论文标题
查找和评估治疗效果临床试验数据中的甜点
Finding and assessing treatment effect sweet spots in clinical trial data
论文作者
论文摘要
在随机对照试验中鉴定异质治疗效果（HTE）是理解和对试验结果作用的重要一步。但是，HTE通常很小且难以识别，非常普遍的HTE建模方法可能会遭受低功率的影响。我们提出了一种利用疾病严重程度和治疗效果之间任何现有关系的方法，并确定了“甜点”，即估计的治疗益处的连续疾病严重程度的连续范围。我们进一步计算了在最佳位置的有条件平均治疗效果（CATE）的偏见校正估计值和$ p $值的估计值。因为我们确定了一个最佳位置和$ p $ - 价值，所以我们认为我们的方法是直接解释和可行的方法：我们方法的结果可以为未来的临床试验提供信息，并帮助临床医生提出个性化的治疗建议。
Identifying heterogeneous treatment effects (HTEs) in randomized controlled trials is an important step toward understanding and acting on trial results. However, HTEs are often small and difficult to identify, and HTE modeling methods which are very general can suffer from low power. We present a method that exploits any existing relationship between illness severity and treatment effect, and identifies the "sweet spot", the contiguous range of illness severity where the estimated treatment benefit is maximized. We further compute a bias-corrected estimate of the conditional average treatment effect (CATE) in the sweet spot, and a $p$-value. Because we identify a single sweet spot and $p$-value, we believe our method to be straightforward to interpret and actionable: results from our method can inform future clinical trials and help clinicians make personalized treatment recommendations.
