Truncated survival data and Bayesian optimal design for changepoint problems

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My first area of research is in the area of survival analysis. Suppose that observations are taken over time, on items (they could be patients, for example) with a well-defined initiation time and a well defined end point. For example, the initiation time could be the onset date of dementia and the endpoint could be death. Interest lies in making statistical inference about the so-called "survival time"-between initiation and the endpoint; for example, the length of time that patients live with dementia. The feature of survival analysis is that some of the survival times for the items are incompletely observed. Most often this happens because either the event of interest has not yet occurred by the time the study ends or the subjects has been lost to follow-up before the event has occurred. If this happens the survival times are said to be right censored. Sometimes however, as a result of the study design, subjects who have short survival times are simply not observed as part of the study. The survival times are then said to be left truncated. I specialize in analyzing survival data that are right censored and left truncated.