insights in long‐term recovery after COVID‐19 infection, using an online home monitoring programme including home spirometry. Moreover, we evaluated patient experiences with the home monitoring programme.

Methods

In this prospective multicentre study, we included adults hospitalized due to COVID‐19 with radiological abnormalities. For 6 months after discharge, patients collected weekly home spirometry and pulse oximetry measurements, and reported visual analogue scales on cough, dyspnoea and fatigue. Patients completed the fatigue assessment scale (FAS), global rating of change (GRC), EuroQol‐5D‐5L (EQ‐5D‐5L) and online tool for the assessment of burden of COVID‐19 (ABCoV tool). Mixed models were used to analyse the results.

Results

A total of 133 patients were included in this study (70.1% male, mean age 60 years [SD 10.54]). Patients had a mean baseline forced vital capacity of 3.25 L (95% CI: 2.99–3.44 L), which increased linearly in 6 months with 19.1% (Δ0.62 L, p < 0.005). Patients reported substantial fatigue with no improvement over time. Nevertheless, health status improved significantly. After 6 months, patients scored their general well‐being almost similar as before COVID‐19. Overall, patients considered home spirometry useful and not burdensome.

Conclusion

Six months after hospital admission for COVID‐19, patients' lung function and quality of life were still improving, although fatigue persisted. Home monitoring enables detailed follow‐up for patients with COVID‐19 at low burden for patients and for the healthcare system.

Modeling rounded counts using a zero-inflated mixture of power series family of distributions

This paper proposes an extension of zero-inflated models for analyzing rounded counting outcomes. A zero-inflated mixture of power series is proposed, and the EM algorithm is developed to estimate parameters. The accuracy of estimators is evaluated using a simulation study. The results of simulations show that the estimation procedure is successful and estimates are accurate. An application of our models for analyzing the number of cigarettes smoked per day of respondents for the American's Changing Lives study is enclosed. The proposed model best fits the data and the relationships between rounded counts and other covariates revealed by proposed regression models.

Long-term follow-up assessment of opioid use outcomes among individuals with comorbid mental disorders and opioid use disorder treated with buprenorphine or methadone in a randomized clinical trial

AIMS

To investigate whether reduction in opioid use differs when treated by either buprenorphine-naloxone (BUP) or methadone (MET) among adults with comorbid opioid use disorder (OUD) and mental disorders.

DESIGN, SETTING AND PARTICIPANTS

In a randomized controlled trial, adults with OUD were randomized to 24 weeks of either BUP or MET treatment and were followed up in 3-yearly assessments. The present secondary analyses were based on 597 participants who completed all assessments.

MEASUREMENTS

The outcome measure was the number of days of using opioids per month during the follow-up period. The Mini-International Neuropsychiatric Interview (MINI) was used to classify participants into three groups: life-time mood disorder (n = 302), life-time mental disorder other than mood disorder (n = 114) and no mental disorder (n = 181). Medication treatment (BUP, MET, no treatment) during the follow-up period was a time-varying predictor.

FINDINGS

Based on zero-inflated Poisson (ZIP) mixed regression analysis, it was found that relative to no treatment, opioid use during the follow-up was significantly reduced by BUP [odds ratio (OR) = 0.12, 95% confidence interval (CI) = 0.07-0.21 for any use; risk ratio (RR) = 0.77, 95% CI =0.66-0.89 for days of use] and by MET [OR = 0.33, 95% CI = 0.25-0.45 for any use; RR = 0.78, 95% CI = 0.72-0.84 for days of use]. Relative to MET, BUP was associated with a lower likelihood of any opioid use among participants with mood disorders (OR = 0.52, 95% CI = 0.36-0.74) and for participants without mental disorder (OR = 0.37, 95% CI = 0.21-0.66) and fewer number of days using opioids (RR = 0.37, 95% CI = 0.25-0.56) among participants with other mental disorders.

CONCLUSIONS

Among adults with comorbid opioid use disorder and mental disorders, treatment with buprenorphine-naloxone produced greater reductions in opioid use than treatment with methadone.

EM Estimation for Zero- and k-Inflated Poisson Regression Model

Count data with excessive zeros are ubiquitous in healthcare, medical, and scientific studies. There are numerous articles that show how to fit Poisson and other models which account for the excessive zeros. However, in many situations, besides zero, the frequency of another count k tends to be higher in the data. The zero- and k-inflated Poisson distribution model (ZkIP) is appropriate in such situations The ZkIP distribution essentially is a mixture distribution of Poisson and degenerate distributions at points zero and k. In this article, we study the fundamental properties of this mixture distribution. Using stochastic representation, we provide details for obtaining parameter estimates of the ZkIP regression model using the Expectation–Maximization (EM) algorithm for a given data. We derive the standard errors of the EM estimates by computing the complete, missing, and observed data information matrices. We present the analysis of two real-life data using the methods outlined in the paper.

