• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Time-dependent dynamics of noncompliance and risk in clinical trials: Methods of assessment and sample size calculation.
【24h】

Time-dependent dynamics of noncompliance and risk in clinical trials: Methods of assessment and sample size calculation.

机译：临床试验中违规和风险随时间变化的动态：评估和样本量计算方法。

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

Most existing methods of sample size calculation for survival trials adjust the estimated outcome event rates for noncompliance assuming that noncompliance is independent of the endpoint risk although there has been published evidence (e.g., Coronary Drug Project Research Group, 1980; Snapinn et al., 2004) that noncompliers are often at a higher risk than compliers. More recent work (e.g., Jiang et al., 2004; Porcher et al., 2002) has started to consider the situations of informative noncompliance and different risks for noncompliers. However, the possibility of a time-varying association between noncompliance and risk has been ignored. Our analysis indicated a strong time-varying relationship between permanent withdrawal from study treatments and endpoint risk in the CONVINCE trial. In this dissertation, we introduce a method of sample size calculation which can account for a wide variety of relationships between the time dynamics of noncompliance and risk. The method is based on Lakatos Markov models. Using our method, we are able to study the impact of various assumptions on sample size calculation. Results show that sample size can vary dramatically with different assumptions about noncompliance and risk. Power can be seriously reduced if the assumed association does not agree with the real situation.; Our method requires trial planners to specify the potential time-varying pattern for the association between noncompliance and risk. In chapter 5, we study some methods including a new method for modeling a time-varying effect associated with a binary non-reversible time-dependent covariate in Cox regression. We first study the Zph method (Therneau and Grambsch, 1994) and show that it may not be the optimum method for the covariate of our interest. We then study a simple continuous approach using spline techniques and a simple discrete approach using partitioning the time axes related to the covariate. For the discrete approach, we introduce a Bayesian model with CAR priors to smooth the coefficient estimates. We also consider the penalized partial likelihood approach for smoothing, which is similar to but not exactly the same as the penalized likelihood approach of Heisey and Foong (1998). Our study show that all methods are reasonably effective.
机译：尽管有已发表的证据，大多数现有的用于生存试验的样本量计算方法都会调整不合规的估计结果事件发生率，前提是不合规与终点风险无关（例如，Coronary Drug Project Research Group，1980； Snapinn等，2004 ）不合规的风险通常比合规的风险更高。最近的工作（例如，Jiang等，2004； Porcher等，2002）已经开始考虑信息不合规的情况以及不合规的风险。但是，违规和风险之间时变关联的可能性已被忽略。我们的分析表明，在CONVINCE试验中，永久退出研究治疗与终点风险之间存在很强的时变关系。在本文中，我们介绍了一种样本量计算方法，该方法可以解决违规时间动态变化与风险之间的多种关系。该方法基于Lakatos Markov模型。使用我们的方法，我们能够研究各种假设对样本量计算的影响。结果表明，对于不合规和风险的不同假设，样本量可能会发生巨大变化。如果假定的关联与实际情况不一致，则可以严重降低权力。我们的方法要求试验计划者为违规和风险之间的关联指定潜在的时变模式。在第5章中，我们研究了一些方法，其中包括一种新方法，该方法用于建模与Cox回归中的二进制不可逆时变协变量相关的时变效应。我们首先研究了Zph方法（Therneau和Grambsch，1994），并表明它可能不是我们感兴趣的协变量的最佳方法。然后，我们研究使用样条技术的简单连续方法，以及使用划分与协变量相关的时间轴的简单离散方法。对于离散方法，我们引入具有CAR先验的贝叶斯模型以平滑系数估计​​。我们还考虑了惩罚性部分似然法进行平滑处理，该方法与Heisey和Foong（1998）的惩罚性似然法相似但不完全相同。我们的研究表明，所有方法都是合理有效的。

著录项

  • 作者

    Li, Bingbing.;

  • 作者单位

    University of Minnesota.;

  • 授予单位 University of Minnesota.;
  • 学科 Biology Biostatistics.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 171 p.
  • 总页数 171
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物数学方法;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
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