ONE-STEP OPTIMIZATION OF SCREENING FOR OVARIAN CANCER

长期目标是减少对妇女死亡的影响 通过高效的卵巢癌筛查技术，并开发 优化筛选的方法，以及更一般地监测 有数量标记的慢性病。卵巢癌死亡率 仅在美国，每年就超过12000人。CA 125是一个定量的 血清检测有可能成为卵巢癌的标志物； CA125水平高的女性患卵巢疾病的几率更高 癌症。由于卵巢癌的平均发病率只有50/10万 对于绝经后妇女，CA125没有一个单一的临界值 作为筛查试验所需的敏感性和特异性。 然而，在卵巢癌病例中，CA125随时间呈指数上升 癌症，基本上保持在非病例水平。此属性将 用来开发更敏感和更具体的筛查模式 在连续CA125水平上。为了基于该标准导出算法， CA125在两种情况下纵向行为的随机模型 将开发非病例。已从两个CA获取了序列数据 对大约5,000名和22,000名妇女进行了125次筛查试验 分别进行了分析。用于检验和估计适当性的方法 这些模型中来自数据的参数是：(I)贝叶斯推断 对于连续时间ARMA过程，以及(Ii)贝叶斯推断 分类分析。 具体目标是：1.开发针对行为的一步算法 最大限度地延长预期年限的卵巢癌筛查计划 挽救生命，同时限制不必要的手术； 2.建立随机模型：(I)卵巢的自然历史 癌症，(Ii)CA125水平的纵向行为，(Iii)长度 考虑到卵巢癌检测阶段的存活率，(Iv) 放射性引起的放射免疫分析的可变性&实验 可变性；3.开发筛选中的统计推断算法 程序以：(I)计算卵巢癌的概率，给定 妇女纵向CA125数据，(Ii)计算预期寿命 根据手术干预和CA 125数据保存，(Iii)派生 女性未来CA125值的预测分布，(Iv) 准确量化放射免疫分析的可变性；以及4.开发计算机 基于上述设计和推理算法的包，以及一个 最优的容易记住的规则，以便充分发挥 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.