MODELING TRANSITION PROBABILITIES AND DURATION TIME FOR EVENT HISTORY DATA

Biostatistical approaches for modeling longitudinal and event time data.

Introduction Prostate cancer is the second leading cause of cancer death in United States men, ranking only after lung cancer. In 2002, it is estimated that there will be 189,000 new prostate cancer cases and 30,200 deaths from prostate cancer (1). Early detection of prostate cancer is the focus of today’s clinical research, but its major hindrance is the lack of early symptoms. The introductio...

Transition Probabilities and Duration Analysis among Disability States: Some Evidence from Spanish Data

In this paper we study the disability transition probabilities (as well as the mortality probabilities) due to concurrent factors to age such as income, gender and education. Although it is well known that ageing and socioeconomic status influence the probability of causing functional disorders, surprisingly little attention has been paid to the combined effect of those factors along the indivi...

Event history graphs for censored survival data.

A compact graphical device for combining survival and time-varying covariate information is proposed. The proposed graph contains the Kaplan-Meier estimator for right-censored data and a simultaneous display of the behaviour of time-dependent covariate(s) and the lifetime for each subject in the sample. The observed levels of time-dependent covariates are possibly subjected to an initial dimens...

Joint modeling of event time and nonignorable missing longitudinal data.

Survival studies usually collect on each participant, both duration until some terminal event and repeated measures of a time-dependent covariate. Such a covariate is referred to as an internal time-dependent covariate. Usually, some subjects drop out of the study before occurrence of the terminal event of interest. One may then wish to evaluate the relationship between time to dropout and the ...

Transition Probabilities for Continual