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Objectives Repeated presentations to emergency departments (EDs) may indicate a lack of access to other health care resources. Age is an important predictor of frequent ED use; however, age-varying effects are not generally investigated. This study examines the age-specific effects of predictors on ED presentation frequency for children in Alberta and Ontario, Canada. Methods This retrospective study used population-based data during April 2010 to March 2017. Data were extracted from the National Ambulatory Care Reporting System for children aged Results There were 2,481,172 patients who made 9,229,156 ED presentations. The effects of sex, lowest income quintile, rural residence, access to primary health care provider and province on the frequency of presentations varied by age. Notably, boys go from having more frequent presentations than girls when aged ≤5 (i.e. adjusted intensity ratio [IR]=1.04 at age 5, 95% confidence interval [CI] = 1.03,1.06) to less frequent for ages 8–11 years and beyond 14 (i.e. IR = 0.80 at age 15, 95% CI = 0.78,0.81). Adolescents aged ≥15 without access to a primary care provider had more frequent presentations compared to those with a primary care provider. Conclusions When examining the frequency of ED presentations in children, age-varying effects of predictors should be considered. Our more nuanced examination of age provides insights into how health services might better target programmes for different ages to potentially reduce unnecessary ED use by providing other health care alternatives.

This paper is concerned with evaluating the association between two event times without specifying the joint distribution parametrically. This is particularly challenging when the observations on t...

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. Supplementary Information The online version contains supplementary material available at 10.1007/s10985-021-09520-w.

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. 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. 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. 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.

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.

The action of crossing the ball in soccer has a long history as an effective tactic for producing goals. Lately, the benefit of crossing the ball has come under question, and alternative strategies have been suggested. This paper utilizes player tracking data to explore crossing at a deeper level. First, we investigate the spatio-temporal conditions that lead to crossing. Then we introduce an intended target model that investigates crossing success. Finally, a contextual analysis is provided that assesses the benefits of crossing in various situations. The analysis is based on causal inference techniques and suggests that crossing remains an effective tactic in particular contexts.

Summary: 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.

Motivated by a recent tuberculosis (TB) study, this paper is concerned with covariates missing not at random (MNAR) and models the potential intracluster correlation by a frailty. We consider the regression analysis of right‐censored event times from clustered subjects under a Cox proportional hazards frailty model and present the semiparametric maximum likelihood estimator (SPMLE) of the model parameters. An easy‐to‐implement pseudo‐SPMLE is then proposed to accommodate more realistic situations using readily available supplementary information on the missing covariates. Algorithms are provided to compute the estimators and their consistent variance estimators. We demonstrate that both the SPMLE and the pseudo‐SPMLE are consistent and asymptotically normal by the arguments based on the theory of modern empirical processes. The proposed approach is examined numerically via simulation and illustrated with an analysis of the motivating TB study data.

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.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.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.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.

This paper considers likelihood-based estimation under the Cox proportional hazards model in the situations where some covariate entries are missing not at random. Assuming the conditional distribu...

Additional file 1. Contains supplementary tables and figures as a Microsoft Word file

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.

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 twostage 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 motivate and illustrate this research.

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.

SUMMARY 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.

This paper considers estimation under the Cox proportional hazards model with right-censored event times in the presence of covariates missing not at random (MNAR). We propose an approach derived from likelihood estimation utilizing supplementary information. We show that available additional information not only helps to account appropriately for the missing covariates but also leads to estimation procedures which are natural and easy to implement. A medical example is used throughout the paper to motivate the problem and to illustrate the proposed methodology.

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.

This paper explores testing procedures with response-related incomplete data, with particular attention centered to pseudolikelihood ratio tests. We construct pseudolikelihood functions with the biased observations supplemented by auxiliary information, without specifying the association between the primary variables and the auxiliary variables. The asymptotic distributions of the test statistics under the null hypothesis are derived and finite-sample properties of the testing procedures are examined via simulation. The methodology is illustrated with an example involving evaluation of kindergarten readiness skills in children with sickle cell disease.

In many longitudinal studies, the individual characteristics associated with the repeated measures may be possible covariates of the time to an event of interest, and thus, it is desirable to model the time-to-event process and the longitudinal process jointly. Statistical analyses may be further complicated in such studies with missing data such as informative dropouts. This article considers a nonlinear mixed-effects model for the longitudinal process and the Cox proportional hazards model for the time-to-event process. We provide a method for simultaneous likelihood inference on the 2 models and allow for nonignorable data missing. The approach is illustrated with a recent AIDS study by jointly modeling HIV viral dynamics and time to viral rebound.

