Occurrence Of Recurrent Events Research Articles

Long-term successfull management of recurrent episodes of nontuberculous mycobacterial infection in an apparently healthy Chinese woman

BackgroundDisseminated non-tuberculous mycobacteria (dNTM) infections are mostly reported among individuals with an underlying congenital or acquired immunodeficiency or receiving immunosuppressive treatment, but are rarely documented in otherwise healthy subjects.Case presentationWe describe a case of recurrent disseminated mycobacterial infection in an apparently immunocompetent Chinese woman. Mycobacterium szulgai and Mycobacterium avium-complex were identified in distinct episodes. Long-term antimycobacterial therapy was administered given the occurrence of recurrent events when off-treatment. Successful management over more than 10 years and immunologic data are reported.ConclusionsThis case-report highlights that dNTM should be suspected also among apparently immunocompetent hosts and that thorough assessment of underling immune-impairments is helpful to define patients’ management. Long-term antimycobacterial therapy and close monitoring is required to grant successful outcomes in case of recurrent dNTM infections.

Group variable selection in the Andersen–Gill model for recurrent event data

In many scientific applications, such as biological studies, the predictors or covariates are naturally grouped. In this research, we consider the Andersen–Gill regression model for the analysis of recurrent event data with group covariates. In order to study the effects of the covariates on the occurrence of recurrent events, a bi-level penalized group selection method is introduced to address the group selection problem. A general group-bridge penalty function with varying weights is invoked to achieve the goal. It is shown that the performance of the bi-level selection depends on the weights. In order to select covariates more efficiently, especially for identifying the important covariates in important groups, adaptive weights are required. The asymptotic oracle properties of these methods are investigated. Three methods of tuning parameter selection are proposed. Our simulation studies show that the proposed method performs well in selecting important groups and important individual covariates in these groups simultaneously, and outperforms other popular group selection methods and the traditional unpenalized Wald testing method. We illustrate the method using a real life data set from medicine.

Dynamic prediction using joint models of longitudinal and recurrent event data: a Bayesian perspective

In cardiovascular disease (CVD) studies, the events of interest may be recurrent (multiple occurrences from the same individual). During the study follow-up, longitudinal measurements are often available and these measurements are highly predictive of event recurrences. It is of great clinical interest to make personalized prediction of the next occurrence of recurrent events using the available clinical information, because it enables clinicians to make more informed and personalized decisions and recommendations. To this end, we propose a joint model of longitudinal and recurrent event data. We develop a Bayesian approach for model inference and a dynamic prediction framework for predicting target subjects' future outcome trajectories and risk of next recurrent event, based on their data up to the prediction time point. To improve computation efficiency, embarrassingly parallel MCMC (EP-MCMC) method is utilized. It partitions the data into multiple subsets, runs MCMC sampler on each subset, and applies random partition trees to combine the posterior draws from all subsets. Our method development is motivated by and applied to the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT), one of the largest CVD studies to compare the effectiveness of medications to treat hypertension.

Evaluating Utility Measurement From Recurrent Marker Processes in the Presence of Competing Terminal Events

ABSTRACTIn follow-up studies, utility marker measurements are usually collected upon the occurrence of recurrent events until a terminal event such as death takes place. In this article, we define the recurrent marker process to characterize utility accumulation over time. For example, with medical cost and repeated hospitalizations being treated as marker and recurrent events, respectively, the recurrent marker process is the trajectory of cumulative cost, which stops to increase after death. In many applications, competing risks arise as subjects are at risk of more than one mutually exclusive terminal event, such as death from different causes, and modeling the recurrent marker process for each failure type is often of interest. However, censoring creates challenges in the methodological development, because for censored subjects, both failure type and recurrent marker process after censoring are unobserved. To circumvent this problem, we propose a nonparametric framework for the recurrent marker process with competing terminal events. In the presence of competing risks, we start with an estimator by using marker information from uncensored subjects. As a result, the estimator can be inefficient under heavy censoring. To improve efficiency, we propose a second estimator by combining the first estimator with auxiliary information from the estimate under noncompeting risks model. The large sample properties and optimality of the second estimator are established. Simulation studies and an application to the SEER-Medicare linked data are presented to illustrate the proposed methods. Supplementary materials for this article are available online.

Semiparametric Regression Estimation for Recurrent Event Data with Errors in Covariates under Informative Censoring.

