Using the geometric average hazard ratio in sample size calculation for time-to-event data with composite endpoints

Sample size calculation for mixture model based on geometric average hazard ratio and its applications to nonproportional hazard

With the advent of cancer immunotherapy, some special features including delayed treatment effect, cure rate, diminishing treatment effect and crossing survival are often observed in survival analysis. They violate the proportional hazard model assumption and pose a unique challenge for the conventional trial design and analysis strategies. Many methods like cure rate model have been developed based on mixture model to incorporate some of these features. In this work, we extend the mixture model to deal with multiple non-proportional patterns and develop its geometric average hazard ratio (gAHR) to quantify the treatment effect. We further derive a sample size and power formula based on the non-centrality parameter of the log-rank test and conduct a thorough analysis of the impact of each parameter on performance. Simulation studies showed a clear advantage of our new method over the proportional hazard based calculation across different non-proportional hazard scenarios. Moreover, the mixture modeling of two real trials demonstrates how to use the prior information on the survival distribution among patients with different biomarker and early efficacy results in practice. By comparison with a simulation-based design, the new method provided a more efficient way to compute the power and sample size with high accuracy of estimation. Overall, both theoretical derivation and empirical studies demonstrate the promise of the proposed method in powering future innovative trial designs.

Prediction of trends in unfavorable prognosis in patients with acute ischemic stroke according to low left ventricular ejection fraction levels

As few studies have reported the impact of lower left ventricular ejection fraction (LVEF) on the prognosis of acute ischemic stroke (AIS) patients, we aimed to explore this through a retrospective cohort study and a meta-analysis. A total of 283 AIS patients receiving intravenous thrombolysis at the Third Affiliated Hospital of Wenzhou Medical University between 2016 and 2019 were enrolled and divided into three groups based on LVEF tertiles. The logistic regression model estimated the association between LVEF and the three-month AIS prognosis. After adjusting for confounding factors, patients in tertile 3 exhibited an increased risk of poor functional outcome and mortality [odds ratio (OR), 2.656 (95% CI: 1.443-4.889); OR, 7.586 (95% CI: 2.102-27.375)]. A systematic search of PubMed, EMBASE and Cochrane Library was performed. Our meta-analysis revealed that LVEF < 40% was significantly associated with poor functional outcome [OR 1.94 (95% CI: 1.08-3.50)], mortality [OR 3.69 (95% CI: 1.22-11.11)], as well as LVEF < 55% [OR 1.68 (95% CI: 1.22-2.32); 2.27 (95% CI: 1.30-3.96)], respectively. A decreased LVEF could predict an inferior prognosis for AIS; therefore, it could aid in clinical decision-making in this patient population.