Spatial Survival and Longitudinal Analysis
In many biostatistical applications there is a need to consider temporal variation. The commonest examples are often found in clinical or behavioral intervention trials where a state can be reached by a patient. The time at which the state is reached could be of primary interest. The end point could be a vital outcome (death) or disease remission, cure or cessation of a behavior. In all cases the time of the event is the important random variable. This is the typical scenario where survival analysis is employed. On the other hand, in some clinical or intervention studies, the variation of response over time is to be monitored. For example, cholesterol concentrations in blood might be monitored under diﬀerent treatments, and the eﬀect of these treatments over time is to be examined. In an intervention trial for diet change, food intake might be repeatedly measured via self-report questionnaire. In these cases, the time of measurement is usually ﬁxed and the measurement itself is the random variable.