Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1177/0962280209105024 http://scihub22266oqcxt.onion/10.1177/0962280209105024 C4806549!4806549!19654171     free     free     free       Stat+Methods+Med+Res 2010 ; 19 (1): 29-51 Nephropedia Template TP {{tp|p=19654171|t=2010. Survival analysis with high-dimensional covariates |pdf=|usr=}}{{19654171}} gab.com Text Survival analysis with high-dimensional covariates JO: Stat Methods Med Res http://www.kidney.de/pget.php?&tw=1&pmid=19654171 #FOAvip In recent years, breakthroughs in biomedical technology have led to a wealth of data in which the number of features (for instance, genes on which expression measurements are available) exceeds the number of observations (e.g. patients). Sometimes survival outcomes are also available for those same observations. In this case, one might be interested in (a) identifying features that are associated with survival (in a univariate sense), and (b) developing a multivariate model for the relationship between the features and survival that can be used to predict survival in a new observation. Due to the high dimensionality of this data, most classical statistical methods for survival analysis cannot be applied directly. Here, we review a number of methods from the literature that address these two problems. Twit Text FOAVip Survival analysis with high-dimensional covariates ????Stat Methods Med Res http://www.kidney.de/pget.php?&tw=1&pmid=19654171 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=19654171 #FOAvip English Wikipedia <ref>{{Cite pmid|19654171}}</ref> Survival analysis with high-dimensional covariates #MMPMID19654171 Witten DM; Tibshirani R Stat Methods Med Res 2010[Feb]; 19 (1): 29-51 PMID19654171show ga In recent years, breakthroughs in biomedical technology have led to a wealth of data in which the number of features (for instance, genes on which expression measurements are available) exceeds the number of observations (e.g. patients). Sometimes survival outcomes are also available for those same observations. In this case, one might be interested in (a) identifying features that are associated with survival (in a univariate sense), and (b) developing a multivariate model for the relationship between the features and survival that can be used to predict survival in a new observation. Due to the high dimensionality of this data, most classical statistical methods for survival analysis cannot be applied directly. Here, we review a number of methods from the literature that address these two problems. äSurvival analysis with high-dimensional covariates (epmc).pdf DeepDyve Pubget Overpricing