Extracting scalar measures from functional data with applications to placebo response.
Extracting scalar measures from functional data with applications to placebo response.
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DOI：
10.4310/20-sii633
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发表时间：
2021
影响因子：
0.8
通讯作者：
Ogden RT
中科院分区：
文献类型：
--
作者：
Tarpey T;Petkova E;Ciarleglio A;Ogden RT
在对照和观察性研究中，结局指标通常是纵向观察的。这些数据很难直接在各单位之间进行比较，因为曲线没有自然的顺序。这不仅在临床试验中是相关的，在临床试验中，通常的目标是评估平均治疗的相对疗效，而且在个性化医疗的日益增长和日益重要的领域中，治疗决策相对于相关的患者结果进行优化。在个性化医疗中，没有使用纵向结果优化治疗决策规则的方法，例如，症状轨迹，因为缺乏曲线的自然顺序。一个典型的做法是总结纵向响应的标量结果，然后可以在患者之间进行比较，治疗等，我们描述了一些常用的摘要，特别是在临床试验。我们考虑一个一般的总结措施（加权平均切线斜率）的权重，可以选择优化特定的推理取决于应用程序。我们说明了抑郁症治疗的研究，其中很难分开的抗抑郁药的具体效果安慰剂的影响的方法。我们认为，这种方法提供了一个更好的总结，估计积极治疗的好处比传统的非加权平均值。
In controlled and observational studies, outcome measures are often observed longitudinally. Such data are difficult to compare among units directly because there is no natural ordering of curves. This is relevant not only in clinical trials, where typically the goal is to evaluate the relative efficacy of treatments on average, but also in the growing and increasingly important area of personalized medicine, where treatment decisions are optimized with respect to a relevant patient outcome. In personalized medicine, there are no methods for optimizing treatment decision rules using longitudinal outcomes, e.g., symptom trajectories, because of the lack of a natural ordering of curves. A typical practice is to summarize the longitudinal response by a scalar outcome that can then be compared across patients, treatments, etc. We describe some of the summaries that are in common use, especially in clinical trials. We consider a general summary measure (weighted average tangent slope) with weights that can be chosen to optimize specific inference depending on the application. We illustrate the methodology on a study of depression treatment, in which it is difficult to separate placebo effects from the specific effects of the antidepressant. We argue that this approach provides a better summary for estimating the benefits of an active treatment than traditional non-weighted averages.
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DOI：
10.1111/insr.12163
发表时间：
2017-08
期刊：
International statistical review = Revue internationale de statistique
影响因子：
--
作者：
Reiss PT;Goldsmith J;Shang HL;Ogden RT
通讯作者：
Ogden RT
影响因子：
4.5
作者：
Qian M;Murphy SA
通讯作者：
Murphy SA
影响因子：
5.4
作者：
Norris, Fran H.;Tracy, Melissa;Galea, Sandro
通讯作者：
Galea, Sandro
影响因子：
--
作者：
Mallinckrodt, Craig H.;Lane, Peter W.;Mancuso, James P.
通讯作者：
Mancuso, James P.
DOI：
10.1007/s10488-011-0335-2
发表时间：
2011-11-01
影响因子：
2.6
作者：
Lindhiem, Oliver;Kolko, David J.
通讯作者：
Kolko, David J.