Variable selection for longitudinal zero-inflated power series transition model

In modeling many longitudinal count clinical studies, the excess of zeros is a common problem. To take into account the extra zeros, the zero-inflated power series (ZIPS) models have been applied. These models assume a latent mixture model consisting of a count component and a degenerated zero component that has a unit point mass at zero. Usually, the current response measurement in a longitudinal sequence is a function of previous outcomes. For example, in a study about acute renal allograft rejection, the number of acute rejection episodes for a patient in current time is a function of this outcome at previous follow-up times. In this paper, we consider a transition model for accounting the dependence of current outcome on the previous outcomes in the presence of excess zeros. New variable selection methods for the ZIPS transition model using least absolute shrinkage and selection operator (LASSO), minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalties are proposed. An expectation-maximization (EM) algorithm using the penalized likelihood is applied for both parameters estimations and conducting variable selection. Some simulation studies are performed to investigate the performance of the proposed approach and the approach is applied to analyze a real dataset.

A comparison of zero-inflated and hurdle models for modeling zero-inflated count data

Counts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros representing the patients with no utilization during a follow-up time. A common feature of this type of data is that the count measure tends to have excessive zero beyond a common count distribution can accommodate, such as Poisson or negative binomial. Zero-inflated or hurdle models are often used to fit such data. Despite the increasing popularity of ZI and hurdle models, there is still a lack of investigation of the fundamental differences between these two types of models. In this article, we reviewed the zero-inflated and hurdle models and highlighted their differences in terms of their data generating processes. We also conducted simulation studies to evaluate the performances of both types of models. The final choice of regression model should be made after a careful assessment of goodness of fit and should be tailored to a particular data in question.

Statistical modeling of numbers of human deaths per road traffic accident in the Oromia region, Ethiopia

BACKGROUND

Globally, road traffic accidents are the leading causes of death among young people in general, and the main cause of death among young people aged 15-29 years. Recently, in Ethiopia, the number of road traffic accidents has been increasing. The study aimed to identify the potential factors associated with the number of human deaths by road traffic accidents in the Oromia Regional State, Ethiopia.

METHODS

We used data obtained from the Oromia region traffic police office recorded on daily basis road traffic accidents from July 2016 up to July 2017. Count regression models were was used to analyses the factors associated with the number of human deaths from road traffic accidents.

RESULTS

Age of the driver's 31-50 years (AOR = 0.289, 95%CI: 0.175, 0.479) and higher than 50 years old (AOR = 0.311, 95%CI: 0.129, 0.751), driver's years of experience 5-10 years (AOR = 0.014, 95%CI: 0.007, 0.027), and more than 10 years (AOR = 0.101, 95%CI: 0.057, 0.176), automobile vehicle type (AOR = 8.642, 95%CI: 2.7644, 27.023), vehicle years of service 5-10 years (AOR = 2.484, 95%CI: 1.194, 5.169), and more than 10 years (AOR = 2.639, 95%CI: 1.268, 5.497), vehicle upside down accidents (AOR = 5.560, 95%CI: 2.506, 12.336), turning illegal position (AOR = 0.454, 95%CI: 0.226, 0.913), residential areas (AOR = 108.506, 95%CI: 13.725, 857.798), and working areas (AOR = 129.606, 95%CI: 16.448, 1021.263) were significant associated number of human deaths per road traffic accident factors in the study area.

CONCLUSION

Human deaths per road traffic accidents occurred due to the younger age of the driver, driver's lack of sufficient experience, vehicle serviced for long years, driving on a wet road, driving in the afternoon, driving near/around residential places and vehicle to driver's relation. Thus, the regional traffic police should give special attention to younger drivers, less experienced drivers, old vehicles, driving near residential areas, driving automobiles, and driving in the afternoon to control traffic system to reduce the number of human deaths pear road traffic accident.

Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana

Discrete count time series data with an excessive number of zeros have warranted the development of zero-inflated time series models to incorporate the inflation of zeros and the overdispersion that comes with it. In this paper, we investigated the characteristics of the trend of daily count of COVID-19 deaths in Ghana using zero-inflated models. We envisaged that the trend of COVID-19 deaths per day in Ghana portrays a general increase from the onset of the pandemic in the country to about day 160 after which there is a general decrease onward. We fitted a zero-inflated Poisson autoregressive model and zero-inflated negative binomial autoregressive model to the data in the partial-likelihood framework. The zero-inflated negative binomial autoregressive model outperformed the zero-inflated Poisson autoregressive model. On the other hand, the dynamic zero-inflated Poisson autoregressive model performed better than the dynamic negative binomial autoregressive model. The predicted new death based on the zero-inflated negative binomial autoregressive model indicated that Ghana's COVID-19 death per day will rise sharply few days after 30th November 2020 and drastically fall just as in the observed data.

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

Effect of alternative income assistance schedules on drug use and drug-related harm: a randomised controlled trial

Background

The synchronised monthly disbursement of income assistance, whereby all recipients are paid on the same day, has been associated with increases in illicit drug use and serious associated harms. This phenomenon is often referred to as the cheque effect. Because payment variability can affect consumption patterns, this study aimed to assess whether these harms could be mitigated through a structural intervention that varied income assistance payment timing and frequency.

Methods

This randomised, parallel group trial was done in Vancouver, Canada, and enrolled recipients of income assistance whose drug use increased around payment days. The recipients were randomly assigned 1:2:2 to a control group that received monthly synchronised income assistance payments on government payment days, a staggered group in which participants received single desynchronised monthly income assistance payments, or a split and staggered group in which participants received desynchronised income assistance payments split into two instalments per month, 2 weeks apart, for six monthly payment cycles. Desynchronised payments in the intervention groups were made on individual payment days outside the week of the standard government schedules. Randomisation was through a pre-established stratified block procedure. Investigators and statisticians were masked to group allocation, but participants and front-line staff were not. Complete final results are reported after scheduled interim analyses and the resulting early stoppage of recruitment. Under intention-to-treat specifications, generalised linear mixed models were used to analyse the primary outcome, which was escalations in drug use, predefined as a 40% increase in at least one of: use frequency; use quantity; or number of substances used during the 3 days after government payments. Secondary analyses examined analogous drug use outcomes coinciding with individual payments as well as exposure to violence. This trial is registered with ClinicalTrials.gov, NCT02457949.

Findings

Between Oct 27, 2015, and Jan 2, 2019, 45 participants were enrolled to the control group, 72 to the staggered group, and 77 to the split and staggered group. Intention-to-treat analyses showed a significantly reduced likelihood of increased drug use coinciding with government payment days, relative to the control group, in the staggered (adjusted odds ratio 0·38, 95% CI 0·20–0·74; p=0·0044) and split and staggered (0·44, 0·23–0·83; p=0·012) groups. Findings were consistent in the secondary analyses of drug use coinciding with individual payment days (staggered group 0·50, 0·27–0·96, p=0·036; split and staggered group 0·49, 0·26–0·94, p=0·030). However, secondary outcome analyses of exposure to violence showed increased harm in the staggered group compared with the control group (2·71, 1·06–6·91, p=0·037). Additionally, 51 individuals had a severe or life-threatening adverse event and there were six deaths, none of which was directly attributed to study participation.

Interpretation

Complex results indicate the potential for modified income assistance payment schedules to mitigate escalations in drug use, provided measures to address unintended harms are also undertaken. Additional research is needed to clarify whether desynchronised schedules produce other unanticipated consequences and if additional measures could mitigate these harms.

Funding

Canadian Institutes of Health Research, Providence Health Care Research Institute, Peter Wall Institute for Advanced Research, Michael Smith Foundation for Health Research.

Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach

This article develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable to also influence the distribution of the positive outcomes. As is common in the quantile regression literature, estimation and inference on the model parameters are based on the asymmetric Laplace distribution. Maximum likelihood estimates are obtained through the EM algorithm without parametric assumptions on the random effects distribution. In addition, a penalized version of the EM algorithm is presented to tackle the problem of variable selection. The proposed statistical method is applied to the well-known RAND Health Insurance Experiment dataset which gives further insights on its empirical behaviour.

A longitudinal evaluation of family caregivers' willingness to pay for an in-home nonpharmacologic intervention for people living with dementia: results from a randomized trial

OBJECTIVE

To determine the willingness-to-pay (WTP) of family caregivers to learn care strategies for persons living with dementia (PLwD).

DESIGN

Randomized clinical trial.

SETTING

Community-dwelling PLwD and their caregivers (dyads) in Maryland and Washington, DC.

