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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.

We assessed the effect of atosiban on pregnancy outcomes following in vitro fertilization (IVF) treatment among infertile women requiring different numbers of embryo transfer (ET) cycles (i.e., one, two, and more than two ET cycles). A longitudinal cohort study was conducted by utilizing the data from the Assisted Reproductive Technology Center in a university tertiary hospital during 2007-2017. Patients receiving IVF treatment with at least one ET cycle were included. Pregnancy outcomes following IVF treatment, including biochemical, clinical, and ongoing pregnancies, were investigated. The association between atosiban and IVF pregnancy was assessed using logistic generalized estimating equation models, with adjustment for time-varying clinical characteristics (e.g., maternal age) across multiple ET cycles for an individual. 403 women with 838 ET cycles were included, where 165 patients required one ET cycle, 133 patients required two ET cycles (a total of 266 ET cycles), and 105 patients required more than two ET cycles (a total of 407 ET cycles). Atosiban use was not significantly associated with pregnancy outcomes among all study infertile women undergoing IVF treatment. However, the results for women requiring more than two ET cycles showed significantly increased pregnancy rates associated with atosiban use (i.e., odds ratios [95% confidence interval] of 4.40 [1.52, 12.73] and 2.85 [1.45, 5.60] for clinical and ongoing pregnancies, respectively). This association was not observed for the women requiring only one or two ET cycles. Atosiban is a potential treatment for enhancing IVF pregnancy, especially among infertile women requiring more than two ET cycles.

BackgroundThe use of atosiban during embryo transfer (ET), an oxytocin receptor antagonist, has been demonstrated to enhance pregnancy rate among infertile patients with endometriosis. However, its efficacy has not been assessed among those with concurrent adenomyosis, which may further affect pregnancy rate.MethodsThis retrospective cohort study assessed in vitro fertilization (IVF) pregnancy, and the effect of atosiban among 34 women with both endometriosis and adenomyosis (with 66 ET cycles), compared to 34 endometriosis patients without adenomyosis (with 54 ETs) and 38 patients with tubal factor (with 56 ETs). Multivariable logistic generalized estimating equation analyses were performed to assess pregnancy outcomes with adjustment for maternal and treatment characteristics.ResultsSignificantly higher chances of biochemical pregnancy and live birth among endometriosis patients without adenomyosis versus those with both endometriosis and adenomyosis were found (odds ratios [95% confidence intervals]: 2.981 [1.307, 6.803]; p=0.009, 2.694 [1.151, 6.304]; p=0.022). A significant positive association between atosiban use and biochemical pregnancy existed among endometriosis cases without adenomyosis (a 2.43-fold [1.01, 5.89] increase in successful pregnancy; p

Background Evidence is limited on excess risks of cardiovascular diseases (CVDs) associated with ambient air pollution in diabetic populations. Survival analyses without considering the spatial structure and possible spatial correlations in health and environmental data may affect the precision of estimation of adverse environmental pollution effects. We assessed the association between air pollution and CVDs in type 2 diabetes through a Bayesian spatial survival approach. Methods Taiwan’s national-level health claims and air pollution databases were utilized. Fine individual-level latitude and longitude were used to determine pollution exposure. The exponential spatial correlation between air pollution and CVDs was analyzed in our Bayesian model compared to traditional Weibull and Cox models. Results There were 2072 diabetic patients included in analyses. PM2.5 and SO2 were significant CVD risk factors in our Bayesian model, but such associations were attenuated or underestimated in traditional models; adjusted hazard ratio (HR) and 95% credible interval (CrI) or confidence interval (CI) of CVDs for a 1 μg/m3 increase in the monthly PM2.5 concentration for our model, the Weibull and Cox models was 1.040 (1.004–1.073), 0.994 (0.984–1.004), and 0.994 (0.984–1.004), respectively. With a 1 ppb increase in the monthly SO2 concentration, adjusted HR (95% CrI or CI) was 1.886 (1.642–2.113), 1.092 (1.022–1.168), and 1.091 (1.021–1.166) for these models, respectively. Conclusions Against traditional non-spatial analyses, our Bayesian spatial survival model enhances the assessment precision for environmental research with spatial survival data to reveal significant adverse cardiovascular effects of air pollution among vulnerable diabetic patients. Graphical abstract

Objective To investigate whether indicators of cortical excitability are good biomarkers of seizure controllability in temporal lobe epilepsy (TLE). Materials and methods Three groups of subjects were recruited: those with poorly controlled (PC) TLE (N = 41), well-controlled (WC) TLE (N = 71), and healthy controls (N = 44). Short- and long-latency recovery curves were obtained by paired-pulse transcranial magnetic stimulation. Linear mixed effect models were used to study the effects of group, interstimulus interval (ISI), and antiepileptic drugs on long-interval intracortical inhibition (LICI) and short-interval intracortical inhibition (SICI) and intracortical facilitation (ICF). Results The mixed effect model that did not incorporate antiepileptic drugs showed that group and ISI were significant factors for LICI and SICI/ICF. LICI in the healthy control group was greater than in the two epilepsy groups, and the difference was significant at ISIs of 50, 150, and 200 msec. In contrast, SICI/ICF in the PC group was greater than in the healthy control and WC groups, and the difference was significant at an ISI of 15 msec. However, due to large variance, it was difficult to identify a cutoff value with both good sensitivity and good specificity. Incorporating the information of antiepileptic drugs to the mixed effect model did not change the overall results. Conclusions Although LICI and SICI/ICF parameters were significantly different at the group level, they may not be suitable biomarkers for the controllability of TLE at the subject level.

To assess the effect of chemotherapy on mitochondrial genome mutations in cancer survivors and their offspring, a study sequenced the full mitochondrial genome and determined the mitochondrial DNA heteroplasmic (mtDNA) mutation rate. To build a model for counts of heteroplasmic mutations in mothers and their offspring, bivariate Poisson regression was used to examine the relationship between mutation count and clinical information while accounting for the paired correlation. However, if the sequencing depth is not adequate, a limited fraction of the mtDNA will be available for variant calling. The classical bivariate Poisson regression model treats the offset term as equal within pairs; thus, it cannot be applied directly. In this research, we propose an extended bivariate Poisson regression model that has a more general offset term to adjust the length of the accessible genome for each observation. We evaluate the performance of the proposed method with comprehensive simulations, and the results show that the regression model provides unbiased parameter estimations. The use of the model is also demonstrated using the paired mtDNA dataset.