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Recurrent events, including cardiovascular events, are commonly observed in biomedical studies. Understanding the effects of various treatments on recurrent events and investigating the underlying mediation mechanisms by which treatments may reduce the frequency of recurrent events are crucial tasks for researchers. Although causal inference methods for recurrent event data have been proposed, they cannot be used to assess mediation. This study proposed a novel methodology of causal mediation analysis that accommodates recurrent outcomes of interest in a given individual. A formal definition of causal estimands (direct and indirect effects) within a counterfactual framework is given, and empirical expressions for these effects are identified. To estimate these effects, a semiparametric estimator with triple robustness against model misspecification was developed. The proposed methodology was demonstrated in a real‐world application. The method was applied to measure the effects of two diabetes drugs on the recurrence of cardiovascular disease and to examine the mediating role of kidney function in this process. [ABSTRACT FROM AUTHOR]

Recurrent event and terminal event data commonly arise in clinical and observational studies. To evaluate the efficacy of a treatment effect for both types of events, a composite endpoint has been used as a possible assessment, particularly when faced with high costs and a longer follow‐up study. To model recurrent event processes complicated by the existence of a terminal event, joint frailty modeling has been typically employed. In this study, the objective was to develop some target‐driven response adaptive randomization strategies using a composite endpoint based on joint frailty modeling. We first implemented a balanced randomized design and then investigated the response adaptive randomization. The former is intuitively first adopted while the latter is expected to be desirable and ethical in terms of allocating more subjects to the more effective treatment. The results show that the proposed procedures using a composite endpoint are capable of reducing the number of trial participants who receive inferior treatment while simultaneously reaching a desired optimal target as compared to a balanced randomized design. The R shiny application for calculating the sample size and allocation probabilities is also available. Finally, two clinical trials were used as pilot datasets to introduce the proposed procedures. [ABSTRACT FROM AUTHOR]

Statistical methods for analyzing recurrent events have attracted significant attention. The majority of existing works consider situations in which subjects are observed over time periods and events of interest that occurred during the course of follow-up are recorded. In some applications, a subject may leave the study for a period of time and then resume due to various reasons. During the absence, which is referred to as an intermittent gap in this study, it may be impossible to observe a recording of the event. A naive analysis disregards gaps and considers events to be a typical recurrent event dataset. However, this may result in biased estimations and misleading results. In this study, we build an additive rates model for recurrent event data considering intermittent gaps. We provide the asymptotic theories behind the proposed model, as well as the goodness of fit between observed and modeled values. Simulation studies reveal that the estimations perform well if intermittent gaps are taken into account. In addition, we utilized the longitudinal cohort of elderly patients who have type 2 diabetes and at least one record of a severe recurrent complication, hypoglycemia, from the National Health Insurance Research Database in Taiwan to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

This study explored heterogeneous treatment effects (HTEs) of the real‐world use of dipeptidyl peptidase‐4 inhibitors (DPP‐4is) vs. sulfonylureas (SUs) on cardiovascular diseases (CVDs) and mortality in patients with type 2 diabetes. Utilizing Taiwan's National Health Insurance Research Database, 19,853 propensity score‐matched pairs of DPP‐4i and SU stable users were identified. Classification and regression tree analyses and Cox models were applied to explore HTEs, according to various patient characteristics, on the composite CVDs, three‐point major adverse cardiovascular event (MACE), and all‐cause mortality. The absolute risk difference (ARD), hazard ratio (HR), and 95% confidence interval (CI) were estimated for comparing treatment effects. CVD history, ischemic stroke, or transient ischemic attack (IS/TIA) history, and age at treatment initiation were significant treatment effect modifiers. Patients with prior IS/TIA but without any other prior CVDs benefited most in reduced risks of composite CVDs from using DPP‐4i vs. SU (ARD −4.31%, 95% CI −7.48% to −1.14%, HR 0.81, 95% CI 0.69 ~ 0.95), followed by those without prior IS/TIA and CVDs and initiated with DPP‐4i at age < 69.3 years (ARD −0.90%, 95% CI −1.47% to −0.32%, HR 0.86, 95% CI 0.77 ~ 0.97). Patients with prior IS/TIA benefited most in reduced risks of three‐point MACE from using DPP‐4i vs. SU (ARD −4.22%, 95% CV −6.66% to −1.78%, HR 0.80, 95% CI 0.69 ~ 0.93), followed by those without prior IS/TIA and initiated with DPP‐4i at age < 69.3 years (ARD −0.68%, 95% CI −1.08% to −0.29%, HR 0.81, 95% CI 0.70 ~ 0.93). Consideration of CVD and IS/TIA histories and age could facilitate individualized diabetes management of using DPP‐4i vs. SU. Future studies are warranted given the hypothesis‐generating nature in this exploratory research. [ABSTRACT FROM AUTHOR]

