It provides an admission-specific estimate for the baseline rate function, which is necessary as shown in Figure 2. It also allows one to determine if the relative rate of admissions changes based on the number of prior hospitalizations.
In model 4, Y i is the at risk indicator of event for subject i, λ0 t) is the baseline rate function for the event, x i is a covariate value, which may be time-dependent but may not contain elements of the event history, for subject i, β is the regression coefficient, and t represents the time from start of observation.
In model 7, Y ij, is the at risk indicator of the jth event for subject i, λ0 j (t) is the baseline rate function for the jth event, x i is a covariate value, which may be time-dependent, for subject i, β j is the regression coefficient for event j, and t represents the time from start of observation.
At any given time t, the multistate model allows the patients who are at risk for their 10th admission, for example, to have a different baseline rate function than patients who are at risk for their 1st admission.
If the point estimate of the relative rate for each transition in the multistate model were similar, and if the estimates of the baseline rate functions for each transition were also similar, then this may warrant use of simpler models.
From model 4 we observe that both the baseline rate functions, λ0, and regression parameters, β, are assumed to be common across events.
The plots of the estimated cumulative baseline rate functions for death among survivors and controls are illustrated in Figure 3.
However, baseline adherence was associated with better functioning at baseline, and baseline functioning was highly related to better functioning at 1 year.
In a community-based study, among treated hypertensive patients, even minimally impaired baseline renal function was associated with an increased rate of aortic stiffening.