There are many practical situations where observation of the primary variable Y for individuals in a population is incomplete and depends on some auxiliary variables X that are potentially correlated with Y. We consider parameter estimation for the distribution of Y with the incomplete data, without specifying the underlying association between Y and X. The approach is based on a class of pseudoscore functions using available information of X. We demonstrate the consistency and asymptotic normality of the estimators and study their finite-sample properties in various situations via simulation. The methodology is illustrated by an example involving kindergarten readiness skills in children with sickle cell disease.

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.

In an attempt to identify similarities between methods for estimating a mean function with different types of response or observation processes, we explore a general theoretical framework for nonparametric estimation of the mean function of a response process subject to incomplete observations. Special cases of the response process include quantitative responses and discrete state processes such as survival processes, counting processes and alternating binary processes. The incomplete data are assumed to arise from a general response-independent observation process, which includes right- censoring, interval censoring, periodic observation, and mixtures of these as special cases. We explore two criteria for defining nonparametric estimators, one based on the sample mean of available data and the other inspired by the construction of Kaplan-Meier (or product-limit) estimator [J. Am. Statist. Assoc. 53 (1958) 457] for right-censored survival data. We show that under regularity conditions the estimated mean functions resulting from both criteria are consistent and converge weakly to Gaussian processes, and provide consistent estimators of their covariance functions. We then evaluate these general criteria for specific responses and observation processes, and show how they lead to familiar estimators for some response and observation processes and new estimators for others. We illustrate the latter with data from an recently completed AIDS clinical trial.

Hospitalization and cancer therapy can contribute to decreased food intake in children and adolescents with cancer, making it a challenge to meet their nutritional needs. The affect of hospitalization and the eating environment for pediatric oncology patients has not been studied very well, and the effect of altering the social aspect of mealtime for hospitalized pediatric oncology patients has not been studied at all. The authors conducteda randomized, prospective clinical trial to determine if hospitalized pediatric oncology patients consume more protein and calories when eating with afamily member or when eating alone in their room at mealtime. All food and beverage intake was recorded for 3 consecutive days, and a food service satisfaction survey was completed on Day 3. Food records were analyzed for calorie and protein intake, and surveys were analyzed for patient/parent satisfaction. The study was completed by 200 hospitalized patients and their parent/caregiver. Overall, neither calorie nor protein intake differed significantly between the two groups, but patient/parent satisfaction was significantly higher in the group of patients who dined with their caregiver. By using analysis of variance, the authors found that ideal body weight and years of sickness were significantly associated with calorie and protein intake.

This paper considers a study where each subject may experience multiple occurrences of an event and the rate of the event occurrences is of primary interest. Specifically, we are concerned with the situations where, for each subject, there are only records of the accumulated counts for the event occurrences at a finite number of time points over the study period. Sets of observation times may vary from subject to subject and differ between groups. We model the mean of the event occurrence number over time semiparametrically, and estimate the regression parameter. The proposed estimation procedures are illustrated with data from a bladder cancer study (Byar, 1980). Both asymptotics and simulation studies on the estimators are presented.

The 24-week extension of AIDS Clinical Trials Group Protocol 359, a study of human immunodeficiency virus (HIV)‐infected, indinavir-experienced patients, was designed to study the durability of “salvage” treatment regimens. Patients received saquinavir in combination with either ritonavir or nelfinavir and, in addition, delavirdine, adefovir, or both. Patients who demonstrated a virologic response at weeks 12‐16 were eligible to continue therapy in the extension through week 48. Of the 105 eligible subjects who were enrolled in the extension, 86 (82%) completed 48 weeks, and 49 (57%) of those 86 had HIV RNA levels 500 copies/ mL at week 48. For these 86 subjects who completed 48 weeks, the median change in CD4 cell count from baseline was +72 cells/mm 3 . Greater body weight, higher CD4 cell count, and greater degree of phenotypic susceptibility to indinavir and saquinavir at baseline were significantly associated with durable virologic suppression. These results show that some patients who experience treatment failure can demonstrate durable virologic and immunologic responses with salvage antiretroviral regimens. Current treatment guidelines recommend starting therapy for human immunodeficiency virus (HIV) infection with 2 nucleoside analogue reverse-transcriptase inhibitors in combination with 1 or 2 protease inhibitors or a nonnucleoside analogue reversetranscriptase inhibitor [1, 2]. However, 20%‐63% of patients from clinical cohorts experience virologic treatment failure while receiving combination antiretroviral therapy [3‐7]. Recent prospective studies have attempted to identify strategies for treatment of the treatment-experienced patient [8‐13], but these studies have focused primarily on 8‐24-week virologic responses to treatment. The durability of virologic and immunologic re