A comparison of 2 treatments with survival outcomes in a clinical study may require treatment randomization on clusters of multiple units with correlated responses. For example, for patients with otitis media in both ears, a specific treatment is normally given to a single patient, and hence, the 2 ears constitute a cluster. Statistical procedures are available for comparison of treatment efficacies. The conventional approach for treatment allocation is the adoption of a balanced design, in which half of the patients are assigned to each treatment arm. However, considering the increasing acceptability of responsive-adaptive designs in recent years because of their desirable features, we have developed a response-adaptive treatment allocation scheme for survival trials with clustered data. The proposed treatment allocation scheme is superior to the balanced design in that it allows more patients to receive the better treatment. At the same time, the test power for comparing treatment efficacies using our treatment allocation scheme remains highly competitive. The advantage of the proposed randomization procedure is supported by a simulation study and the redesign of a clinical study. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a formula for calculating the required sample size for paired recurrent events data. The developed formula is based on robust non-parametric tests for comparing the marginal mean function of events between paired samples. This calculation can accommodate the associations among a sequence of paired recurrent event times with a specification of correlated gamma frailty variables for a proportional intensity model. We evaluate the performance of the proposed method with comprehensive simulations including the impacts of paired correlations, homogeneous or nonhomogeneous processes, marginal hazard rates, censoring rate, accrual and follow-up times, as well as the sensitivity analysis for the assumption of the frailty distribution. The use of the formula is also demonstrated using a premature infant study from the neonatal intensive care unit of a tertiary center in southern Taiwan. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Aim The present study aimed to identify the health profiles of older adults by using latent class analysis to investigate health heterogeneity and to determine what factors predicted healthy aging among an oldest-old sample cohort that was followed up for 14 years in Taiwan. Methods Data were drawn from five waves (carried out in 1993, 1996, 1999, 2003 and 2007) of the Taiwan Longitudinal Study on Aging to examine the changes in health heterogeneity in a nationally representative oldest-old cohort of Taiwanese. Overall, data from a total of 11 145 observations of 3155 older adults were considered. The influential factors predicting health changes were analyzed by using a generalized estimating equation. Results The results showed that four health profiles were identified among the aging population observed in the Taiwan Longitudinal Study on Aging. With increasing age, the combined effects of the physical functioning, cognitive and emotional health, and comorbidities of older adults significantly impact their health changes. Apart from health deteriorating with age and sex disparities, educational and economic status, health behaviors, and social participation at the individual level were found to be the robust factors in predicting healthy aging. Conclusions In considering what factors impact healthy aging, we suggest that a person-centered approach would be useful and critical for policy makers to understand the compositions of health profiles and the influencing factors in view of a life-course perspective. Based on the factors identified as influencing healthy aging at the individual level, it is imperative from a policy-making perspective to maximize opportunities for healthy aging. Geriatr Gerontol Int 2017; 17: 697-707. [ABSTRACT FROM AUTHOR]

Current status data arise naturally from tumorigenicity experiments, epidemiology studies, biomedicine, econometrics and demographic and sociology studies. Moreover, clustered current status data may occur with animals from the same litter in tumorigenicity experiments or with subjects from the same family in epidemiology studies. Because the only information extracted from current status data is whether the survival times are before or after the monitoring or censoring times, the nonparametric maximum likelihood estimator of survival function converges at a rate of n 1∕3 to a complicated limiting distribution. Hence, semiparametric regression models such as the additive hazards model have been extended for independent current status data to derive the test statistics, whose distributions converge at a rate of n 1∕2, for testing the regression parameters. However, a straightforward application of these statistical methods to clustered current status data is not appropriate because intracluster correlation needs to be taken into account. Therefore, this paper proposes two estimating functions for estimating the parameters in the additive hazards model for clustered current status data. The comparative results from simulation studies are presented, and the application of the proposed estimating functions to one real data set is illustrated. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Clustered right-censored data often arise from tumorigenicity experiments and clinical trials. For testing the equality of two survival functions, Jung and Jeong extended weighted logrank (WLR) tests to two independent samples of clustered right-censored data, while the weighted Kaplan-Meier (WKM) test can be derived from the work of O'Gorman and Akritas. The weight functions in both classes of tests (WLR and WKM) can be selected to be more sensitive to detect a certain alternative; however, since the exact alternative is unknown, it is difficult to specify the selected weights in advance. Since WLR is rank-based, it is not sensitive to the magnitude of the difference in survival times. Although WKM is constructed to be more sensitive to the magnitude of the difference in survival times, it is not sensitive to late hazard differences. Therefore, in order to combine the advantages of these two classes of tests, this paper developed a class of versatile tests based on simultaneously using WLR and WKM for two independent samples of clustered right-censored data. The comparative results from a simulation study are presented and the implementation of the versatile tests to two real data sets is illustrated. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]