Transitions in health care settings for frequent and infrequent users of emergency departments: a population-based retrospective cohort study.

BACKGROUND: Efforts to reduce emergency department (ED) volumes often target frequent users. We examined transitions in care across ED, hospital, and community settings, and in-hospital death, for high system users (HSUs) compared to controls. METHODS: Population-based databases provided ED visits and hospitalizations in Alberta and Ontario, Canada. The retrospective cohort included the top 10% of all the ED users during 2015/2016 (termed HSUs) and a random sample of controls (4 per each HSU) from the bottom 90% per province. Rates of transitions among ED, hospitalization, community settings, and in-hospital mortality were adjusted for sociodemographic and ED variables in a multistate statistical model. RESULTS: There were 2,684,924 patients and 579,230 (21.6%) were HSUs. Patient characteristics associated with shorter community to ED transition times for HSUs included Alberta residence (ratio of hazard ratio [RHR] = 1.11, 95% confidence interval [CI] 1.11,1.12), living in areas in the lower income quintile (RHR = 1.06, 95%CI 1.06,1.06), and Ontario residents without a primary health care provider (RHR = 1.13, 95%CI 1.13,1.14). Once at the ED, characteristics associated with shorter ED to hospital transition times for HSUs included higher acuity (e.g., RHR = 1.70, 95% CI 1.61, 1.81 for emergent), and for many diagnoses including chest pain (RHR = 1.71, 95%CI 1.65,1.76) and gastrointestinal (RHR = 1.66, 95%CI 1.62,1.71). Once admitted to hospital, HSUs did not necessarily have longer stays except for conditions such as chest pain (RHR = 0.90, 95% CI 0.86, 0.95). HSUs had shorter times to death in the ED if they presented for cancer (RHR = 2.51), congestive heart failure (RHR = 1.93), myocardial infarction (RHR = 1.53), and stroke (RHR = 1.84), and shorter times to death in-hospital if they presented with cancer (RHR = 1.29). CONCLUSIONS: Differences between HSUs and controls in predictors of transitions among care settings were identified. Co-morbidities and limitations in access to primary care are associated with more rapid transitions from community to ED and hospital among HSUs. Interventions targeting these challenges may better serve patients across health systems.. TRIAL REGISTRATION: Not applicable.

A new time-varying coefficient regression approach for analyzing infectious disease data.

Since the beginning of the global pandemic of Coronavirus (SARS-COV-2), there has been many studies devoted to predicting the COVID-19 related deaths/hospitalizations. The aim of our work is to (1) explore the lagged dependence between the time series of case counts and the time series of death counts; and (2) utilize such a relationship for prediction. The proposed approach can also be applied to other infectious diseases or wherever dynamics in lagged dependence are of primary interest. Different from the previous studies, we focus on time-varying coefficient models to account for the evolution of the coronavirus. Using two different types of time-varying coefficient models, local polynomial regression models and piecewise linear regression models, we analyze the province-level data in Canada as well as country-level data using cumulative counts. We use out-of-sample prediction to evaluate the model performance. Based on our data analyses, both time-varying coefficient modeling strategies work well. Local polynomial regression models generally work better than piecewise linear regression models, especially when the pattern of the relationship between the two time series of counts gets more complicated (e.g., more segments are needed to portray the pattern). Our proposed methods can be easily and quickly implemented via existing R packages.

Evaluating Association Between Two Event Times with Observations Subject to Informative Censoring.

This article is concerned with evaluating the association between two event times without specifying the joint distribution parametrically. This is particularly challenging when the observations on the event times are subject to informative censoring due to a terminating event such as death. There are few methods suitable for assessing covariate effects on association in this context. We link the joint distribution of the two event times and the informative censoring time using a nested copula function. We use flexible functional forms to specify the covariate effects on both the marginal and joint distributions. In a semiparametric model for the bivariate event time, we estimate simultaneously the association parameters, the marginal survival functions, and the covariate effects. A byproduct of the approach is a consistent estimator for the induced marginal survival function of each event time conditional on the covariates. We develop an easy-to-implement pseudolikelihood-based inference procedure, derive the asymptotic properties of the estimators, and conduct simulation studies to examine the finite-sample performance of the proposed approach. For illustration, we apply our method to analyze data from the breast cancer survivorship study that motivated this research. Supplementary materials for this article are available online.

