When specifying a continuous time Markov model like you have transitions are usually expressed as intensities, for example average time in the current state before the transition, or a hazard. If the data you have contains hazard ratios (HR) you need a baseline hazard function h(t) to compute hz(t)=HR*bhz(t). 

To make transition probabilities meaningful you have to look at the Markov chain at specified times. Going from hazards to transition probabilities is possible by using the relations between survival function S(t), hazard, and cumulative hazard.

Without the baseline hazard bhz(t) you cannot go from transition intensities to transition probabilities since the HR is just a ratio of two hazards, it does not have any dimension or unit.

I hope this helps you.

Fredrik

By using the relations between survival function and  hazard rate.

Thank you for your answer! This is my first modelling experience, so i'm sorry for bothering you once again.

If I have those Information given of a HTA Report on p. 63-65: https://nyemetoder.no/Documents/Rapporter/Rapport_2015_Malignt_melanom.pdf

Page: 63-65, is it possible for me to derive the transition probabilities?

To derive the transition probabilities they used the formula suggested by Briggs, 2007. They estimated trans. probabilities based on fitting distrubutions.

Can you give me some advice?

Thanks in advance.

I think this paper may be useful for you:

https://www.ncbi.nlm.nih.gov/pubmed/24338265

I have got the extracted data from Kaplane-Meier curve. The extracted data is as following. But I do not know how to conduct parametric suvival analyize using this kind of data. I do need help.

duration survive

0.00 1.00

0.52 0.99

0.70 0.97

0.87 0.95

1.08 0.92

1.32 0.90

1.64 0.89

1.84 0.86

2.00 0.85

2.12 0.83

2.25 0.81

2.54 0.80

2.72 0.78

2.98 0.76

3.60 0.75

3.78 0.73 3.95 0.71 4.12 0.69 4.46 0.67 4.70 0.66 4.88 0.64 4.99 0.62 5.16 0.59 5.36 0.57 5.55 0.55 5.81 0.53 5.98 0.51 6.08 0.49 6.25 0.46 6.52 0.45 6.75 0.43 7.22 0.42duration survive 0 0.997923451 0.518713882 0.993652415 0.699376223 0.968050006 0.872033573 0.950784844 1.081406585 0.923662692 1.317997371 0.898629715 1.643519107 0.888884985 1.840304452 0.863848652 1.997161288 0.851809338 2.122923435 0.828894207 2.249130136 0.813963271 2.540471424 0.798765241 2.717129009 0.777290858 2.983051587 0.761893457 3.604613826 0.745295156 3.777228981 0.727272172 3.945541754 0.708031862 4.117245195 0.688845201 4.464353935 0.671325151 4.696356268 0.655147684 4.881578178 0.635381706 4.987326843 0.618080959 5.158537005 0.590034999 5.360574621 0.568065317 5.550171749 0.550824808 5.810129681 0.529706956 5.979675235 0.507256228 6.080188097 0.487184613 6.253433553 0.461468474 6.517471843 0.4464533 6.750397995 0.431656934 7.217334709 0.421540224 7.465459293 0.405915302 7.726106745 0.387040762 8.200616454 0.376038843 8.406392847 0.360374839 8.498666744 0.344438296 8.860127053 0.333696057 9.124252577 0.324050283