Dey R, Schaubel D E, Hanley J A, Saha-Chaudhuri P
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 0G3, Canada.
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104-6021, United States.
Biometrics. 2024 Oct 3;80(4). doi: 10.1093/biomtc/ujae150.
In many clinical contexts, the event of interest could occur multiple times for the same patient. Considerable advancement has been made on developing recurrent event models based on or that use biomarker information. However, less attention has been given to evaluating the prognostic accuracy of a biomarker or a composite score obtained from a fitted recurrent event-rate model. In this manuscript, we propose novel measures to characterize the prognostic accuracy of a marker measured at baseline in the presence of recurrent events. The proposed estimators are based on a semiparametric frailty model that accounts for the informativeness of a marker and unobserved heterogeneity among patients with respect to the rate of event occurrence. We investigate the asymptotic properties of the proposed accuracy estimators and demonstrate these estimators' finite sample performance through simulation studies. The proposed estimators have minimal bias and appropriate coverage. The estimators are applied to evaluate the performance of a baseline forced expiratory volume, a measure of lung capacity, for repeated episodes of pulmonary exacerbations in patients with cystic fibrosis.
在许多临床情况下，同一患者可能会多次出现感兴趣的事件。在基于生物标志物信息或使用生物标志物信息开发复发事件模型方面已经取得了相当大的进展。然而，对于评估生物标志物或从拟合的复发事件率模型获得的综合评分的预后准确性，关注较少。在本手稿中，我们提出了新的方法来表征在存在复发事件的情况下基线测量的标志物的预后准确性。所提出的估计器基于半参数脆弱模型，该模型考虑了标志物的信息性以及患者之间在事件发生率方面未观察到的异质性。我们研究了所提出的准确性估计器的渐近性质，并通过模拟研究证明了这些估计器的有限样本性能。所提出的估计器具有最小的偏差和适当的覆盖率。这些估计器被应用于评估基线用力呼气量（一种肺容量测量指标）在囊性纤维化患者肺部反复加重发作中的性能。