An exploration of the relationship between wastewater viral signals and COVID-19 hospitalizations in Ottawa, Canada.

Monitoring of viral signal in wastewater is considered a useful tool for monitoring the burden of COVID-19, especially during times of limited availability in testing. Studies have shown that COVID-19 hospitalizations are highly correlated with wastewater viral signals and the increases in wastewater viral signals can provide an early warning for increasing hospital admissions. The association is likely nonlinear and time-varying. This project employs a distributed lag nonlinear model (DLNM) (Gasparrini et al., 2010) to study the nonlinear exposure-response delayed association of the COVID-19 hospitalizations and SARS-CoV-2 wastewater viral signals using relevant data from Ottawa, Canada. We consider up to a 15-day time lag from the average of SARS-CoV N1 and N2 gene concentrations to COVID-19 hospitalizations. The expected reduction in hospitalization is adjusted for vaccination efforts. A correlation analysis of the data verifies that COVID-19 hospitalizations are highly correlated with wastewater viral signals with a time-varying relationship. Our DLNM based analysis yields a reasonable estimate of COVID-19 hospitalizations and enhances our understanding of the association of COVID-19 hospitalizations with wastewater viral signals.

Comparative effectiveness of urine drug screening strategies alongside opioid agonist treatment in British Columbia, Canada: a population-based observational study protocol.

INTRODUCTION: Urine drug tests (UDTs) are commonly used for monitoring opioid agonist treatment (OAT) responses, supporting the clinical decision for take-home doses and monitoring potential diversion. However, there is limited evidence supporting the utility of mandatory UDTs-particularly the impact of UDT frequency on OAT retention. Real-world evidence can inform patient-centred approaches to OAT and improve current strategies to address the ongoing opioid public health emergency. Our objective is to determine the safety and comparative effectiveness of alternative UDT monitoring strategies as observed in clinical practice among OAT clients in British Columbia, Canada from 2010 to 2020. METHODS AND ANALYSIS: We propose a population-level retrospective cohort study of all individuals 18 years of age or older who initiated OAT from 1 January 2010 to 17 March 2020. The study will draw on eight linked health administrative databases from British Columbia. Our primary outcomes include OAT discontinuation and all-cause mortality. To determine the effectiveness of the intervention, we will emulate a 'per-protocol' target trial using a clone censoring approach to compare fixed and dynamic UDT monitoring strategies. A range of sensitivity analyses will be executed to determine the robustness of our results. ETHICS AND DISSEMINATION: The protocol, cohort creation and analysis plan have been classified and approved as a quality improvement initiative by Providence Health Care Research Ethics Board and the Simon Fraser University Office of Research Ethics. Results will be disseminated to local advocacy groups and decision-makers, national and international clinical guideline developers, presented at international conferences and published in peer-reviewed journals electronically and in print.

Traumatic brain injury in precariously housed persons: Incidence and risks.

BACKGROUND: Homeless and precarious housed persons are particularly prone to traumatic brain injuries (TBIs), but existent incidence rates are hampered by poor case acquisition. We rigorously documented TBIs in precariously housed persons transitioning in and out of homelessness. METHODS: Between December 2016 and May 2018, 326 precariously housed participants enrolled in a longitudinal study in Vancouver, Canada were assessed monthly for TBI occurrences after education on sequelae. Over one participant-year, 2433 TBI screenings were acquired for 326 person-years and variables associated with odds of incident TBI were evaluated. FINDINGS: One hundred participants acquired 175 TBIs, yielding an observed incidence proportion of 30·7% and event proportion of 53·7%. Of the injured, 61% reported one TBI and 39% reported multiple injuries. Acute intoxication was present for more than half of the TBI events assessed. Additionally, 9·7% of TBI events occurred in the context of a drug overdose. Common injury mechanisms were falls (45·1%), assaults (25·1%), and hitting one's head on an object (13·1%). In this community-based but non-randomly recruited sample, exploratory analyses identified factors associated with odds of an incident TBI over one year of follow-up, including: schizophrenia disorders (odds ratio (OR) = 0·43, 95% confidence interval (CI) 0·19, 0·94), role functioning (OR = 0·69, 95% CI 0·52, 0·91), opioid dependence (OR = 2·17, 95% CI 1·27, 3·72) and those reporting past TBIs (OR = 1·99, 95% CI 1·13, 3·52). INTERPRETATION: Given the ubiquity of TBIs revealed in this precariously housed sample, we identify an underappreciated and urgent healthcare priority. Several factors modified the odds of incident TBI, which can facilitate investigations into targeted prevention efforts. FUNDING: Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, William and Ada Isabelle Steel Research Fund, Simon Fraser University Vice-President Research Undergraduate Student Research Award and Simon Fraser University Psychology Department Research Grant.

Estimating duration distribution aided by auxiliary longitudinal measures in presence of missing time origin.

Understanding the distribution of an event duration time is essential in many studies. The exact time to the event is often unavailable, and thus so is the full event duration. By linking relevant longitudinal measures to the event duration, we propose to estimate the duration distribution via the first-hitting-time model (e.g. Lee and Whitmore in Stat Sci 21(4):501-513, 2006). The longitudinal measures are assumed to follow a Wiener process with random drift. We apply a variant of the MCEM algorithm to compute likelihood-based estimators of the parameters in the longitudinal process model. This allows us to adapt the well-known empirical distribution function to estimate the duration distribution in the presence of missing time origin. Estimators with smooth realizations can then be obtained by conventional smoothing techniques. We establish the consistency and weak convergence of the proposed distribution estimator and present its variance estimation. We use a collection of wildland fire records from Alberta, Canada to motivate and illustrate the proposed approach. The finite-sample performance of the proposed estimator is examined by simulation. Viewing the available data as interval-censored times, we show that the proposed estimator can be more efficient than the well-established Turnbull estimator, an alternative that is often applied in such situations.

Characteristics of frequent users of emergency departments in Alberta and Ontario, Canada: an administrative data study.

OBJECTIVE: Frequent users to emergency departments (EDs) are a diverse group of patients accounting for a disproportionate number of ED presentations. This study examined sociodemographic and ED visit characteristics of adult high-system users in two Canadian provinces. METHODS: Cohorts of high-system users were created for Alberta and Ontario including patients with the top 10% of presentations in the National Ambulatory Care Reporting System (April 2015-March 2016). Controls were random samples of non-high-system user patients. Sociodemographic and ED visits data were used to predict high-system user group membership in a multivariable logistic regression model. RESULTS: There were 579,674 high-system users and 2,115,960 controls. High-system users were more likely to be female [odds ratio (OR) = 1.1, 95% confidence interval (CI) 1.1,1.1], older (OR 1.02 per 5 years, 95% CI 1.02,1.02), from the lowest-income quintile (OR 1.8, 95% CI 1.7,1.8), and more rural (OR 1.6, 95% CI 1.6,1.6) than controls. High-system users had a higher proportion of presentations by ambulance (OR 1.1 per 0.25 increase, 95% CI 1.1,1.1) and disposition was admission/transfer (OR 1.1 per 0.25 increase, 95% CI 1.1,1.1), left without being seen (OR 1.1, 95% CI 1.1,1.1), or left against medical advice (OR 1.1, 95% CI 1.1,1.1) more often than controls. CONCLUSION: High-system users were more likely to be female, older, live in rural areas and within the lowest-income quintile compared to controls. Their heterogeneity in acuity, comorbid chronic diseases, and limited access to primary care suggests that interventions referring high-system users to primary care may be fruitful in reducing ED utilization by high-system users.

