The following is on behalf of Martin Bergstrand, as there is some difficulty in 
posting on to nmusers:

Dear Ya-Feng,

The first data example seems correct but probably requires another record at 
TIME= 0, to account for what happens before the first observation (e.g. TIME=0 
& EVID=2). If you have a dose record etc. at TIME=0 this is not needed. Also 
notice that with the current code you assume that TOXGR=0 at TIME= 0 [ 
IF(NEWIND.NE.2) PDV=0 => A_0(3)=1 ]. This is often a reasonable assumption but 
it is important to be aware of.

Now for some explanation: With a continuous time Markov Model the most recent 
preceding state is by default impacting the probability of the next state 
(higher order markov effects are possible but most often not needed). The 
"amount" in each compartment in the markov chain represents the probability for 
observing the corresponding state. Right after observing that the TOXGR 
(toxicity score) was = 1 you re-initialize each compartment so that at that 
point the probability is 1 for TOXGR=1 (CMT=4) and 0 for the other states i.e. 
TOXGR=0|2 (CMT=3|5). In the time that passes between the system being reset and 
the next observation some probability will distribute from CMT=4 to CMT=3 and 
CMT = 5 (and some will remain in CMT= 4). The rate of distribution of 
probability between the 3 compartments are given by the rate constants 
K34,K43,K45 and K54 (that are not present in your example code). Rather than 
estimating the rate constraints (that can be hard to interpret), Schindler et 
al showed how you can estimate mean equilibrium times and steady state 
probabilities (and from them derive the rate constants).

I hope this was helpful?

Kind regards,

Martin Bergstrand, Ph.D.
Principal Consultant
Pharmetheus AB

+46(0)709 994 396
[email protected]<mailto:[email protected]>
www.pharmetheus.com<http://www.pharmetheus.com/>

+46(0)18 513 328
U-A Science Park, Dag Hammarskjölds v. 36b
752 37 Uppsala, Sweden
<br /><br />
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