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In long-term clinical studies, recurrent event data are frequently collected to contrast the efficacy of two different treatments. However, the recurrent event process can be stopped by a terminal event, such as death. For analyzing recurrent event and terminal event data, joint frailty modeling has recently received considerable attention because it makes it possible to study the joint evolution over time of both recurrent and terminal event processes and gives consistent and efficient parameters. For a two-arm clinical trial design based on these data sets, there has been limited research on investigating the balanced design, let alone adaptive treatment allocation. Although equal sample size allocation obtained for both treatments is intuitively first adopted in a trial design, if one treatment is expected to be superior, it may be desirable to allocate more subjects to the effective treatment. In this article, we calculate the required sample size based on restricted randomization and then propose a target response-adaptive randomization procedure for recurrent and terminal event outcomes based on the joint frailty model. A randomization procedure, the doubly adaptive biased coin design that targets some optimal allocations, is implemented. The proposed adaptive treatment allocation schemes have been shown to be capable of reducing the number of trial participants who receive inferior treatment while simultaneously reaching an optimal target, as well as retaining a comparable test power as compared to a restricted randomization design. Finally, two clinical studies, the COAPT trial and the A-HeFT trial, are used to illustrate the advantages of adopting the proposed procedure., (© 2021 John Wiley & Sons Ltd.)

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., (© 2020 The Authors Clinical Pharmacology & Therapeutics © 2020 American Society for Clinical Pharmacology and Therapeutics.)

We assessed the quality of life (QoL) associated with patient's characteristics and different cancer treatments among Chinese breast cancer survivors in Taiwan. A cross-sectional survey was conducted in 2017 where 193 patients with hormone receptor-positive/human epidermal growth factor receptor-2-negative metastatic breast cancer were recruited. Three QoL questionnaires were administered: European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30), its breast cancer supplementary measure (QLQ-BR23) and EQ-5D-5L. Multiple linear regression was performed to assess the association between QoL and cancer treatments, with adjustment for patient's characteristics. The mean age of study participants was 55.52 years. Simple linear regression showed that cancer stage and receiving chemotherapy were significantly associated with QoL scores (p < 0.05). Significant adverse effects of chemotherapy on QoL were found among early-stage cancer women (i.e., I or II), including poor cognitive and sexual functioning, and a higher symptom burden (i.e., dyspnoea, constipation, systematic therapy side effects). Multiple linear regression also revealed that receiving chemotherapy was significantly associated with poor QoL (e.g., lower functional health and higher symptom burden measured by the QLQ-BR23), compared to none chemotherapy (p < 0.05). Receiving chemotherapy was associated with poor QoL, especially among early-stage breast cancer patients., (© 2019 John Wiley & Sons Ltd.)

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., (© 2018 John Wiley & Sons, Ltd.)

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.)

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.