Statistical methods for gene/protein expression and palliative care

我的研究建议涉及的项目是基于与生物和健康科学家的联合研究。多重对应分析(CA)是一种描述性技术，旨在通过图形显示数据中的模式来调查行和列变量之间的关联。我建议探索和开发CA技术的变体，以识别差异表达的基因，并调查使用CA来评估复制DNA阵列的质量。多重对应分析(MCA)和称为联合对应分析(JCA)的相关技术是可视化两个或更多分类变量的联合特征的方法。我们一直在与UBC的遗传病理评估中心(GPEC)和不列颠哥伦比亚省癌症机构的乳腺癌预后股(BCOU)合作，研究分子标志物和乳腺癌预后之间的关系。一些研究人员使用附加点将生存信息纳入MCA分析，而不考虑审查。我建议开发技术，适应MCA和JCA对标志物数据的分析可能审查的结果。正如对生命受限疾病的患者一样，了解还有多少时间对于有关护理目标的决策通常很重要，研究表明，临床医生往往高估生存时间，从而证明需要客观的预后指标。缓解性表现量表(PPS)是一种与性别、疾病和年龄一起被证明是有效的生存预测指标的衡量标准。由于PPS轨迹不能被视为外部时间依赖协变量，我建议开发联合纵向/生存模型，以解释纵向数据的分类性质。

My research proposal involves projects that are grounded in joint research with biological and health scientists.      Correspondence analysis (CA) is a descriptive technique designed to investigate the association between row and column variables by graphically displaying the patterns in the data.  I propose to explore and develop variations of CA techniques to identify differentially expressed genes and to investigate the use of CA to assess the quality of replicate DNA arrays.   Multiple correspondence analysis (MCA) and a related technique called joint correspondence analysis (JCA) are methods for visualizing the joint features of 2 or more categorical variables. We have been working with the Genetic Pathology Evaluation Centre (GPEC) at UBC and the Breast Cancer Outcomes Unit (BCOU) at the B.C. Cancer Agency to study relationships between molecular markers and outcomes for breast cancer.  Some researchers have incorporated survival information in an MCA analysis using supplementary points without regard to censoring.  I propose to develop techniques that accommodate possibly censored outcomes in MCA and JCA analyses of marker data.     For patients with life-limited illness, knowing how much time remains is often important for decision-making regarding goals of care, treatment options and dealing with closure on personal and family matters.  Research has shown that clinicians tend to overestimate survival times thus demonstrating the need for objective prognostic indicators.  The Palliative Performance Scale (PPS) is one such measure that, together with gender, disease and age, has been shown to be an effective predictor of survival.  Since the PPS trajectory cannot be treated as an external time-dependent covariate, I propose to develop joint longitudinal/survival models that account for the categorical nature of the longitudinal data.