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Interval-censored survival data arise naturally in many fields such as medical follow-up studies, in which the event or failure is not observed exactly but only known to occur within a time interval. Most existing approaches for analyzing interval-censored failure time data assume that the examination times and the failure time are independent or conditionally independent given covariates. While this assumption offers considerable simplification, it is not plausible in some situations, e.g., the visiting rate can be positively or negatively correlated with the risk of failure due to unobservable health status even after adjusting for observable covariates. In this article, we consider dependent interval-censored data, where there exists dependence between the failure time and the entire visiting process. A shared frailty is used to characterize the dependence of hazard function of failure time and intensity function of visiting process. Moreover, the joint model could describe the possible none, positive or negative association between failure time and visiting process. We propose the semiparametric maximum likelihood estimators and develop an EM algorithm based on a Poisson data augmentation. The performance of the proposed method is examined through extensive simulation studies and an application to a bladder cancer dataset is presented. [ABSTRACT FROM AUTHOR]

Methods for joint analysis of longitudinal measures and right-censored survival outcomes have received much attention in the literature. However, in clinic and epidemiology research, the event of interest is only examined at the inspection time, resulting in the interval-censored failure time data. In this article, we construct a joint model for the longitudinal outcomes and the failure time, which is subject to interval censoring. We account for the dependence between the longitudinal measures and failure time through the incorporation of random effects. The semiparametric regression model with random effects and the Cox frailty model is used for the longitudinal outcome and the failure time, respectively. Spline functions are applied to approximate the infinite-dimensional baseline mean function and baseline cumulative hazard function in the joint model. The EM algorithm is used to find the maximum likelihood estimators and the estimated standard deviations are computed from the log observed-data likelihood. Extensive simulation studies are conducted to assess the performance of the proposed estimators. Simulation results show that the proposed method performs quite well. Finally, we illustrate the proposed approach by analyzing the chronic kidney disease. [ABSTRACT FROM AUTHOR]

Mixed case interval‐censored data arise when the event of interest is known only to occur within an interval induced by a sequence of random examination times. Such data are commonly encountered in disease research with longitudinal follow‐up. Furthermore, the medical treatment has progressed over the last decade with an increasing proportion of patients being cured for many types of diseases. Thus, interest has grown in cure models for survival data which hypothesize a certain proportion of subjects in the population are not expected to experience the events of interest. In this article, we consider a two‐component mixture cure model for regression analysis of mixed case interval‐censored data. The first component is a logistic regression model that describes the cure rate, and the second component is a semiparametric transformation model that describes the distribution of event time for the uncured subjects. We propose semiparametric maximum likelihood estimation for the considered model. We develop an EM type algorithm for obtaining the semiparametric maximum likelihood estimators (SPMLE) of regression parameters and establish their consistency, efficiency, and asymptotic normality. Extensive simulation studies indicate that the SPMLE performs satisfactorily in a wide variety of settings. The proposed method is illustrated by the analysis of the hypobaric decompression sickness data from National Aeronautics and Space Administration. [ABSTRACT FROM AUTHOR]

Aim: To explore the association between the sociodemographic factors and the needs of patients undergoing haemodialysis in Taiwan. Background: Concomitant discomfort, including physical and mental aspects, affects the patients' quality of life and their willingness to undergo haemodialysis. Maslow's hierarchy of needs is a well‐known tool to assess different levels of human needs. Method: We conducted a small‐scale cross‐sectional observational study using a structured needs assessment questionnaire on 159 patients from the Taipei Veterans General Hospital haemodialysis unit. Results: The overall mean scores of physical, mental, spiritual, other needs and needs in relation to medical staff care were 4.0 ± 0.8, 3.2 ± 0.8, 2.7 ± 1.0, 3.1 ± 0.9 and 4.1 ± 0.7, respectively. The results showed that the patients' highest need was in relation to medical staff care, followed by physical needs. Further analysis showed that patients who are still employed during the treatment process have higher mental, spiritual and other needs. Patient who is financially supported by their family has higher physical needs. Patients taken care of by paid caregivers have lower spiritual needs and other needs. This is also the same with patients who are religious as opposed to those who are nonreligious. Patients who have attained tertiary education have higher other needs compared with patients who have only achieved up to primary or secondary education. Conclusion: The study is the first in Taiwan to identify and quantify the needs of patients undergoing haemodialysis. When the needs of the patients are identified in relation to their sociodemographic factors, the medical staff can give the appropriate treatment in order to meet the needs and improve the patients' well‐being. Relevance to clinical practice: Healthcare providers should not only focus on the patients' physiological needs, but should determine and address their other needs in various aspects in order to improve the quality and efficacy of the dialysis care process. [ABSTRACT FROM AUTHOR]

This work is motivated by a nephrology study in Taiwan, where, after shunt implantation, dialysis patients may experience one of the two types, acute and non-acute, of shunt thrombosis, and each of them may alternatively recur in a patient. In this work, treating the two types of shunt thrombosis as competing risks, we assess covariate effects on the cumulative incidence probability function, or subdistribution, of gap times to the occurrences of acute shunt thrombosis. To accommodate potentially time-varying covariate effects, we extend a varying-coefficient subdistribution regression model to recurrent event analysis and propose associated estimation procedures. The inverse probability of censoring weighting technique is employed to ensure consistent estimation of the regression parameter. Asymptotic distributional theory is derived for the proposed estimator. Simulation results confirm that the proposed estimator performs well in finite samples. Application of the proposed analysis to the shunt thrombosis data reveals that dialysis patients with graft shunts and hypertension are associated with significantly increased incidence of acute shunt thrombosis. [ABSTRACT FROM AUTHOR]