RéSUMé: OBJECTIF: Les utilisateurs fréquents des services d'urgence sont un groupe diversifié de patients qui représentent un nombre disproportionné de présentations aux services d'urgence. Cette étude a examiné les caractéristiques sociodémographiques et les visites aux urgences de grands utilisateurs adultes du système de santé dans deux provinces canadiennes. MéTHODE: Des cohortes de grands utilisateurs du système de santé ont été créées pour l'Alberta et l'Ontario, y compris les patients ayant fait l'objet des 10 % de présentations les plus importantes dans le système national d'information sur les soins ambulatoires (avril 2015-mars 2016). Les contrôles étaient des échantillons aléatoires des patients ne faisant pas partie des grands utilisateurs. Les données sociodémographiques et les données sur les visites aux urgences ont été utilisées pour prédire l'appartenance à un groupe de grands utilisateurs du système dans un modèle de régression logistique multivariable. RéSULTATS: Il y avait 579 674 de grands utilisateurs et 2 115 960 témoins. Les grands utilisateurs de système étaient plus susceptibles d'être des femmes (rapport des cotes [RC] = 1,1, intervalle de confiance à 95 % (IC) 1,1, 1,1), plus âgés (RC = 1,02 tous les 5 ans, IC à 95 % 1,02, 1,02), d'après quintile de revenu le plus bas (RC = 1,8, IC à 95 % 1,7,1,8), et plus rural (RC = 1,6, IC à 95 % 1,6,1,6) que les témoins. Les grands utilisateurs du système avaient une proportion plus élevée de présentations par ambulance (RC = 1,1 pour une augmentation de 0,25, IC à 95 % 1,1, 1,1) et la disposition était l'admission / transfert (RC = 1,1 pour une augmentation de 0,25, IC à 95 % 1,1, 1,1), parti sans être vu (OR = 1,1, IC à 95% 1,1, 1,1), ou parti contre l'avis médical (OR = 1,1, IC à 95 % 1,1, 1,1) plus souvent que les témoins. CONCLUSIONS: Les grands utilisateurs du système étaient plus susceptibles d'être des femmes, des personnes âgées, vivant dans des zones rurales et se situant dans le quintile de revenu le plus bas, par rapport aux témoins. Leur hétérogénéité en termes d'acuité, de comorbidité des maladies chroniques et d'accès limité aux soins primaires suggère que les interventions qui orientent les grands utilisateurs du haut système vers les soins primaires peuvent être fructueuses pour réduire l'utilisation des urgences par les grands utilisateurs du système.

Handling coarsened age information in the analysis of emergency department presentations.

BACKGROUND: Administrative databases offer vast amounts of data that provide opportunities for cost-effective insights. They simultaneously pose significant challenges to statistical analysis such as the redaction of data because of privacy policies and the provision of data that may not be at the level of detail required. For example, ages in years rather than birthdates available at event dates can pose challenges to the analysis of recurrent event data. METHODS: Hu and Rosychuk provided a strategy for estimating age-varying effects in a marginal regression analysis of recurrent event times when birthdates are all missing. They analyzed emergency department (ED) visits made by children and youth and privacy rules prevented all birthdates to be released, and justified their approach via a simulation and asymptotic study. With recent changes in data access rules, we requested a new extract of data for April 2010 to March 2017 that includes patient birthdates. This allows us to compare the estimates using the Hu and Rosychuk (HR) approach for coarsened ages with estimates under the true, known ages to further examine their approach numerically. The performance of the HR approach under five scenarios is considered: uniform distribution for missing birthdates, uniform distribution for missing birthdates with supplementary data on age, empirical distribution for missing birthdates, smaller sample size, and an additional year of data. RESULTS: Data from 33,299 subjects provided 58,166 ED visits. About 67% of subjects had one ED visit and less than 9% of subjects made over three visits during the study period. Most visits (84.0%) were made by teenagers between 13 and 17 years old. The uniform distribution and the HR modeling approach capture the main trends over age of the estimates when compared to the known birthdates. Boys had higher ED visit frequencies than girls in the younger ages whereas girls had higher ED visit frequencies than boys for the older ages. Including additional age data based on age at end of fiscal year did not sufficiently narrow the widths of potential birthdate intervals to influence estimates. The empirical distribution of the known birthdates was close to a uniform distribution and therefore, use of the empirical distribution did not change the estimates provided by assuming a uniform distribution for the missing birthdates. The HR approach performed well for a smaller sample size, although estimates were less smooth when there were very few ED visits at some younger ages. When an additional year of data is added, the estimates become better at these younger ages. CONCLUSIONS: Overall the Hu and Rosychuk approach for coarsened ages performed well and captured the key features of the relationships between ED visit frequency and covariates.

Frequent users of emergency departments and patient flow in Alberta and Ontario, Canada: an administrative data study.

BACKGROUND: This paper describes and compares patient flow characteristics of adult high system users (HSUs) and control groups in Alberta and Ontario emergency departments (EDs), Canada. METHODS: Annual cohorts of HSUs were created by identifying patients who made up the top 10% of ED users (by count of ED presentations) in the National Ambulatory Care Reporting System during 2011-2016. Random samples of patients not in the HSU groups were selected as controls. Presentation (e.g., acuity) and ED times (e.g., time to physician initial assessment [PIA], length of stay) data were extracted and described. The length of stay for 2015/2016 data was decomposed into stages and Cox models compared time between stages. RESULTS: There were 20,343,230 and 18,222,969 ED presentations made by 7,032,655 and 1,923,462 individuals in the control and HSU groups, respectively. The Ontario groups had higher acuity than the Alberta groups: about 20% in the Ontario groups were from the emergent level whereas Alberta had 11-15%. Time to PIA was similar across provinces and groups (medians of 60 min to 67 min). Lengths of stay were longest for Ontario HSUs (median = 3 h) and shortest for Alberta HSUs (median = 2.2 h). HSUs had shorter times to PIA (hazard ratio [HR] = 1.03; 95% confidence interval [CI] 1.02,1.03), longer times from PIA to decision (HR = 0.84; 95%CI 0.84,0.84), and longer times from decision to leaving the ED (HR = 0.91; 95%CI 0.91,0.91). CONCLUSIONS: Ontario HSUs had higher acuity and longer ED lengths of stay than the other groups. In both provinces, HSU had shorter times to PIA and longer times after assessment.

Exploring spatio-temporal patterns in mental health related emergency department use from children and adolescents.

To understand the spatio-temporal patterns and associated risk factors with the frequency, we analyze records of mental health related emergency department (MHED) visits from youth. The data are extracted for the period 2002--2011 from the population-based, provincial health administrative data systems of Alberta, Canada. Guided by a descriptive analysis, we conduct generalized linear regression analyses of the counts of MHED visits from various health areas. Seasonal effects are examined via three different types of functions, including trigonometric functions. We specify the temporal correlation using an autoregressive model of order 1 and formulate the spatial correlation by a random effects model. Our analysis reveals a strong seasonal pattern and indicates that the MHED visit counts are significantly associated with age, gender, and a proxy for socio-economic status. The final statistical model may be used to forecast future MHED use and identify regions and groups at a higher risk to the MHEDs.

Multiple event times in the presence of informative censoring: modeling and analysis by copulas.

Motivated by a breast cancer research program, this paper is concerned with the joint survivor function of multiple event times when their observations are subject to informative censoring caused by a terminating event. We formulate the correlation of the multiple event times together with the time to the terminating event by an Archimedean copula to account for the informative censoring. Adapting the widely used two-stage procedure under a copula model, we propose an easy-to-implement pseudo-likelihood based procedure for estimating the model parameters. The approach yields a new estimator for the marginal distribution of a single event time with semicompeting-risks data. We conduct both asymptotics and simulation studies to examine the proposed approach in consistency, efficiency, and robustness. Data from the breast cancer program are employed to illustrate this research.

The Flooring for Injury Prevention (FLIP) Study of compliant flooring for the prevention of fall-related injuries in long-term care: A randomized trial.