This study compared antiretroviral activity among 6 salvage therapy regimens. The study was a prospective, randomized, 2 × 3 factorial, multicenter study of the AIDS Clinical Trials Group. The study enrolled 277 human immunodeficiency virus (HIV)-infected patients naive to nonnucleoside analogues who had taken indinavir >6 months. The patients had 2000-200,000 HIV RNA copies/mL. Patients received saquinavir with ritonavir or nelfinavir together with delavirdine andlor adefovir and were followed for safety and antiretroviral response between baseline and week 16. At week 16, 30% (77/254) of patients had ≤500 HIV RNA copies/mL. Virologic response did not differ significantly between pooled ritonavir and nelfinavir groups (28% vs. 33%; P =.50) or between pooled delavirdine and delavirdine/adefovir groups (40% vs. 33%; P =.42). Pooled delavirdine groups had a greater virologic response rate than did adefovir groups (40% vs. 18%; P =.002). Overall, one-third of patients who experienced virologic failure on an indinavir-containing regimen suppressed virus load levels while they were taking a new salvage regimen.

In many clinical trials, treatment efficacy is based upon response to a biological marker that is measured repeatedly during the course of follow-up. However, in some of these trials it is not clear, a priori, how treatment effects on the marker may manifest themselves or what kinds of effects are clinically meaningful and/or acceptable. It is, therefore, desirable to allow flexibility in design and monitoring process by not prespecifying a stopping rule or even the parameter on which inferences will be based. Using the more general results in Hu and Lagakos, this paper extends the idea of the repeated confidence intervals for a parameter (Jennison and Turnbull) to repeated confidence bands for the mean function of a repeated measure process. We illustrate the approach and some considerations in its application with the results of a recent AIDS clinical trial.

Many practical situations involve a response variable Y and covariates X, where data on (Y,X) are incomplete for some portion of a sample of individuals. We consider two general types of pseudolikelihood estimation for problems in which missingness may be response-related. These are typically simpler to implement than ordinary maximum likelihood, which in this context is semiparametric. Asymptotics for the pseudolikelihood methods are presented, and simulations conducted to investigate the methods for an important class of problems involving lifetime data. Our results indicate that for these problems the two methods are effective and comparable with respect to efficiency. Plusieurs situations pratiques impliquent une variable de reponse Y et des covariables X, ou les donnees sur (Y, X) sont incompletes pour une certaine portion d'un echantillon d'individus. Nous considerons deux types generaux d'estimation de pseudo-vraisemblance pour des problemes dans lesquels l'absence de donnees peut ětre reliee a la reponse. Ceux-ci sont typiquement plus faciles a mettre en pratique que le maximum de vraisemblance ordinaire, qui, dans ce contexte, est semiparametrique. Nous presentons les proprietes asymptotiques pour les methodes de pseudovraisemblance, ainsi que les simulations effectuees pour etudier les methodes pour une classe importante de problemes impliquant des donnees de duree de vie. Nos resultats indiquent que, pour ces problemes, les deux methodes sont efficaces et comparables quant a leur efficacite.

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.

SUMMARY In epidemiology, reliability and other areas one encounters situations where response times and covariates for individuals in a population are observed only if the response time does not exceed an associated censoring time. Methods for obtaining supplementary information about covariates and censoring times for nonresponding individuals are considered. Estimation procedures are developed for the resulting combination of truncated data and supplementary data. Theoretical and simulation results are given, and the methodology illustrated on some automobile failure data collected under a warranty plan.

This article investigates data on recurrent events that arise from sources such as warranty claims, where the observation period for a unit is unknown until it experiences at least one event. This creates a type of truncation in the data. We consider nonparametric estimation of means and rates of the event occurrences with such “zero-truncated” data and examine the case where the population size and the distribution of observation times across units are at least approximately known. We study the behaviors of the proposed estimators by simulation. We examine some car warranty data by applying the methodology developed here and considering the underlying assumptions.

This paper considers nonparametric estimation of the mean function of a counting process based on periodic observations, i.e., panel counts. We present estimators derived through minimizing a class of generalized sums of squares subject to a monotonicity constraint. We establish consistency of the estimators and provide procedures to implement them with various weight functions. For specific weight functions, they reduce to the estimator given in Sun and Kalbfleisch (1995), and are closely related to the nonparametric maximum likelihood estimator studied in Wellner and Zhang (2000). With other weight functions, the proposed estimators provide alternatives that can have better efficiency in non-Poisson situations than previous approaches. Simulations are used to examine the finite-sample performance of the proposed estimators.