In follow-up studies, the disease event time can be subject to left truncation and right censoring. Furthermore, medical advancements have made it possible for patients to be cured of certain types of diseases. In this article, we consider a semiparametric mixture cure model for the regression analysis of left-truncated and right-censored data. The model combines a logistic regression for the probability of event occurrence with the class of transformation models for the time of occurrence. We investigate two techniques for estimating model parameters. The first approach is based on martingale estimating equations (EEs). The second approach is based on the conditional likelihood function given truncation variables. The asymptotic properties of both proposed estimators are established. Simulation studies indicate that the conditional maximum-likelihood estimator (cMLE) performs well while the estimator based on EEs is very unstable even though it is shown to be consistent. This is a special and intriguing phenomenon for the EE approach under cure model. We provide insights into this issue and find that the EE approach can be improved significantly by assigning appropriate weights to the censored observations in the EEs. This finding is useful in overcoming the instability of the EE approach in some more complicated situations, where the likelihood approach is not feasible. We illustrate the proposed estimation procedures by analyzing the age at onset of the occiput-wall distance event for patients with ankylosing spondylitis. [ABSTRACT FROM AUTHOR]

Recurrent event data are commonly observed in biomedical longitudinal studies. In many instances, there exists a terminal event, which precludes the occurrence of additional repeated events, and usually there is also a nonignorable correlation between the terminal event and recurrent events. In this article, we propose a partly Aalen's additive model with a multiplicative frailty for the rate function of recurrent event process and assume a Cox frailty model for terminal event time. A shared gamma frailty is used to describe the correlation between the two types of events. Consequently, this joint model can provide the information of temporal influence of absolute covariate effects on the rate of recurrent event process, which is usually helpful in the decision-making process for physicians. An estimating equation approach is developed to estimate marginal and association parameters in the joint model. The consistency of the proposed estimator is established. Simulation studies demonstrate that the proposed approach is appropriate for practical use. We apply the proposed method to a peritonitis cohort data set for illustration. [ABSTRACT FROM AUTHOR]

Recurrent event data arise in longitudinal follow-up studies, where each subject may experience the same type of events repeatedly. The work in this article is motivated by the data from a study of repeated peritonitis for patients on peritoneal dialysis. Due to the aspects of medicine and cost, the peritonitis cases were classified into two types: Gram-positive and non-Gram-positive peritonitis. Further, since the death and hemodialysis therapy preclude the occurrence of recurrent events, we face multivariate recurrent event data with a dependent terminal event. We propose a flexible marginal model, which has three characteristics: first, we assume marginal proportional hazard and proportional rates models for terminal event time and recurrent event processes, respectively; second, the inter-recurrences dependence and the correlation between the multivariate recurrent event processes and terminal event time are modeled through three multiplicative frailties corresponding to the specified marginal models; third, the rate model with frailties for recurrent events is specified only on the time before the terminal event. We propose a two-stage estimation procedure for estimating unknown parameters. We also establish the consistency of the two-stage estimator. Simulation studies show that the proposed approach is appropriate for practical use. The methodology is applied to the peritonitis cohort data that motivated this study. [ABSTRACT FROM AUTHOR]

Background: Currently, the contribution of kidney function decline in renal and patient outcomes is unclear. There are few data on the associations of different etiologies of estimated glomerular filtration rate (eGFR) decline with outcomes in multidisciplinary care. The purpose of this investigation was to establish whether eGFR decline in patients with disease is an important risk factor for developing end-stage renal disease (ESRD) and death. Methods: From December 1, 2001 to December 31, 2011, 5097 adults with chronic kidney disease (CKD) received biochemical tests, physical examinations, a pathological examination, and a comprehensive questionnaire. We used linear regression models and multivariate Cox proportional hazards model to examine the outcome of eGFR decline in renal diseases with different etiologies. Results: Mean age was 68.1±16.1 (standard deviation, SD) years, and 63.3% patients were male. In the studied cohort, 58.2% of the patients had systemic disease-related nephropathy (SDRN), 29.4% had primary renal diseases (PRDs), and 12.4% had other etiologies. The eGFR decline in SDRN had a significant association with dialysis in the Cox proportional hazards model [crude hazard ratio (HR) = 1.07, 95% confidence interval (CI), 1.04 to 1.10; adjusted HR 1.05, 95% CI, 1.02 to 1.08]. Diabetic nephropathy (DN) had the most severe eGFR decline in CKD stages 3, 4, and 5, and all contributed to the initiation of dialysis and death regardless of whether DN with or without eGFR decline was considered to be the cause. Although hypertensive nephropathy (HN) was related to significant acceleration of eGFR decline, it did not lead to poor outcome. There were still discrepancies between eGFR decline and outcomes in PRDs, hypertensive nephropathy, and lupus nephritis. Conclusions: eGFR decline and CKD staging provide an informative guide for physicians to make proper clinical judgments in the treatment of CKD, especially SDRN. Poor control of the underlying systemic disease will thus lead to more rapid progression of SDRN. [ABSTRACT FROM AUTHOR]