Innovative approaches for analyzing SEER breast cancer data

描述（由申请人提供）：监测，流行病学和最终结果（SEER）计划是美国癌症统计数据的主要来源。适当和有效地利用SEER计划的可用资源符合公共和国家利益。因此，我们提出了估计5年生存概率的创新方法，确定重要的生存预测因子，并估计预测变量对生存的影响。 使用SEER数据的癌症患者的时间。特别是，我们考虑乳腺癌生存数据，因为它是女性中最常见的癌症类型。 由于数据的删失性质和许多参数的存在（高维问题），根据几种疾病特征和人口统计学因素对生存时间进行建模具有挑战性。在目标A中，我们考虑了加速失效时间（AFT）型模型，并提出了一个非参数贝叶斯解决方案。该解决方案涉及根据与疾病特征和人口统计学因素相对应的许多参数对平均值进行建模，并将方差建模为平均值的平滑非参数函数。AFT模型的非参数误差分布通过约束Dirichlet过程先验处理。由于均值包含大量的参数，采用变量选择技术来降低问题的有效维数。本文的主要创新之处在于从一个前所未有的真实的和普遍的角度来看待AFT模型。 SEER数据库中的许多疾病特征包含显著比例的缺失值。忽略任何疾病特征中伴有缺失值的受试者可能会扭曲结论，并且肯定会降低检测生存时间和预测变量之间潜在关联的能力。在目标B中，我们提出了一个半参数方法处理缺失的预测变量的线性变换模型，一个半参数模型，其中包含的比例风险和比例优势模型作为两个特殊情况。这一部分的主要创新之处在于我们如何处理缺失数据，以及如何在无限维参数的情况下对有限维参数进行推断。 最后，我们提出的方法允许从现代流行病学的角度分析结果的有用和准确的解释。我们的模型是广泛的，我们寻求一个无分布的过程来估计模型参数，无论是在存在许多预测或在存在一个缺失的预测。

DESCRIPTION (provided by applicant): The Surveillance, Epidemiology and End Results (SEER) Program is a premier source for cancer statistics in the United States. Proper and efficient use of the available resources from the SEER program is of public and national interest. Therefore, we propose innovative methods for estimating 5-year survival probability, identifying important predictors for survival, and estimating the effect of predictor variables on the survival time of cancer patients using the SEER data. In particular, we consider breast cancer survival data as it is the most common type of cancer among women. Modeling survival time in terms of several disease characteristics and demographic factors is challenging due to the censored nature of the data and the presence of many parameters (high- dimensional problem). In Aim A, we consider an accelerated failure time (AFT) type model, and propose a nonparametric Bayesian solution to this problem. The solution involves modeling mean in terms of many parameters corresponding to the disease characteristics and demographic fac- tors, and modeling variance as a smooth nonparametric function of the mean. The nonparametric error distribution of the AFT model is handled via a constrained Dirichlet process prior. A variable selection technique is adopted to reduce the effective dimension of the problem as the mean involves a large number of parameters. The main innovation is treating the AFT model from such a real and general perspective which no one has done it before. Many of the disease characteristics in the SEER database contain significant proportion of missing values. Ignoring the subjects accompanied with missing values in any disease characteristic may distort the conclusion, and would definitely reduce the power to detect a potential association between the survival time and predictor variables. In Aim B we propose a semiparametric method of handling a missing predictor variable in the linear transformation model, a semiparametic model which contains the proportional hazard and the proportional odds model as two special cases. The main innovation of this part is how we handle missing data, and make inference about a finite dimensional parameter in the presence of an infinite-dimensional parameter. Finally, our proposed methods permit a useful and accurate interpretation of results of the analysis from modern epidemiological perspective. Our models are broad, and we seek a distribution- free procedure to estimate the model parameters either in the presence of many predictors or in the presence of a missing predictor.