We develop nonparametric estimation procedures for the marginal mean function of a counting process based on periodic observations, using two types of self-consistent estimating equations. The first is derived from the likelihood studied by Wellner & Zhang (2000), assuming a Poisson counting process. It gives a nondecreasing estimator, which equals the nonparametric maximum likelihood estimator of Wellner & Zhang and is consistent without the Poisson assumption. Motivated by the construction of parametric generalized estimating equations, the second type is a set of data-adaptive quasi-score functions, which are likelihood estimating functions under a mixed-Poisson assumption. We evaluate the procedures using simulation, and illustrate them with the data from a bladder cancer study. Copyright 2009, Oxford University Press.

Randomized clinical trials with a multivariate response and/or multiple treatment arms are increasingly common, in part because of their efficiency and a greater concern about balancing risks with benefits. In some trials, the specific types and magnitudes of treatment group differences that would warrant early termination cannot easily be specified prior to the onset of the trial and/or could change as the trial progresses. This underscores the need for more flexible monitoring methods than traditional approaches. This paper extends the repeated confidence bands approach for interim monitoring to more general settings where there can be a multivariate response and/or multiple treatment arms and where the metrics for comparing treatment groups can change during the conduct of the trial. We illustrate the approach using the results of a recent AIDS clinical trial and examine its efficiency and robustness via simulation.

Background Moderate aplastic anemia (MAA) in children is a rare, idiopathic condition of bone marrow insufficiency that can resolve spontaneously, persist for months or years, or progress to severe aplastic anemia (SAA). We evaluated the rate of progression to SAA. Methods We reviewed the records of 136 children referred for evaluation of bone marrow failure from 1978 to 2002 at St. Jude Children's Research Hospital. MAA was defined by a hypocellular bone marrow (

Background Catheter-related bloodstream infections (CRBIs) are frequent complications of the use of long term central venous catheters (CVCs). Comparative quantitative culture of blood obtained via the CVC and a peripheral vein (PV) is a well-accepted method of diagnosing CRBI; however, an alternative definition for use when a PV culture is not available is desirable. Methods A computerized search of patient records identified all positive blood culture results from the St. Jude Children's Research Hospital Microbiology Laboratory between January 1996 and May 2001. Demographic data, catheter information and culture results were abstracted. Sensitivity, specificity, positive predictive value (PPV), and likelihood ratio were calculated for 2 alternative definitions of CRBI. Results Review of the medical records revealed 136 episodes of bacteremia that were evaluable for alternative definition 1 and 241 episodes that were evaluable for alternative definition 2. In patients with a double lumen CVC, CRBI can be diagnosed by a > or = 5-fold difference in colony-forming units/mL between the 2 lumens (alternative definition 1) with sensitivity, specificity, PPV and likelihood ratio of 61.8, 93.3, 92.2 and 9.22, respectively. In patients with a single or double lumen CVC, CRBI can be diagnosed when the CVC culture yields > or = 100 colony-forming units/mL (alternative definition 2) with sensitivity, specificity, PPV and likelihood ratio of 75.5, 69.1, 79.3, and 2.44, respectively. Conclusions Our study suggests that comparison of colony counts from 2 lumens of a double lumen catheter is acceptable for diagnosis of CRBI when a PV culture is not available. Further validation is needed before discontinuing the recommendation to obtain a PV culture.

Objective To evaluate the steady state concentrations of saquinavir, ritonavir, nelfinavir, delavirdine, and adefovir in six different three- and four-drug combination regimens. Design Randomized, partially double-blinded, multicenter study in a population of indinavir-experienced subjects with virologic failure. The first seven subjects enrolled in each of the six treatment arms from 10 participating sites were entered into this pharmacokinetic evaluation. Setting Multicenter study of the AIDS Clinical Trials Group (ACTG). Patients HIV-infected subjects. Interventions A 12-hour pharmacokinetic study was conducted after 2 weeks of drug administration. Main outcome measures Area under the concentration-time curve with statistical comparisons to evaluate the effect of the second protease inhibitor and the effect of the non-protease inhibitors. Results There was no difference in saquinavir concentrations according to whether the second protease inhibitor was ritonavir or nelfinavir. Saquinavir concentrations in the groups receiving the combination of delavirdine plus adefovir dipivoxil were reduced by approximately 50% compared with those receiving delavirdine. Delavirdine concentrations were reduced by approximately 50%, in the delavirdine plus adefovir dipivoxil arms compared with the delavirdine arms. Conclusions Saquinavir concentrations were significantly lower in the arms containing the combination of delavirdine and adefovir dipivoxil compared with the arms containing delavirdine. Delavirdine concentrations were significantly lower when coadministered with adefovir dipivoxil. These drug-drug interactions were not expected, the mechanism(s) is (are) not clear, and additional studies are warranted. This study illustrates the need to understand more fully the pharmacokinetic characteristics of complex combination antiretroviral regimens prior to use in patient management.