Dynamic prediction of survival with clinical and genomic data
published: Oct. 24, 2011, recorded: September 2011, views: 4506
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !