Group sequential clinical trials for longitudinal data with analyses using summary statistics.
Group sequential clinical trials for longitudinal data with analyses using summary statistics.
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对纵向数据进行分组序贯临床试验，并使用汇总统计进行分析。
DOI：
10.1002/sim.2127
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发表时间：
2005
影响因子：
2
通讯作者：
Emerson,ScottS
中科院分区：
文献类型：
--
作者：
Kittelson,JohnM;Sharples,Katrina;Emerson,ScottS
临床试验中使用纵向终点，结果分析通常使用个体内汇总统计数据进行。当对这些试验进行监测时，由于治疗效果的真实时间轨迹中的非线性偏差，包括随访不完整的受试者在内的中期分析可能会给出错误的决定。线性混合效应模型可用于消除这种偏差，但缺乏软件来支持这种情况下监测计划的设计和实施。本文考虑了一项临床试验，其中测量时间安排是固定的（至少对于试验前设计而言），并且通过这些测量时间的对比来参数化科学问题。此设置可确保在存在非线性时间轨迹的情况下进行可概括的推理。给出了使用纵向结果测量的中期分析中的治疗效果估计的分布，并提供了用于计算每个中期分析的信息量的软件。临时信息指定了分析时间，从而允许使用标准组序贯设计软件包进行具有纵向结果的试验。描述了实施这些设计的实际问题；特别是，当未根据预试验时间表测量结果时，提出了在中期分析中对治疗效果进行一致估计的方法。提供了使用适当的线性混合效应模型实现此推论的 Splus/R 函数。这些设计通过他汀类药物治疗外周动脉疾病症状的临床试验进行了说明。版权所有 © 2005 约翰·威利父子有限公司
Longitudinal endpoints are used in clinical trials, and the analysis of the results is often conducted using within‐individual summary statistics. When these trials are monitored, interim analyses that include subjects with incomplete follow‐up can give incorrect decisions due to bias by non‐linearity in the true time trajectory of the treatment effect. Linear mixed‐effects models can be used to remove this bias, but there is a lack of software to support both the design and implementation of monitoring plans in this setting. This paper considers a clinical trial in which the measurement time schedule is fixed (at least for pre‐trial design), and the scientific question is parameterized by a contrast across these measurement times. This setting assures generalizable inference in the presence of non‐linear time trajectories. The distribution of the treatment effect estimate at the interim analyses using the longitudinal outcome measurements is given, and software to calculate the amount of information at each interim analysis is provided. The interim information specifies the analysis timing thereby allowing standard group sequential design software packages to be used for trials with longitudinal outcomes. The practical issues with implementation of these designs are described; in particular, methods are presented for consistent estimation of treatment effects at the interim analyses when outcomes are not measured according to the pre‐trial schedule.Splus/Rfunctions implementing this inference using appropriate linear mixed‐effects models are provided. These designs are illustrated using a clinical trial of statin treatment for the symptoms of peripheral arterial disease. Copyright © 2005 John Wiley & Sons, Ltd.