Assessment of data quality in an international multi-centre randomised trial of coronary artery surgery

Reducing the Adverse Maternal and Fetal Outcomes in IVF Women by Exercise Interventions During Pregnancy

Purpose: The role of a structured program of exercise training on the low-risk pregnancy in Iranian women undergoing in vitro fertilization (IVF) based on the reduction of gestational diabetes was examined. Method: A comparative quasi-experimental clinical trial with 170 IVF-pregnant women in two intervention and control groups was studied to monitor the training effect of physical activities in terms of walking, and aerobic, strength-conditioning, and relaxation exercises during pregnancy on the improvement of maternal and fetal health. Gestational diabetes as the main complication was evaluated with 2-h 75-g OGTT and FBS tests in 24-28 and 34- weeks' gestation, respectively. Other adverse maternal (e.g., preeclampsia, cesarean section, and preterm delivery) and neonatal (e.g., intrauterine growth retardation, low/high birth weight, and fetal death) outcomes were determined and analyzed by statistical and regression tests. Results: A significantly lower gestational diabetes and preeclampsia rate was found in the intervened IVF-pregnant women with the maintenance of an active lifestyle. Although there was no significant difference in other negative maternal and neonatal outcomes between the two groups, lower symptoms from these complications were recorded in IVF-pregnant women trained with regular exercise activities. Conclusion: Implementing the physical activity program by IVF-women during pregnancy is an effective intervention strategy in reducing the risk of adverse maternal and fetal outcomes.

Simulation optimization for Bayesian multi-arm multi-stage clinical trial with binary endpoints

Multi-arm multi-stage designs, in which multiple active treatments are compared to a control and accumulated information from interim data are used to add or remove arms from the trial, may reduce development costs and shorten the drug development timeline. As such, this adaptive update is a natural complement to Bayesian methodology in which the prior clinical belief is sequentially updated using the observed probability of success. Simulation is often required for planning clinical trials to accommodate the complexity of the design and to optimize key design characteristics. This paper addresses two key limiting factors in simulations, namely the computational burden and the time needed to obtain results. We first introduce a generic process for simulating Bayesian multi-arm multi-stage designs with binary endpoints. Then, to address the computational burden and time, we optimize the method for calculating the posterior probability and posterior predictive probability of success.