...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics in medicine >One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information
【24h】

One-stage individual participant data meta-analysis models: estimation of treatment-covariate interactions must avoid ecological bias by separating out within-trial and across-trial information

机译：一阶段个体受试者数据荟萃分析模型：治疗-协变量相互作用的估计必须通过分离试验内和跨试验信息来避免生态偏倚

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Stratified medicine utilizes individual-level covariates that are associated with a differential treatment effect, also known as treatment-covariate interactions. When multiple trials are available, meta-analysis is used to help detect true treatment-covariate interactions by combining their data. Meta-regression of trial-level information is prone to low power and ecological bias, and therefore, individual participant data (IPD) meta-analyses are preferable to examine interactions utilizing individual-level information. However, one-stage IPD models are often wrongly specified, such that interactions are based on amalgamating within- and across-trial information. We compare, through simulations and an applied example, fixed-effect and random-effects models for a one-stage IPD meta-analysis of time-to-event data where the goal is to estimate a treatment-covariate interaction. We show that it is crucial to centre patient-level covariates by their mean value in each trial, in order to separate out within-trial and across-trial information. Otherwise, bias and coverage of interaction estimates may be adversely affected, leading to potentially erroneous conclusions driven by ecological bias. We revisit an IPD meta-analysis of five epilepsy trials and examine age as a treatment effect modifier. The interaction is -0.011 (95 CI: -0.019 to -0.003; p = 0.004), and thus highly significant, when amalgamating within-trial and across-trial information. However, when separating within-trial from across-trial information, the interaction is -0.007 (95 CI: -0.019 to 0.005; p = 0.22), and thus its magnitude and statistical significance are greatly reduced. We recommend that meta-analysts should only use within-trial information to examine individual predictors of treatment effect and that one-stage IPD models should separate within-trial from across-trial information to avoid ecological bias. (C) 2016 The Authors. Statistics in Medicine published by John Wiley Sons Ltd.
机译：分层医学利用与差异治疗效果相关的个体水平协变量，也称为治疗-协变量相互作用。当有多项试验可用时，meta分析用于通过结合其数据来帮助检测真正的治疗-协变量相互作用。试验水平信息的荟萃回归容易产生低功效和生态偏倚，因此，个体参与者数据 （IPD） 荟萃分析更可取，以利用个体水平信息检查相互作用。然而，单阶段IPD模型经常被错误地指定，因此相互作用是基于合并试验内和跨试验的信息。我们通过模拟和应用示例比较了固定效应和随机效应模型，用于对事件发生时间数据进行一阶段 IPD 荟萃分析，目的是估计治疗-协变量相互作用。我们表明，在每次试验中，通过患者水平的协变量的平均值居中至关重要，以便分离出试验内和跨试验的信息。否则，相互作用估计的偏差和覆盖率可能会受到不利影响，从而导致由生态偏倚驱动的潜在错误结论。我们重新审视了五项癫痫试验的IPD荟萃分析，并检查了年龄作为治疗效果调节因素。交互作用为-0.011（95%CI：-0.019至-0.003;p = 0.004），因此在合并试验内和跨试验信息时非常显著。然而，当将试验内信息与跨试验信息分开时，交互作用为-0.007（95%CI：-0.019至0.005;p = 0.22），因此其量级和统计学意义大大降低。我们建议meta分析人员应仅使用试验内信息来检查治疗效果的个体预测因子，并且一期IPD模型应将试验内信息与跨试验信息分开，以避免生态偏倚。（c） 2016年作者。Statistics in Medicine由John Wiley & Sons Ltd.出版。

著录项

相似文献

  • 外文文献
  • 中文文献
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号