Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland.
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Cancer Epidemiol Biomarkers Prev. 2020 Jun;29(6):1196-1203. doi: 10.1158/1055-9965.EPI-19-1574. Epub 2020 Apr 10.
Independent validation of risk prediction models in prospective cohorts is required for risk-stratified cancer prevention. Such studies often have a two-phase design, where information on expensive biomarkers are ascertained in a nested substudy of the original cohort.
We propose a simple approach for evaluating model discrimination that accounts for incomplete follow-up and gains efficiency by using data from all individuals in the cohort irrespective of whether they were sampled in the substudy. For evaluating the AUC, we estimated probabilities of risk-scores for cases being larger than those in controls conditional on partial risk-scores, computed using partial covariate information. The proposed method was compared with an inverse probability weighted (IPW) approach that used information only from the subjects in the substudy. We evaluated age-stratified AUC of a model including questionnaire-based risk factors and inflammation biomarkers to predict 10-year risk of lung cancer using data from the Prostate, Lung, Colorectal, and Ovarian Cancer (1993-2009) trial (30,297 ever-smokers, 1,253 patients with lung cancer).
For estimating age-stratified AUC of the combined lung cancer risk model, the proposed method was 3.8 to 5.3 times more efficient compared with the IPW approach across the different age groups. Extensive simulation studies also demonstrated substantial efficiency gain compared with the IPW approach.
Incorporating information from all individuals in a two-phase cohort study can substantially improve precision of discrimination measures of lung cancer risk models.
Novel, simple, and practically useful methods are proposed for evaluating risk models, a critical step toward risk-stratified cancer prevention.
为了进行风险分层癌症预防，需要在前瞻性队列中对风险预测模型进行独立验证。此类研究通常具有两阶段设计，其中在原始队列的嵌套子研究中确定昂贵生物标志物的信息。
我们提出了一种简单的方法来评估模型区分度，该方法考虑了不完全随访，并通过使用队列中的所有个体的数据来提高效率，而不论他们是否在子研究中被抽样。为了评估 AUC，我们根据部分协变量信息，估计了病例的风险评分大于对照的条件概率，从而计算了条件概率。与仅使用子研究中受试者信息的逆概率加权（IPW）方法相比，提出了一种新的方法。我们使用前列腺癌、肺癌、结直肠癌和卵巢癌（1993-2009 年）试验的数据（30,297 名曾吸烟者，1,253 名肺癌患者），评估了包括问卷调查风险因素和炎症生物标志物在内的模型预测肺癌 10 年风险的年龄分层 AUC。
对于估计联合肺癌风险模型的年龄分层 AUC，与 IPW 方法相比，该方法在不同年龄组的效率提高了 3.8 到 5.3 倍。广泛的模拟研究也表明，与 IPW 方法相比，该方法具有显著的效率增益。
在两阶段队列研究中纳入所有个体的信息可以大大提高肺癌风险模型区分度测量的精度。
提出了用于评估风险模型的新颖、简单且实用的方法，这是风险分层癌症预防的关键步骤。
