By reading this and others recent papers on this issue it is not clear to me which is, the alternative best option to that explained in the above paper by using E-Surge.
My feeling is that, at the moment, E-Surge is a software you can't learn to use by yourself (at least I can't! up to now there is not something analogous to the Gentle Introduction to MARK for this software).
Even though I hope I will attend a Workshop on this software because it must be really worthing it, I would like to understand what is the best (and easily accessible) option in case you are using MARK.
In Conn and Cooch 2009, they compared the results of M-Surge on the data-censored (without "unknown" state) dataset and those of E-Surge on the same dataset not censored (by using Hidden Markov Models with "unknown", "apparently infected" and "apparently not infected" states). They found that the latter approach get much more precise estimates particularly when the unknown states make up a large percentage of the observations. They also stated that
.Conn and Cooch, 2009 wrote: Censoring data when state cannot be ascertained is also a viable solution leading to unbiased estimators of parameters of interest. The issue the is whether to sacrifice some precision in favour of using a simpler model with fewer assumptions (e.g. by censoring data), or whether to employ a more complicated model to be able to utilize all available data (but perhaps at the expense of introducing bias if assumptions are not met)
Faustino et al. 2004 used an approach that is not equivalent to the approach implemented in M-Surge in Conn and Cooch 2009 (classic multi-state on censored data).
Faustino et al. 2004 used a double, sequential approach to deal with logistical limitations of their data (very similar situation I am dealing with).
They first made model selection on just phi and p on the complete dataset that included the unknown states (at this stage transition rates were constrained to vary only with disease state), and after they went on by shaping the transition rates on the data-censored dataset by holding fixed the structure of the model pertinent to phi and p as it was selected in the first stage.
As outlined also in the Conn and Cooch paper, the first approach of Faustino et al. 2004 should give typically unbiased estimates of survival and encounter rates and the second approach, as said above, should be unbiased on the transition rates estimates.
It being understood there are other many other important issues about the modeling of disease dynamics by MCR (see Cooch et al 2010 for a review), I have two questions about the best options when dealing with partially observed states in a context like the Faustino et al. paper.
Q1: Is the Faustino et al. 2004 approach the best way to deal with this situation by using the (normally accessible) features in MARK?
Q2: In case the first question is answered "yes", would you report the survival and encounter rates as estimated by the first approach?
Thanks for any help you might provide,
Simone