New statistical models for analysing survival data in an intensive care unit context have recently been developed. Two models that offer significant advantages over standard survival analyses are competing risks models and multistate models. Wolkewitz and colleagues used a competing risks model to examine survival times for nosocomial pneumonia and mortality. Their model was able to incorporate time-dependent covariates and so examine how risk factors that changed with time affected the chances of infection or death. We briefly explain how an alternative modelling technique (using logistic regression) can more fully exploit time-dependent covariates for this type of data
Recently, regression analysis of the cumulative incidence function has gained interest in competing ...
Survival data analysis is a set of statistical methodologies that is used to model time until a cert...
Competing risks occur frequently in the analysis of survival data. A competing risk is an event whos...
New statistical models for analysing survival data in an intensive care unit context have recently b...
The thesis concerns regression models related to the competing risks setting in survival analysis an...
Time-dependent covariates are frequently encountered in regression analysis for event history data a...
Prognostic studies often involve modeling competing risks, where an individual can experience only o...
In survival analysis or medical studies each person can be exposed to more than one type of outcomes...
International audienceIn survival analysis, time-varying covariates are covariates whose value can c...
International audiencePatients are frequently exposed to failure from several mutually exclusive cau...
Despite decades of research in the medical literature, assessment of the attributable mortality due ...
Analyses of human mortality data classified according to cause of death frequently are based on comp...
University of Minnesota Ph.D. dissertation. May 2011. Major: Biostatistics. Advisor: Melanie M. Wall...
Abstract Background Joint modeling of longitudinal and survival data has been increasingly considere...
Advisors: Sanjib Basu; Nader Ebrahimi.Committee members: Alan M. Polansky; Duchwan Ryu; Ananda Sen; ...
Recently, regression analysis of the cumulative incidence function has gained interest in competing ...
Survival data analysis is a set of statistical methodologies that is used to model time until a cert...
Competing risks occur frequently in the analysis of survival data. A competing risk is an event whos...
New statistical models for analysing survival data in an intensive care unit context have recently b...
The thesis concerns regression models related to the competing risks setting in survival analysis an...
Time-dependent covariates are frequently encountered in regression analysis for event history data a...
Prognostic studies often involve modeling competing risks, where an individual can experience only o...
In survival analysis or medical studies each person can be exposed to more than one type of outcomes...
International audienceIn survival analysis, time-varying covariates are covariates whose value can c...
International audiencePatients are frequently exposed to failure from several mutually exclusive cau...
Despite decades of research in the medical literature, assessment of the attributable mortality due ...
Analyses of human mortality data classified according to cause of death frequently are based on comp...
University of Minnesota Ph.D. dissertation. May 2011. Major: Biostatistics. Advisor: Melanie M. Wall...
Abstract Background Joint modeling of longitudinal and survival data has been increasingly considere...
Advisors: Sanjib Basu; Nader Ebrahimi.Committee members: Alan M. Polansky; Duchwan Ryu; Ananda Sen; ...
Recently, regression analysis of the cumulative incidence function has gained interest in competing ...
Survival data analysis is a set of statistical methodologies that is used to model time until a cert...
Competing risks occur frequently in the analysis of survival data. A competing risk is an event whos...