BACKGROUND: Fall-related injuries exert an enormous health burden on older adults in long-term care (LTC). Softer landing surfaces, such as those provided by low-stiffness "compliant" flooring, may prevent fall-related injuries by decreasing the forces applied to the body during fall impact. Our primary objective was to assess the clinical effectiveness of compliant flooring at preventing serious fall-related injuries among LTC residents. METHODS AND FINDINGS: The Flooring for Injury Prevention (FLIP) Study was a 4-year, randomized superiority trial in 150 single-occupancy resident rooms at a single Canadian LTC site. In April 2013, resident rooms were block randomized (1:1) to installation of intervention compliant flooring (2.54 cm SmartCells) or rigid control flooring (2.54 cm plywood) covered with identical hospital-grade vinyl. The primary outcome was serious fall-related injury over 4 years that required an emergency department visit or hospital admission and a treatment procedure or diagnostic evaluation in hospital. Secondary outcomes included minor fall-related injury, any fall-related injury, falls, and fracture. Outcomes were ascertained by blinded assessors between September 1, 2013 and August 31, 2017 and analyzed by intention to treat. Adverse outcomes were not assessed. During follow-up, 184 residents occupied 74 intervention rooms, and 173 residents occupied 76 control rooms. Residents were 64.3% female with mean (SD) baseline age 81.7 (9.5) years (range 51.1 to 104.6 years), body mass index 25.9 (7.7) kg/m2, and follow-up 1.64 (1.39) years. 1,907 falls were reported; 23 intervention residents experienced 38 serious injuries (from 29 falls in 22 rooms), while 23 control residents experienced 47 serious injuries (from 34 falls in 23 rooms). Compliant flooring did not affect odds of ≥1 serious fall-related injury (12.5% intervention versus 13.3% control, odds ratio [OR]: 0.98, 95% CI: 0.52 to 1.84, p = 0.950) or ≥2 serious fall-related injuries (5.4% versus 7.5%, OR: 0.74, 95% CI: 0.31 to 1.75, p = 0.500). Compliant flooring did not affect rate of serious fall-related injuries (0.362 versus 0.422 per 1,000 bed nights, rate ratio [RR]: 1.04, 95% CI: 0.45 to 2.39, p = 0.925; 0.038 versus 0.053 per fall, RR: 0.81, 95% CI: 0.38 to 1.71, p = 0.560), rate of falls with ≥1 serious fall-related injury (0.276 versus 0.303 per 1,000 bed nights, RR: 0.97, 95% CI: 0.52 to 1.79, p = 0.920), or time to first serious fall-related injury (0.237 versus 0.257, hazard ratio [HR]: 0.92, 95% CI: 0.52 to 1.62, p = 0.760). Compliant flooring did not affect any secondary outcome in this study. Study limitations included the following: findings were specific to 2.54 cm SmartCells compliant flooring installed in LTC resident rooms, standard fall and injury prevention interventions were in use throughout the study and may have influenced the observed effect of compliant flooring, and challenges with concussion detection in LTC residents may have prevented estimation of the effect of compliant flooring on fall-related concussions. CONCLUSIONS: In contrast to results from previous retrospective and nonrandomized studies, this study found that compliant flooring underneath hospital-grade vinyl was not effective at preventing serious fall-related injuries in LTC. Future studies are needed to identify effective methods for preventing fall-related injuries in LTC. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01618786.

Bayesian adaptive group lasso with semiparametric hidden Markov models.

This paper presents a Bayesian adaptive group least absolute shrinkage and selection operator method to conduct simultaneous model selection and estimation under semiparametric hidden Markov models. We specify the conditional regression model and the transition probability model in the hidden Markov model into additive nonparametric functions of covariates. A basis expansion is adopted to approximate the nonparametric functions. We introduce multivariate conditional Laplace priors to impose adaptive penalties on regression coefficients and different groups of basis expansions under the Bayesian framework. An efficient Markov chain Monte Carlo algorithm is then proposed to identify the nonexistent, constant, linear, and nonlinear forms of covariate effects in both conditional and transition models. The empirical performance of the proposed methodology is evaluated via simulation studies. We apply the proposed model to analyze a real data set that was collected from the Alzheimer's Disease Neuroimaging Initiative study. The analysis identifies important risk factors on cognitive decline and the transition from cognitive normal to Alzheimer's disease.

Age Affects the Impact of Important Predictors on Mental Health Emergency Department Visits.

Visits to emergency departments (EDs) for pediatric mental health care have increased over the years. The likelihood of ED re-visit is greatest for older children; however, little is known about age-varying effects on ED visits/re-visits. This study used population-based administrative databases from Alberta, Canada, to investigate the association of predictors on mental health ED visit frequency by age for 27,947 children who presented at least once for mental health care from April 2002 to March 2011. A marginal regression model with age-varying effects for sex, geographic area, and socio-demographic group was fit. The impact of predictors depended on a child's age. Notably, older males had lower ED visit frequencies compared to females (age 15 adjusted hazard ratio [aHR] = 0.70, 95% confidence interval [CI] [0.65, 0.76]) than younger males (age 10 aHR = 1.83, 95% CI [1.50, 2.24]). Children with particular ages for some socio-demographic groups also showed higher ED frequencies than children from the non-subsidized group. The analysis revealed important age-varying effects on predictors of mental health ED visit frequency.