PARTICIPANTS

250 dyads.

INTERVENTION

Tailored Activity Program (TAP) compared to attention control. TAP provides activities tailored to the PLwD and instructs caregivers in their use.

MEASUREMENT

At baseline, 3 and 6 months, caregivers were asked their WTP per session for an 8-session 3-month in-home nonpharmacologic intervention to address behavioral symptoms and functional dependence.

RESULTS

At baseline, 3 and 6 months, caregivers assigned to TAP were willing to pay $26.10/session (95%CI:$20.42, $33.00), $28.70 (95%CI:$19.73, $39.30), and $22.79 (95%CI: $16.64, $30.09), respectively; attention control caregivers were willing to pay $37.90/session (95%CI: $27.10, $52.02), $30.92 (95%CI: $23.44, $40.94), $27.44 (95%CI: $20.82, $35.34), respectively. The difference in baseline to 3 and 6 months change in WTP between TAP and the attention control was $9.58 (95%CI: -$5.00, $25.47) and $7.15 (95%CI: -$5.72, $21.81). The difference between TAP and attention control in change in the proportion of caregivers willing to pay something from baseline to 3 and 6 months was -12% (95%CI: -28%, -5%) and -7% (95%CI:-25%, -11%), respectively. The difference in change in WTP, among caregivers willing to pay something, between TAP and attention control from baseline to 3 and 6 months was $17.93 (95%CI: $0.22, $38.30) and $11.81 (95%CI: -$2.57, $28.17).

CONCLUSIONS

Family caregivers are willing to pay more for an intervention immediately following participation in a program similar to which they were asked to value.

Social facilitation of alcohol subjective effects in adolescents: Associations with subsequent alcohol use

RATIONALE

Laboratory research in adults indicates that alcohol-related subjective effects are enhanced under some social conditions. However, it is unknown whether this "social facilitation" of alcohol effects occurs in adolescents and is associated with alcohol use in the natural ecology.

OBJECTIVES

We examined associations of social facilitation of alcohol-related subjective effects with subsequent alcohol use among a relatively high-risk group of adolescents who reported drinking alcohol both with friends and alone.

METHODS

Los Angeles high school students from a prospective study (N = 142; 51% female; 10th graders) completed a baseline survey that assessed alcohol-related "positive" and "negative" subjective effects in two contexts: social (alcohol with friends) and solitary (alcohol alone); social facilitation was calculated as the difference between social and solitary. Students then completed five semi-annual surveys spanning 30 months (2014-2017) assessing 30-day alcohol use (days used, number of drinks, binge drinking).

RESULTS

Greater social facilitation of positive effects was significantly associated with greater number of alcohol use days (RR [95% CI] = 1.48 [1.19, 1.82]; p < .001), greater number of drinks (RR [95% CI] = 1.38 [1.14, 1.66]; p = .001), and greater odds of binge drinking (OR [95% CI] = 1.75 [1.20, 2.57]; p = .004). Similar associations were found with social positive effects. There were no significant associations between solitary positive effects-or any negative effects-and alcohol use outcomes.

CONCLUSIONS

Social facilitation can be measured outside of the laboratory. Relatively high-risk drinking adolescents who are more susceptible to the social facilitation of subjective alcohol effects are more likely to use more alcohol and binge drink.

TWO-SIGMA: A novel two-component single cell model-based association method for single-cell RNA-seq data

In this paper, we develop TWO-SIGMA, a TWO-component SInGle cell Model-based Association method for differential expression (DE) analyses in single-cell RNA-seq (scRNA-seq) data. The first component models the probability of "drop-out" with a mixed-effects logistic regression model and the second component models the (conditional) mean expression with a mixed-effects negative binomial regression model. TWO-SIGMA is extremely flexible in that it: (i) does not require a log-transformation of the outcome, (ii) allows for overdispersed and zero-inflated counts, (iii) accommodates a correlation structure between cells from the same individual via random effect terms, (iv) can analyze unbalanced designs (in which the number of cells does not need to be identical for all samples), (v) can control for additional sample-level and cell-level covariates including batch effects, (vi) provides interpretable effect size estimates, and (vii) enables general tests of DE beyond two-group comparisons. To our knowledge, TWO-SIGMA is the only method for analyzing scRNA-seq data that can simultaneously accomplish each of these features. Simulations studies show that TWO-SIGMA outperforms alternative regression-based approaches in both type-I error control and power enhancement when the data contains even moderate within-sample correlation. A real data analysis using pancreas islet single-cells exhibits the flexibility of TWO-SIGMA and demonstrates that incorrectly failing to include random effect terms can have dramatic impacts on scientific conclusions. TWO-SIGMA is implemented in the R package twosigma available at https://github.com/edvanburen/twosigma.

