COMBINING DIFFERENT DATA SOURCES TO ASSESS TREATMENTS

当临床试验的荟萃分析显示 研究之间的异质性，通过汇集处理进行总结 效果可能没有意义。在这笔赠款的第一阶段，我们使用了 贝叶斯分层回归模型探索这种现象的来源 异质性。推测异质治疗的大小 影响可能与控制率、基本基线有关 风险，我们开发了回归治疗效果的算法 控制针对测量误差进行适当调整的速率 控制率及治疗效果与控制效果的相关性 费率。分析了115项具有两种结果的荟萃分析，我们发现 在15%的病例中，与优势比显著相关。 进一步探索临床试验异质性的假设 源于不同亚群之间治疗效果的差异 根据试验的定义，我们寻求在这次继续中扩大我们的研究范围 通过实现以下目标来实现控制率回归：1)扩展 控制率回归在连续生存中的应用 时间结果、非线性和非正态模型以及荟萃分析 事件少的试验；2)确定控制率回归是否可以 改进对集中处理效果的估计；3)确定如何控制 不同试验之间的比率差异与报告的 风险因素；4)寻找报告风险因素汇总的新方法 这可能与结果的差异有更好的相关性；5)研究 Meta分析中控制率回归的泛化 来自不同类型的数据源，包括不同的医疗 以印刷和电子形式出版的专业；以及6)开发 控制率回归在前瞻性设计中的应用 临床试验。 临床试验结果的异质性提供了一个机会 通过揭示变异来源来优化治疗效益。控制 速率回归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.