COMBINING DIFFERENT DATA SOURCES TO ASSESS TREATMENTS

当临床试验的荟萃分析表明 研究间异质性，按汇总治疗总结 效果可能没有意义。 在第一阶段，我们使用 贝叶斯分层回归模型，以探索这一来源 异质性 推测异质处理的大小 效果可能与控制率、基础基线 风险，我们开发了回归治疗效果的算法， 控制率，适当调整测量误差， 控制率和治疗效果与控制的相关性 率分析了115项二元结果的荟萃分析，我们发现 在15%的病例中与比值比显著相关。 进一步探讨临床试验异质性 源自亚群间治疗疗效的差异 通过试验定义，我们在此继续寻求扩大我们的研究 通过实现以下目标来控制率回归：1）扩展 控制率回归在连续生存问题中的应用 时间结果，非线性和非正态模型以及荟萃分析 2）确定控制率回归是否可以 改善合并治疗效果估计; 3）确定如何控制 试验间的发生率差异与报告的 4）寻找新的方法来报告风险因素的摘要 这可能与结果的差异更好地相关; 5）研究 控制率回归在荟萃分析中的普遍性 从不同类型的数据源，包括不同的医疗 以印刷和电子格式出版的专业;以及6）开发 在前瞻性设计中应用控制率回归的工具 临床试验 临床试验结果之间的异质性提供了一个机会， 通过揭示变异来源优化治疗效益。 控制 速率回归可以是探索这一点的有效工具 异质性

When meta-analysis of clinical trials indicates substantial heterogeneity between studies, summarization by a pooled treatment effect may not be meaningful. In the first phase of this grant, we used Bayesian hierarchical regression models to explore sources of this heterogeneity. Speculating that the size of heterogeneous treatment effects might be related to the control rate, the underlying baseline risk, we developed algorithms for regressing treatment effects on control rates that properly adjusted for measurement error in the control rate and correlation between the treatment effect and control rate. Analyzing 115 meta-analyses with binary outcomes, we found significant correlation with the odds ratio in 15 percent of cases. To further explore the hypothesis that clinical trial heterogeneity derives from differences in treatment efficacy across subpopulations defined by the trials, we seek in this continuation to broaden our study of control rate regression by achieving the following aims: 1) Extend the application of control rate regression to continuous and survival time outcomes, to nonlinear and non-normal models and to meta-analysis of trials with few events; 2) Determine if control rate regression can improve estimation of pooled treatment effects; 3) Determine how control rate differences across trials correlate with differences in reported risk factors; 4) Search for new ways to report summaries of risk factors that might correlate better with differences in outcomes; 5) Study the generalizability of control rate regression in meta-analyses derived from different types of data sources including different medical specialties published in print and electronic formats; and 6) Develop tools to apply control rate regression in the prospective design of clinical trials. Heterogeneity among clinical trial results provides an opportunity to optimize treatment benefit by revealing sources of variation. Control rate regression can be an effective tool for exploring this heterogeneity.