INFORMATION BASED DESIGN AND INTERIM MONITORING SOFTWARE

描述：现在标准的做法是将正式的顺序 长期随机化的设计和实施的监测程序 临床试验临时监测可预先确定 以便数据和安全监测委员会可以决定 是否提前终止研究。然而，研究的完整性可能 如果积累的数据受到不适当的监控，则会严重受损。一 然后冒着将数据中的偶然波动归因于一个真实的 治疗效果因此，需要专门的统计方法， 附带的软件，可以平衡的道德和经济优势， 尽早停止研究，以避免得出错误结论的风险。研究 在过去的二十年里，在这一领域取得了迅速的进展， 是为了满足关键的实际需求。这些方法还没有 临床试验从业者可以接触到，因为不存在友好的， 专业开发和验证的软件在流行的 平台，如Microsoft Windows。第二阶段SBIR提案旨在 填补这个空白。目标是完成WinEaSt的开发， 组序贯临床试验设计和中期监测软件包 审判完成后，WinEaSt将：（1）执行组顺序推理 对于来自实际上任何类型的数学模型的感兴趣的参数 包括广义线性模型、参数和半参数生存 纵向研究的模型和混合模型。(2)允许自适应 重新估计样本量、招募期、总研究持续时间和其他 设计参数，在每个中期监测时间点，使用 基于信息的监测。 拟议商业应用：不可用

DESCRIPTION: It is now standard practice to incorporate formal sequential monitoring procedures into the design and conduct of long term randomized clinical trials. Interim monitoring provides an advance determination of efficacy and safety so that a data and safety monitoring board can decide whether to terminate the study early. However the integrity of a study can be seriously compromised if the accruing data are monitored inappropriately. One then runs the risk of attributing chance fluctuations in the data to a real therapeutic effect. Hence one needs specialized statistical methods and accompanying software that can balance the ethical and economic advantages of stopping a study early against the risk of an incorrect conclusion. Research has progressed rapidly in this area over the past two decades and methods have been developed to meet the key practical needs. These methods are not yet accessible to clinical trials practitioners because there exists no friendly, professionally developed and validated software implementing them on a popular platform such as Microsoft Windows. This Phase II SBIR proposal is intended to fill this void. The goal is to complete the development of WinEaSt, a Windows package for the design and interim monitoring of group sequential clinical trials. When completed, WinEaSt will: (1) Perform group-sequential inference for the parameters of interest from practically any type of mathematical model including generalized linear models, parametric and semi-parametric survival models and mixed models for longitudinal studies. (2) Permit adaptive re-estimation of sample size, accrual period, total study duration and other design parameters, at each interim monitoring time-point using information-based monitoring. PROPOSED COMMERCIAL APPLICATION: NOT AVAILABLE