论文标题
纵向结果的单个索引模型，以优化个人治疗决策规则
A Single Index Model for Longitudinal Outcomes to Optimize Individual Treatment Decision Rules
论文作者
论文摘要
医学研究中的紧迫挑战是确定对个别患者的最佳治疗方法。这在心理健康环境中尤其具有挑战性，在心理健康环境中，平均反应在多种治疗中通常相似。例如，用活性药物和安慰剂治疗的患者的平均纵向轨迹可能非常相似，但不同的治疗可能表现出明显不同的单个轨迹形状。大多数精确的医学使用纵向数据通常忽略了纵向数据结构中的信息。本文通过检查基线协变量对纵向结果轨迹的影响来指导治疗决策，而不是从纵向数据（例如变化得分）中得出的传统标量结果指标，从而研究了一种强大的精确医学方法。我们介绍了一种估计“生物签名”的方法，该方法定义为基线特征的线性组合（即单个指数），该组合最佳地分离了不同治疗组之间的纵向轨迹。使用的标准是最大化不同治疗结果分布之间的kullback-leibler差异。通过模拟研究和抑郁临床试验来说明该方法。该方法还与更传统的方法形成鲜明对比，并在缺少数据的情况下比较性能。
A pressing challenge in medical research is to identify optimal treatments for individual patients. This is particularly challenging in mental health settings where mean responses are often similar across multiple treatments. For example, the mean longitudinal trajectories for patients treated with an active drug and placebo may be very similar but different treatments may exhibit distinctly different individual trajectory shapes. Most precision medicine approaches using longitudinal data often ignore information from the longitudinal data structure. This paper investigates a powerful precision medicine approach by examining the impact of baseline covariates on longitudinal outcome trajectories to guide treatment decisions instead of traditional scalar outcome measures derived from longitudinal data, such as a change score. We introduce a method of estimating "biosignatures" defined as linear combinations of baseline characteristics (i.e., a single index) that optimally separate longitudinal trajectories among different treatment groups. The criterion used is to maximize the Kullback-Leibler Divergence between different treatment outcome distributions. The approach is illustrated via simulation studies and a depression clinical trial. The approach is also contrasted with more traditional methods and compares performance in the presence of missing data.
