Statistical Methods in Diagnostic Medicine and Measures of Agreement

诊断预测因子的开发以及评估评估者或设备之间的一致性对于流行病学、统计学和预防研究部的许多研究都很重要。 这些数据的特点是复杂的，往往需要新的统计方法来充分解决这些分析问题。 自然驾驶研究的一个目标是预测青少年在撞车时或撞车前的不良驾驶行为中撞车或险些撞车的发生。 开发和评估预测器的性能是复杂的，因为测量是在长时间的真实的时间内进行的，并且一些人比其他人有更多的碰撞。此外，预测器的质量本身可能与在给定青少年身上观察到的接近碰撞的数量有关（对于那些有更多碰撞的人，预测的质量可能更差）。 我们计划在开发这个预测器时开发一个集群内reservation方法。虽然建立了开发预测器的方法（尽管这将是该方法的新应用），但在开发验证预测器的方法方面还需要进一步的研究。我们将研究不同的交叉验证程序，这个问题，并概括我们的工作，以制定指导方针，开发和验证预测的纵向数据时，事件的数量是翔实的。 预测排卵的发生和时间是生殖流行病学的一个重要目标。生物周期研究收集了大约250名妇女两个月经周期的排卵发生和时间的信息。 我们将使用多变量纵向生物标志物数据来开发该事件的预测因子。新的统计方法将需要估计这些预测模型，以及验证其预测性能。 评估者之间的一致性措施已经制定了评估者在一个单一的时间，以及随着时间的推移。最近，ENDO研究遇到了一个未解决的分析问题，其中的协议是单调增加。具体而言，一组专家评分员在一系列时间点的每一个都呈现有越来越多信息的病例。人们的兴趣在于测试是否随着时间的推移而增加这种额外的信息。将在该项目下为这一问题制定新的统计方法。

The development of diagnostic predictors as well as assessing agreement between raters or devices is important for many studies in the Division of Epidemiology, Statistics, and Prevention Research. Features of these data are complex and new statistical methodology is often required to adequately address these analytic issues. A goal in the natural driving study is to predict the occurrence of crashes or near crashes among teenagers from poor driving behavior at or before the time of the crash. Developing and assessing the performance of a predictor is complex since measurements are taken in real time over a long period, and some individuals have many more crashes than others. Also, the quality of the predictor may itself be related to the number of near crashes observed on a given teenager (the quality of the prediction may be worse for those who have more crashes). We plan on developing a within-cluster resampling approach in developing this predictor. Although the methodology for developing the predictor is established (although this would be a novel application of this methodology), further research will be needed in developing methodology for validating the predictor. We will examine different cross-validation procedures for this problem and generalize our work in order to develop guidelines for developing and validating predictors from longitudinal data when the number of events is informative. Predicting the occurrence and time of ovulation is an important goal in reproductive epidemiology. The biocycle study collects information on the occurrence and time of ovulation for two menstrual cycles on approximately 250 women. We will use multivariate longitudinally biomarker data in developing a predictor of this event. New statistical methodology will be required for estimating these predictive models as well as to validate their predictive performance. Measures of agreement between raters have been developed for raters at a single time as well as raters over time. Recently, the ENDO study encountered an unsolved analytic problem in which the agreement is monotonically increasing. Specifically, a group of expert raters are presented with a case with an increasing amount of information at each of a series of time points. Interest is on testing whether the agreement is increasing over time with this additional information. New statistical methodology will be developed for this problem under this project.