A random time interval approach for analysing the impact of a possible intermediate event on a terminal event.
We consider the impact of a possible intermediate event on a terminal event in an illness-death model with states 'initial', 'intermediate' and 'terminal'. One aim is to unambiguously describe the occurrence of the intermediate event in terms of the observable data, the problem being that the intermediate event may not occur. We propose to consider a random time interval, whose length is the time spent in the intermediate state. We derive an estimator of the joint distribution of the left and right limit of the random time interval from the Aalen-Johansen estimator of the matrix of transition probabilities and study its asymptotic properties. We apply our approach to hospital infection data. Estimating the distribution of the random time interval will usually be only a first step of an analysis. We illustrate this by analysing change in length of hospital stay following an infection and derive the large sample properties of the respective estimator.
Beyersmann
Jan
J
Freiburg Centre for Data Analysis and Modelling, Eckerstrasse 1, D-79104 Freiburg, Germany. jan.beyersmann@fdm.uni-freiburg.de
D003428:Cross Infection / Q000453:epidemiology*; D003627:Data Interpretation, Statistical; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D015999:Multivariate Analysis; D018410:Pneumonia, Bacterial / Q000453:epidemiology*; D015995:Prevalence; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors