ONE-STEP OPTIMIZATION OF SCREENING FOR OVARIAN CANCER

长期目标是减少妇女死亡率的影响， 卵巢癌通过有效的筛查技术， 优化筛选和更一般地监测的方法， 慢性疾病的定量指标。 卵巢癌死亡率 仅在美国，每年就超过12,000人。 CA 125是一种定量的 有可能作为卵巢癌标志物的血清测量; CA 125水平较高的女性患卵巢癌的可能性较高， 癌 由于卵巢癌的平均发病率仅为50/10万， 对于绝经后妇女，CA 125没有单一的临界值， 需要灵敏度和特异性作为筛选试验。 然而，CA 125在卵巢癌患者中随时间呈指数增加， 癌症，基本上保持非病例水平。 此属性将 用于开发更敏感和更具体的筛查模式， 连续CA 125水平。 为了推导出基于该准则的算法， CA 125纵向行为的随机模型， 将开发非案例。 从两个CA获得了序列数据 125项筛查试验，涉及约5，000和22，000名女性 分别 用于测试适当性和估计的方法 这些模型中的参数来自数据：（i）贝叶斯推断 连续时间阿尔马过程，和（ii）贝叶斯推理 分类分析 具体目标是：1.开发一步算法， 最大化预期寿命的卵巢癌筛查项目 挽救生命，同时限制不必要手术的比例; 2.发展随机模型：（一）自然历史的卵巢 癌症，（ii）CA 125水平的纵向行为，（iii） （iv）卵巢癌的发病率和死亡率; 放射免疫测定的变异性，由于放射性和实验 可变性; 3.开发筛选统计推断算法 程序：（i）计算卵巢癌的概率， 妇女的纵向CA 125数据，（ii）计算预期寿命 保存给定的手术干预和CA 125数据，（iii）导出 女性未来CA 125值的预测分布，（iv） 准确定量放射免疫测定变异性;和4.开发计算机 基于上述设计和推理算法的软件包，以及 最佳的容易记住的规则，使充分的潜力， 可以实现在临床实践中的适用性。

The long term objectives are to lessen the mortality impact on women of ovarian cancer through efficient screening techniques, and to develop methodology for optimizing screening, and more generally monitoring, of chronic diseases with quantitative markers. Ovarian cancer mortality rate in the US alone is over 12,000 per year. CA 125 is a quantitative serum measurement that has potential as a marker for ovarian cancer; women with higher levels of CA 125 have a higher probability of ovarian cancer. Since ovarian cancer has an average incidence of only 50/100,000 for postmenopausal women, no single cutoff level for CA 125 has the required sensitivity and specificity to be used as a screening test. However, CA 125 increases exponentially with time in cases of ovarian cancer, and essentially remains level for non-cases. This property will be used to develop a more sensitive and specific screening modality based on serial CA 125 levels. To derive algorithms based on this criterion, stochastic models of the longitudinal behavior of CA 125 in cases and non-cases will be developed. Serial data have been obtained from two CA 125 screening trials of approximately 5,000 and 22,000 women respectively. The methods used to test the appropriateness and estimate the parameters in these models from the data are (i) Bayesian inference for continuous time ARMA processes, and (ii) Bayesian inference for classification analysis. The specific aims are: 1. To develop one step algorithms for the conduct of screening programs for ovarian cancer which maximizes expected years of life saved, while constraining the fraction of unnecessary surgeries; 2. develop stochastic models for: (i) the natural history of the ovarian cancer, (ii) the longitudinal behavior of CA 125 levels, (iii) the length of survival given the stage at detection of ovarian cancer, (iv) the variability of radioimmunoassays, due to radioactivity & experimental variability; 3. develop algorithms for statistical inference in screening programs to: (i) calculate the probability of ovarian cancer given the woman's longitudinal CA 125 data, (ii) calculate expected years of life saved given surgical intervention and CA 125 data, (iii) derive predictive distributions for a woman's future CA 125 values, (iv) accurately quantify radioimmunoassay variability; and 4. develop computer packages based on the above design and inference algorithms, and an optimal easily remembered rule so that the full potential for applicability in clinical practice can be realized.