Recurrent event data arise frequently in many longitudinal follow-up studies. Hence, evaluating covariate effects on the rates of occurrence of such events is commonly of interest. Examples include repeated hospitalizations, recurrent infections of HIV, and tumor recurrences. In this article, we consider semiparametric regression methods for the occurrence rate function of recurrent events when the covariates may be measured with errors. In contrast to the existing works, in our case the conventional assumption of independent censoring is violated since the recurrent event process is interrupted by some correlated events, which is called informative drop-out. Further, some covariates may be measured with errors. To accommodate for both informative censoring and measurement error, the occurrence of recurrent events is modelled through an unspecified frailty distribution and accompanied with a classical measurement error model. We propose two corrected approaches based on different ideas, and we show that they are numerically identical when estimating the regression parameters. The asymptotic properties of the proposed estimators are established, and the finite sample performance is examined via simulations. The proposed methods are applied to the Nutritional Prevention of Cancer trial for assessing the effect of the plasma selenium treatment on the recurrence of squamous cell carcinoma.

A general additive-multiplicative rates model for recurrent and terminal events

Recurrent events data with a terminal event (e.g. death) often arise in clinical and observational studies. Most of existing models assume multiplicative covariate effects and model the conditional recurrent event rate given survival. In this article, we propose a general additive-multiplicative rates model for recurrent event data in the presence of a terminal event, where the terminal event stop the further occurrence of recurrent events. Based on the estimating equation approach and the inverse probability weighting technique, we propose two procedures for estimating the regression parameters and the baseline mean function. The asymptotic properties of the resulting estimators are established. In addition, some graphical and numerical procedures are presented for model checking. The finite-sample behavior of the proposed methods is examined through simulation studies, and an application to a bladder cancer study is also illustrated.

Sustained risk of recurrent thromboembolic events in patients with patent foramen ovale and paradoxical embolism: long-term follow-up over more than 15 years

Patients with patent foramen ovale (PFO) and cryptogenic stroke are at risk of recurrence. Therapeutic regimens range from no treatment to anticoagulation treatment to surgical or interventional closure. However, long-term follow-up is only available for up to 4years. Among ~5,000 transesophageal echocardiographies in stroke/TIA-patients between 1988 and 1997, a PFO was found and considered a possible mediator for the neurological event in 97 patients. In these patients, the PFO was judged to be responsible for the neurological event. Patients with cardiac or other reasons for embolism were excluded. The therapy for stroke was chosen by the attending physician. Follow-up information was obtained through telephone interviews. Follow-up was available for 86 patients (89%) with a mean period of 15.4years (range, 11.2-25.9years). Thirteen patients (15%) suffered from recurrent ischemic events (7 TIAs, 5 strokes, 1 peripheral embolism) after a mean period of 4.9years. Four patients died, not associated with recurrent thromboembolism. The risk of recurrence was increased over the entire length of the mean follow-up period. The occurrence of recurrent events was not associated with differences in baseline data, the presence of ASA, PFO size or the chosen treatment. In patients with paradoxical embolism, recurrent ischemic events are frequent despite medical therapy. These events are not limited to the early years after the index event; this long-term follow-up revealed a risk of occurrence over the entire follow-up. These patients have a sustained risk of recurrence, requiring lifetime protection, which should be considered in tailoring individual therapeutic strategies.

Analyzing Recurrent Event Data With Informative Censoring

Recurrent event data are frequently encountered in longitudinal follow-up studies. In statistical literature, noninformative censoring is typically assumed when statistical methods and theory are developed for analyzing recurrent event data. In many applications, however, the observation of recurrent events could be terminated by informative dropouts or failure events, and it is unrealistic to assume that the censoring mechanism is independent of the recurrent event process. In this article we consider recurrent events of the same type and allow the censoring mechanism to be possibly informative. The occurrence of recurrent events is modeled by a subject-specific nonstationary Poisson process via a latent variable. A multiplicative intensity model is used as the underlying model for nonparametric estimation of the cumulative rate function. The multiplicative intensity model is also extended to a regression model by taking the covariate information into account. Statistical methods and theory are developed for estimation of the cumulative rate function and regression parameters. As a major feature of this article, we treat the distributions of both the censoring and latent variables as nuisance parameters. We avoid modeling and estimating the nuisance parameters by proper procedures. An analysis of the AIDS Link to Intravenous Experiences cohort data is presented to illustrate the proposed methods.

Large deviations for the numbers of occurrence of recurrent events

The abstracts (in two languages) can be found in the pdf file of the article.
 Original author name(s) and title in Russian and Lithuanian:
 А. Алешкявичене. Большие уклонения для числа появлений рекуррентного события
 A. Aleškevičienė. Didelių atsilenkimų teoremos rekurentinių įvykių pasirodymo skaičiui