This paper presents an approach to modeling progressive event-history data when the overall objective is prediction based on time-dependent co-variates. This approach does not model the hazard function directly. Instead, it models the process of the state indicators of the event history so that the time-dependent covariates can be incorporated and predictors of the future events easily formulated. Our model can be applied to a range of real-world problems in medical and agricultural science.
Recently interest has developed regarding the statistical properties and uses of marker processes in...
This dissertation focuses on developing new mathematical and statistical methods to properly represe...
Recently interest has developed regarding the statistical properties and uses of marker processes in...
In this article we introduce a general approach to dynamic path analysis. This is an extension of cl...
An important practical problem in the survival analysis is predicting the time to a future event suc...
Abstract In this article we introduce a general approach to dynamic path analysis. This is an extens...
There could be covariates that are related to transition probabilities, but not related to duration ...
Time-dependent covariates are frequently encountered in regression analysis for event history data a...
A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowada...
Predicting patient survival probabilities based on observed covariates is an important assessment in...
It is common in longitudinal studies to collect information on the time until a key clinical event, ...
A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowada...
In many healthcare settings it is of great interest to be able to predict the risk of events occurri...
We are dealing with the prediction of forthcoming outcomes of a categorical time series. We will ass...
University of Minnesota Ph.D. dissertation. May 2011. Major: Biostatistics. Advisor: Melanie M. Wall...
Recently interest has developed regarding the statistical properties and uses of marker processes in...
This dissertation focuses on developing new mathematical and statistical methods to properly represe...
Recently interest has developed regarding the statistical properties and uses of marker processes in...
In this article we introduce a general approach to dynamic path analysis. This is an extension of cl...
An important practical problem in the survival analysis is predicting the time to a future event suc...
Abstract In this article we introduce a general approach to dynamic path analysis. This is an extens...
There could be covariates that are related to transition probabilities, but not related to duration ...
Time-dependent covariates are frequently encountered in regression analysis for event history data a...
A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowada...
Predicting patient survival probabilities based on observed covariates is an important assessment in...
It is common in longitudinal studies to collect information on the time until a key clinical event, ...
A key question in clinical practice is accurate prediction of patient prognosis. To this end, nowada...
In many healthcare settings it is of great interest to be able to predict the risk of events occurri...
We are dealing with the prediction of forthcoming outcomes of a categorical time series. We will ass...
University of Minnesota Ph.D. dissertation. May 2011. Major: Biostatistics. Advisor: Melanie M. Wall...
Recently interest has developed regarding the statistical properties and uses of marker processes in...
This dissertation focuses on developing new mathematical and statistical methods to properly represe...
Recently interest has developed regarding the statistical properties and uses of marker processes in...