Marginal regression analysis of recurrent events with coarsened censoring times.

Motivated by an ongoing pediatric mental health care (PMHC) study, this article presents weakly structured methods for analyzing doubly censored recurrent event data where only coarsened information on censoring is available. The study extracted administrative records of emergency department visits from provincial health administrative databases. The available information of each individual subject is limited to a subject-specific time window determined up to concealed data. To evaluate time-dependent effect of exposures, we adapt the local linear estimation with right censored survival times under the Cox regression model with time-varying coefficients (cf. Cai and Sun, Scandinavian Journal of Statistics 2003, 30, 93-111). We establish the pointwise consistency and asymptotic normality of the regression parameter estimator, and examine its performance by simulation. The PMHC study illustrates the proposed approach throughout the article.

Jointly modeling time-to-event and longitudinal data: A Bayesian approach.

This article explores Bayesian joint models of event times and longitudinal measures with an attempt to overcome departures from normality of the longitudinal response, measurement errors, and shortages of confidence in specifying a parametric time-to-event model. We allow the longitudinal response to have a skew distribution in the presence of measurement errors, and assume the time-to-event variable to have a nonparametric prior distribution. Posterior distributions of the parameters are attained simultaneously for inference based on Bayesian approach. An example from a recent AIDS clinical trial illustrates the methodology by jointly modeling the viral dynamics and the time to decrease in CD4/CD8 ratio in the presence of CD4 counts with measurement errors and to compare potential models with various scenarios and different distribution specifications. The analysis outcome indicates that the time-varying CD4 covariate is closely related to the first-phase viral decay rate, but the time to CD4/CD8 decrease is not highly associated with either the two viral decay rates or the CD4 changing rate over time. These findings may provide some quantitative guidance to better understand the relationship of the virological and immunological responses to antiretroviral treatments.

Analysis of counts with two latent classes, with application to risk assessment based on physician-visit records of cancer survivors.

Motivated by a cancer survivorship program, this paper explores event counts from two categories of individuals with unobservable membership. We formulate the counts using a latent class model and consider two likelihood-based inference procedures, the maximum likelihood estimation (MLE) and a pseudo-MLE procedure. The pseudo-MLE utilizes additional information on one of the latent classes. It yields reduced computational intensity and potentially increased estimation efficiency. We establish the consistency and asymptotic normality of the proposed pseudo-MLE, and we present an extended Huber sandwich estimator as a robust variance estimator for the pseudo-MLE. The finite-sample properties of the two-parameter estimators along with their variance estimators are examined by simulation. The proposed methodology is illustrated by physician-claim data from the cancer program.

Estimation with Right-Censored Observations Under A Semi-Markov Model.

The semi-Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end-point of the support of the censoring time is strictly less than the right end-point of the support of the semi-Markov kernel, the transition probability of the semi-Markov process is nonidentifiable, and the estimators proposed in the literature are inconsistent in general. We derive the set of all attainable values for the transition probability based on the censored data, and we propose a nonparametric inference procedure for the transition probability using this set. Second, the conventional approach to constructing confidence bands is not applicable for the semi-Markov kernel and the sojourn time distribution. We propose new perturbation resampling methods to construct these confidence bands. Different weights and transformations are explored in the construction. We use simulation to examine our proposals and illustrate them with hospitalization data from a recent cancer survivor study.

Analysis of recurrent events with non-negligible event duration, with application to assessing hospital utilization.

In an attempt to provide tools for assessing hospital utilization, this paper extends well-known models for recurrent events to address non-negligible event duration and presents a procedure for estimating the model parameters. The model extension is natural and easy to understand. Asymptotic properties of the associated inferences are derived adapting the well-developed methods based on the counting process formulation. Several specifications of the proposed modeling are illustrated with the hospitalization records of childhood cancer survivors from a health care insurance system that motivated this research. The usefulness and robustness of the proposed approach is demonstrated numerically via simulation.