MID for analyzing the cost-effectiveness of a new type of hip prosthesis.

Reference:

Briggs A, Sculpher M, Dawson J, Fitzpatrick R, Murray D, Malchau H.

The use of probabilistic decision models in technology assessment: the case of total hip replacement.

Appl Health Econ Health Policy. 2004;3:79-89.

An MS Excel version of this model was included as an exercise in:

Briggs A, Claxton K, Sculpher M.

Decision Modelling for Health Economic Evaluation.

Oxford University Press, 2006

http://www.herc.ox.ac.uk/pubs/books/material_modelling, exercise 5.7, or

http://www.herc.ox.ac.uk/pubs/books/decision/Ex57sol.

MID built by Iñigo Bermejo and F. Javier Díez in July 2013.

Revised: 24 January 2014.

Open the properties dialog of each node to obtain additional information. See also the chapter on MIDs in OpenMarkov's tutorial.

]]>

The initial state is primary THR (total hip replacement) for all patients. At the beginning of cycle 1 all the patients transit to successful THR, except in case of death. Later, if the prosthesis fails, the patient will be in state revision THR for one cycle (in which the prosthesis is replaced) and then transit to successful revised, except in case of death.

]]>

Age at the first cycle. It is the same as the age at entry.

]]> 1.0 1.0

]]>

Death due to other causes than hip replacement.

]]>

Failure of the prosthesis.

]]>

Death due to THR (total hip replacement).

]]>

Age of the patient when entering the model, i.e., when the hip is replaced by a prosthesis.

The Delta probability potential associated to this node implies that the patient (in the reference case) is 60 years when entering the model.

]]> 1.0 0.01 0.01 0.01 0.0 0.0 0.0 0.0 1.0 0.0 1.0 Constant Age at entry See the comment to node State [0] to understand this potential. The Excel model assumes when the prosthesis is replaced (states primary THR and revision THR) that the patient cannot die because of other causes than THR. It also assumes that when the prosthesis fails, the patient cannot die in that cycle. If we discarded these hypotheses, we might place the nodes Death THR and Death OC in higher positions, thus obtaining a more compact tree. However, the purpose of this MID is not to improve the Excel model, but to reproduce its results. ]]> 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.96 0.04 0.0 0.0 0.0 0.96 4.0 96.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.99901 9.9E-4 0.99849 0.00151 0.9974 0.0026 0.99607 0.00393 0.9933 0.0067 0.9891 0.0109 0.9807 0.0193 0.9684 0.0316 0.9465 0.0535 0.9199 0.0801 0.8452 0.1548 0.8121 0.1879 0.3740968 -5.490935 -0.0367 0.7685 -1.344474 Gamma Constant Age at entry Sex Prosthesis type 0.00225151199001 -0.005691 0.043219083664 2.8E-8 -7.83E-4 2.715660544E-5 5.1E-6 -0.007247 3.3E-5 0.011895392356 2.59E-4 -6.42E-4 -1.11E-4 1.84E-4 0.14636860414225 In the Excel model the probability of death is the same for primary THR as for revision THR. Nevertheless, they are implemented as two different parameters. In our model we might have joined both branches of the tree into a sigle one, but that would have caused a very slight difference in sensitivity analysis. ]]> 0.98 0.02 0.98 2.0 98.0 1.0 0.0 0.98 0.02 0.98 2.0 98.0 1.0 0.0 1.0 0.0 60.0 0.0 0.0 5294.0 0.0 0.0 5294.0 1487.0 0.0 0.7499784835183751 0.3000043042 0.8500035544181418 0.0 87.14 29.05 69.7 162.63 119.57 21.1 394.0 579.0 COST_EFFECTIVENESS 60 BEGINNING