Count Models Analysis of Factors Associated with Under-Five Mortality in Ethiopia

Background. Under-five mortality has continued a key challenge to public health in Ethiopia, and other sub-Saharan Africa countries. The threat of under-five mortality is incessant and more studies are needed to generate new scientific evidence. This study aimed to model the number of under-five deaths a mother has experienced in her lifetime and factors associated with it in Ethiopia. Method. A retrospective cross-sectional study based on data obtained from the Ethiopian Demographic and Health Survey (DHS), 2016 was used. The response variable was the total number of under-five children died per mother in her lifetime. Variables such as maternal socioeconomic and demographic characteristics, health, and environmental factors were considered as risk factors of under-five mortality. Hurdle negative binomial (HNB) regression analysis was employed to determine the factors associated with under-five mortality. Results. The data showed that 27.2% (95%CI: 0263, 0.282) of women experienced under-five deaths. The study revealed the age of mother at first birth, the age of mother at the time of under-five mortality occurred, number of household members, household access to electricity, region, educational level of the mother, sex of household head, wealth index, mother residing with husband/partner at the time of under-five mortality occurred as factors associated with under-five mortality. Age of mother at first birth 18 to 24 (IRR = .663; 95%CI: 0.587, 0.749), 25 or higher years old (IRR = 0.424; 95%CI: 0.306, 0.588), access to electricity (IRR = 0.758; 95%CI: 0.588, 0.976), primary education level of the mother (IRR = 0.715; 95%CI: 0.584, 0.875) and the richer wealth index (IRR = 0.785; 95%CI: 0.624, 0.988) were associated with reduced incidence of under-five mortality controlling for other variables in the model. Whereas older age of mother 35 to 39 (IRR = 5.252; 95%CI: 2.992, 9.218), 40 to 44 (IRR = 7.429; 95%CI: 4.188, 13.177), 45 to 49 (IRR = 8.697; 95%CI: 4.853, 15.585), being a resident of the Benishangul-gumuz region (IRR = 1.781; 95%CI: 1.303, 2.434), female household head (IRR = 1.256; 95%CI: 1.034, 1.525) were associated with an increased incidence of under-five mortality. Conclusion. The findings suggested that early age of mothers’ at first birth and old ages of mothers’, female household head and being uneducated were found to increase the incidence of the under-five mortality, whereas access to electricity and living with husband was statistically associated with reduced incidence of under-five mortality. The implication of this study is that policymakers and stakeholders should provide health education for mothers not to give birth at an earlier age and improve living standards to achieve sustainable development goals.

Nonparametric Bayesian functional two-part random effects model for longitudinal semicontinuous data analysis

Longitudinal semicontinuous data, characterized by repeated measures of a large portion of zeros and continuous positive values, are frequently encountered in many applications including biomedical, epidemiological, and social science studies. Two-part random effects models (TPREM) have been used to investigate the association between such longitudinal semicontinuous data and covariates accounting for the within-subject correlation. The existing TPREM is, however, limited to incorporate a functional covariate, which is often available in a longitudinal study. Moreover, the existing TPREM typically assumes the normality of subject-specific random effects, which can be easily violated when there exists a subgroup structure. In this article, we propose a nonparametric Bayesian functional TPREM to assess the relationship between the longitudinal semicontinuous outcome and various types of covariates including a functional covariate. The proposed model also relaxes the normality assumption for the random effects through a Dirichlet process mixture of normals, which allows for identifying an underlying subgroup structure. The methodology is illustrated through an application to social insurance expenditure data collected by the Korean Welfare Panel Study and a simulation study.

Modeling daily and weekly moderate and vigorous physical activity using zero-inflated mixture Poisson distribution

Recently developed accelerometer devices have been used in large epidemiological studies for continuous and objective monitoring of physical activities. Typically, physical movements are summarized as minutes in light, moderate, and vigorous physical activities in each wearing day. Because of preponderance of zeros, zero-inflated distributions have been used for modeling the daily moderate or higher levels of physical activity. Yet, these models do not fully account for variations in daily physical activity and cannot be extended to model weekly physical activity explicitly, while the weekly physical activity is considered as an indicator for a subject's average level of physical activity. To overcome these limitations, we propose to use a zero-inflated Poisson mixture distribution that can model daily and weekly physical activity in same family of mixture distributions. Under this method, the likelihood of an inactive day and the amount of exercise in an active day are simultaneously modeled by a joint random effects model to incorporate heterogeneity across participants. If needed, the method has the flexibility to include an additional random effect to address extra variations in daily physical activity. Maximum likelihood estimation can be obtained through Gaussian quadrature technique, which is implemented conveniently in an R package GLMMadaptive. Method performances are examined using simulation studies. The method is applied to data from the Hispanic Community Health Study/Study of Latinos to examine the relationship between physical activity and BMI groups and within a participant the difference in physical activity between weekends and weekdays.

