Early detection of clinical outcomes such as cancer may be predicted using longitudinal biomarker measurements. Tracking longitudinal biomarkers as a way to identify early disease onset may help to reduce mortality from diseases like ovarian cancer that are more treatable if detected early. Two disease risk prediction frameworks, the shared random effects model (SREM) and the pattern mixture model (PMM) could be used to assess longitudinal biomarkers on disease early detection. In this article, we studied the discrimination and calibration performances of SREM and PMM on disease early detection through an application to ovarian cancer, where early detection using the risk of ovarian cancer algorithm (ROCA) has been evaluated. Comparisons of the above three approaches were performed via analyses of the ovarian cancer data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Discrimination was evaluated by the time‐dependent receiver operating characteristic curve and its area, while calibration was assessed using calibration plot and the ratio of observed to expected number of diseased subjects. The out‐of‐sample performances were calculated via using leave‐one‐out cross‐validation, aiming to minimize potential model overfitting. A careful analysis of using the biomarker cancer antigen 125 for ovarian cancer early detection showed significantly improved discrimination performance of PMM as compared with SREM and ROCA, nevertheless all approaches were generally well calibrated. Robustness of all approaches was further investigated in extensive simulation studies. The improved performance of PMM relative to ROCA is in part due to the fact that the biomarker measurements were taken at a yearly interval, which is not frequent enough to reliably estimate the changepoint or the slope after changepoint in cases under ROCA.

中文翻译：

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使用纵向生物标志物进行疾病早期检测的统计方法：方法比较。

可以使用纵向生物标志物测量来预测临床结果（例如癌症）的早期检测。跟踪纵向生物标志物作为识别早期疾病发作的一种方法可能有助于降低卵巢癌等疾病的死亡率，如果及早发现，这些疾病更容易治疗。两种疾病风险预测框架，共享随机效应模型（SREM）和模式混合模型（PMM）可用于评估疾病早期检测的纵向生物标志物。在本文中，我们研究了 SREM 和 PMM 在疾病早期检测中的鉴别和校准性能，方法是将其应用于卵巢癌，其中评估了使用卵巢癌风险算法 (ROCA) 进行的早期检测。上述三种方法的比较是通过分析来自前列腺癌、肺癌、结直肠癌和卵巢癌筛查试验。通过时间依赖性接受者操作特征曲线及其面积评估辨别力，同时使用校准图和观察到的患病受试者数量与预期患病受试者数量的比率评估校准。通过使用留一法交叉验证计算样本外性能，旨在最大限度地减少潜在的模型过度拟合。对使用生物标志物癌症抗原 125 进行卵巢癌早期检测的仔细分析表明，与 SREM 和 ROCA 相比，PMM 的辨别性能显着提高，但所有方法通常都经过良好校准。在广泛的模拟研究中进一步研究了所有方法的稳健性。