In addition, each subject provides a (possibly right censored) failure time and an event indicator, which indicates whether the observed failure time is a true failure time,, or a censoring time C i. Perhaps, the simplest method to include association between the longitudinal and time-to-event processes is a survival model with the longitudinal measurements specified as time-dependent covariates.
Specifically, time-dependent sensitivity and specificity functions are defined as follows: s e n s i t i v i t y [ c, t | f (X ) ] = P r { f (X ) > c | δ (t ) = 1 }, s p e c i f i c i t y [ c, t | f (X ) ] = P r { f (X ) > c | δ (t ) = 0 }, in which c is the cut-off point, t is the survival time, f(X) are the prognostic indices, f (X ) = X ′ β and δ(t) is the event indicator at time t [ 16].
Based on the relative error with respect to the measurement signal, an event indicator variable is introduced and the corresponding event-triggered scheme is proposed in order to determine whether the measurement output is transmitted to the controller or not.
The topmost three figures illustrate the smoothing distribution of the velocity variables n τ, bar position variables m τ, and the acoustic event indicator variables r τ.
The imputation model included the event indicator, all study variables, and the Nelson-Aalen estimate of cumulative hazard [28].
We performed BP modelisation as previously reported [1]: performance series for each event are fitted by the following function: yj (t) = ΔBP exp(−aj.t' +b; where ΔBP = BPi,j−BPf,j is an event indicator for the studied j period; BPi,j and BPf,j are the initial and final BP values respectively; aj is the positive curvature factor given by non linear regression; b is the asymptotic limit.
As part of the hazard model, a latent frailty variable (Fi), common to age specific intervals, was defined to be a function of age interval specific event indicators.
For this, we simulated the distribution of these statistics under the null hypothesis by permuting the event indicators among subjects at risk at that time.
In other words, the number of subjects with events is kept fixed at each event time, but their event indicators are randomly exchanged among those currently at risk [17].
However, this study provides only a first step in evaluating these adverse event indicators.
More evolved safety measurement systems combining both lagging (after the event) and leading (before an event) indicators.
