图 书 馆
应用临床和基因组数据动态预测生存率Dynamic prediction of survival with clinical and genomic data |
|
| 课程网址: | http://videolectures.net/as2011_van_houwelingen_prediction/ |
| 主讲教师: | Hans C. van Houwelingen |
| 开课单位: | 莱顿大学 |
| 开课时间: | 2011-10-24 |
| 课程语种: | 英语 |
| 中文简介: |  生物统计学的重要临床应用是在诊断时开发用于患者预后的统计模型。在癌症中，通常的预后判断方法是借助x年生存率，例如x = 1、5或10。传统上，预后基于治疗开始时的临床信息，例如年龄，性别，肿瘤大小，肿瘤分期等。在过去的十年中，已经有了新类型的基因组信息，例如微阵列基因表达和蛋白质组学光谱数据。这种新型数据的问题在于它的丰富性。例如，微阵列可以测量成千上万个基因的表达。 演讲将解决三个问题： 如何基于高维基因组数据获得有效的预后模型。 如何评估基因组信息的附加值。 如何获取可靠的动态预测（后续的预测中可以使用这些预测） |
| 课程简介: | An important clinical application of biostatistics is the development of statistical models for the prognosis of a patient at the moment of diagnosis. In cancer the usual way of giving a prognosis is by means of the x-year survival probability, with x=1, 5 or 10, for example. Traditionally, the prognosis is based on clinical information at the start of the treatment, like age, gender, size of the tumor, tumor stage etc. In the last decade new types of genomic information have become available like micro-array gene expression and proteomic mass spectrometry data. The problem with this new type of data is its abundance. Micro-arrays can measure the expression of tens of thousands of genes, for example. The talk will address three issues: How to obtain valid prognostic model based on high-dimensional genomic data. How to assess the added value of the genomic information. How to obtain robust dynamic predictions (predictions available later on in the follow-up) |
| 关 键 词: | 应用临床; 基因组 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-09-27：zkj |
| 最后编审: | 2020-10-22：zyk |
| 阅读次数: | 34 |