Han Yongli, Albert Paul S, Berg Christine D, Wentzensen Nicolas, Katki Hormuzd A, Liu Danping
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
Stat Med. 2020 Dec 20;39(29):4405-4420. doi: 10.1002/sim.8731. Epub 2020 Sep 16.
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.
使用纵向生物标志物测量可以预测癌症等临床结果的早期检测。追踪纵向生物标志物作为识别疾病早期发作的一种方法，可能有助于降低卵巢癌等疾病的死亡率，如果早期发现，这些疾病更易于治疗。两种疾病风险预测框架，即共享随机效应模型（SREM）和模式混合模型（PMM），可用于评估疾病早期检测中的纵向生物标志物。在本文中，我们通过应用于卵巢癌研究了SREM和PMM在疾病早期检测中的区分和校准性能，其中使用卵巢癌风险算法（ROCA）进行早期检测已经得到评估。通过分析来自前列腺、肺、结肠和卵巢癌筛查试验的卵巢癌数据，对上述三种方法进行了比较。通过时间依赖的受试者工作特征曲线及其面积评估区分能力，同时使用校准图和患病受试者观察数与预期数的比率评估校准情况。通过留一法交叉验证计算样本外性能，旨在最小化潜在的模型过度拟合。对使用生物标志物癌抗原125进行卵巢癌早期检测的仔细分析表明，与SREM和ROCA相比，PMM的区分性能显著提高，不过所有方法总体上校准良好。在广泛的模拟研究中进一步研究了所有方法的稳健性。PMM相对于ROCA的性能改进部分是由于生物标志物测量是每年进行一次，这对于可靠估计ROCA情况下的变化点或变化点后的斜率来说不够频繁。
