Sample Size Considerations of Prediction-Validation Methods in High-Dimensional Data for Survival Outcomes
نویسندگان
چکیده
منابع مشابه
Methods for regression analysis in high-dimensional data
By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...
متن کاملBayesian Sample Size Determination for Joint Modeling of Longitudinal Measurements and Survival Data
A longitudinal study refers to collection of a response variable and possibly some explanatory variables at multiple follow-up times. In many clinical studies with longitudinal measurements, the response variable, for each patient is collected as long as an event of interest, which considered as clinical end point, occurs. Joint modeling of continuous longitudinal measurements and survival time...
متن کاملFeature Selection for Small Sample Sets with High Dimensional Data Using Heuristic Hybrid Approach
Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...
متن کاملSparse kernel methods for high-dimensional survival data
UNLABELLED Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportiona...
متن کاملMethods for class prediction with high-dimensional gene expression data
An increasing amount of genomic data has become available. The work deals with class prediction with highdimensional gene expression data. Combining gene expression data with other data can improve the prediction of disease prognosis. The main part of the work is aimed at combining gene expression data with clinical data. We use logistic regression models that can be built through various regul...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Genetic Epidemiology
سال: 2013
ISSN: 0741-0395
DOI: 10.1002/gepi.21721