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A Dashboard for Latent Class Trajectory Modeling: Application in Rheumatoid Arthritis

机译:延迟类轨迹建模的仪表板:在类风湿性关节炎中的应用

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A key trend in current medical research is a shift from a one-size-fit-all to precision treatment strategies, where the focus is on identifying narrow subgroups of the population that would benefit from a given intervention. Precision medicine will greatly benefit from accessible tools that clinicians can use to identify such subgroups, and to generate novel inferences about the patient population they are treating. We present a novel dashboard app that enables clinician users to explore patient subgroups with varying longitudinal treatment response, using latent class mixed modeling. The dashboard was developed in R Shiny. We present results of our approach applied to an observational study of patients with moderate to severe rheumatoid arthritis (RA) on first-line biologic treatment.
机译:目前的医学研究的一个关键趋势是从一个单尺寸适合所有到精密治疗策略的转变,其中重点是识别将从给定干预中受益的狭窄子组。 精密药物将极大地受益于临床医生可以用于识别这些亚组的可访问工具,并产生关于他们正在治疗的患者人口的新推论。 我们展示了一款新型仪表板应用程序,使临床医生能够利用潜在级混合建模,探索具有不同纵向治疗响应的患者子组。 仪表板是在R闪亮的开发的。 我们呈现我们的方法的结果,该方法适用于对一线生物治疗中度至重度类风湿性关节炎(RA)的患者的观察研究。

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