A new mixed-effects regression model for the analysis of zero-modified hierarchical count data

Count data sets are traditionally analyzed using the ordinary Poisson distribution. However, such a model has its applicability limited as it can be somewhat restrictive to handle specific data structures. In this case, it arises the need for obtaining alternative models that accommodate, for example, (a) zero-modification (inflation or deflation at the frequency of zeros), (b) overdispersion, and (c) individual heterogeneity arising from clustering or repeated (correlated) measurements made on the same subject. Cases (a)-(b) and (b)-(c) are often treated together in the statistical literature with several practical applications, but models supporting all at once are less common. Hence, this paper's primary goal was to jointly address these issues by deriving a mixed-effects regression model based on the hurdle version of the Poisson-Lindley distribution. In this framework, the zero-modification is incorporated by assuming that a binary probability model determines which outcomes are zero-valued, and a zero-truncated process is responsible for generating positive observations. Approximate posterior inferences for the model parameters were obtained from a fully Bayesian approach based on the Adaptive Metropolis algorithm. Intensive Monte Carlo simulation studies were performed to assess the empirical properties of the Bayesian estimators. The proposed model was considered for the analysis of a real data set, and its competitiveness regarding some well-established mixed-effects models for count data was evaluated. A sensitivity analysis to detect observations that may impact parameter estimates was performed based on standard divergence measures. The Bayesian p -value and the randomized quantile residuals were considered for model diagnostics.

Using School Climate to Improve Attendance and Grades: Understanding the Importance of School Satisfaction Among Middle and High School Students

BACKGROUND

Students with chronic absences tend to struggle academically and may not benefit fully from all school has to offer. A positive school climate has been shown to reduce absenteeism and promote academic success. In this study, we explored how a positive school climate and high satisfaction with school may influence absences and academic performance.

METHODS

We used mediated path analysis to describe relationships involving school climate, school satisfaction, absences, and grades among 6839 middle school (49% female, 82% white) and 7470 high school (51% female, 85.0% white) students from 26 West Virginia schools.

RESULTS

In the middle and high school samples, we found that a positive school climate and high satisfaction with school reduces school absenteeism. Findings also suggest students with more absences tend to perform less well academically; a positive school climate and school satisfaction may promote good grades.

CONCLUSIONS

Missing a substantial amount of school days for any reason may hinder students academic success, but "skipping" may require added attention. Improving school climate and student satisfaction with school may contribute to better attendance and grades.

A comparison of residual diagnosis tools for diagnosing regression models for count data

Background

Examining residuals is a crucial step in statistical analysis to identify the discrepancies between models and data, and assess the overall model goodness-of-fit. In diagnosing normal linear regression models, both Pearson and deviance residuals are often used, which are equivalently and approximately standard normally distributed when the model fits the data adequately. However, when the response vari*able is discrete, these residuals are distributed far from normality and have nearly parallel curves according to the distinct discrete response values, imposing great challenges for visual inspection.

Methods

Randomized quantile residuals (RQRs) were proposed in the literature by Dunn and Smyth (1996) to circumvent the problems in traditional residuals. However, this approach has not gained popularity partly due to the lack of investigation of its performance for count regression including zero-inflated models through simulation studies. Therefore, we assessed the normality of the RQRs and compared their performance with traditional residuals for diagnosing count regression models through a series of simulation studies. A real data analysis in health care utilization study for modeling the number of repeated emergency department visits was also presented.

Results

Our results of the simulation studies demonstrated that RQRs have low type I error and great statistical power in comparisons to other residuals for detecting many forms of model misspecification for count regression models (non-linearity in covariate effect, over-dispersion, and zero inflation). Our real data analysis also showed that RQRs are effective in detecting misspecified distributional assumptions for count regression models.

Conclusions

Our results for evaluating RQRs in comparison with traditional residuals provide further evidence on its advantages for diagnosing count regression models.