Estimation of Transition Probabilities for State-Transition Models: A Review of NICE Appraisals

2021 ◽  
Author(s):  
Tushar Srivastava ◽  
Nicholas R. Latimer ◽  
Paul Tappenden
Keyword(s):  
State Transition ◽  
Transition Probabilities ◽  
Transition Models

Alternative Conversion Methods for Transition Probabilities in State-Transition Models: Validity and Impact on Comparative Effectiveness and Cost-Effectiveness

2019 ◽  
Vol 39 (5) ◽  
pp. 509-522 ◽  
Author(s):  
Beate Jahn ◽  
Christina Kurzthaler ◽  
Jagpreet Chhatwal ◽  
Elamin H. Elbasha ◽  
Annette Conrads-Frank ◽  
...  
Keyword(s):  
State Transition ◽  
Transition Probabilities ◽  
Treatment Strategies ◽  
Transition Matrices ◽  
Long Term Outcomes ◽  
Health States ◽  
Life Years ◽  
Transition Models ◽  
Transformation Methods

Background. In state-transition models (STMs), decision problems are conceptualized using health states and transitions among those health states after predefined time cycles. The naive, commonly applied method (C) for cycle length conversion transforms all transition probabilities separately. In STMs with more than 2 health states, this method is not accurate. Therefore, we aim to describe and compare the performance of method C with that of alternative matrix transformation methods. Design. We compare 2 alternative matrix transformation methods (Eigenvalue method [E], Schure-Padé method [SP]) to method C applied in an STM of 3 different treatment strategies for women with breast cancer. We convert the given annual transition matrix into a monthly-cycle matrix and evaluate induced transformation errors for the transition matrices and the long-term outcomes: life years, quality-adjusted life-years, costs and incremental cost-effectiveness ratios, and the performance related to the decisions. In addition, we applied these transformation methods to randomly generated annual transition matrices with 4, 7, 10, and 20 health states. Results. In theory, there is no generally applicable correct transformation method. Based on our simulations, SP resulted in the smallest transformation-induced discrepancies for generated annual transition matrices for 2 treatment strategies. E showed slightly smaller discrepancies than SP in the strategy, where one of the direct transitions between health states was excluded. For long-term outcomes, the largest discrepancy occurred for estimated costs applying method C. For higher dimensional models, E performs best. Conclusions. In our modeling examples, matrix transformations (E, SP) perform better than transforming all transition probabilities separately (C). Transition probabilities based on alternative conversion methods should therefore be applied in sensitivity analyses.

scholarly journals PAA22 STATE TRANSITION MODELS FOR ESTIMATING TRANSITION PROBABILITIES IN MARKOV MODELS

2007 ◽  
Vol 10 (6) ◽  
pp. A404
Author(s):  
JD Campbell ◽  
DK Blough ◽  
SD Sullivan
Keyword(s):  
State Transition ◽  
Markov Models ◽  
Transition Probabilities ◽  
Transition Models

scholarly journals PMU78 Estimation of Transition Probabilities for State-Transition Models: A Review of NICE Appraisals

2020 ◽  
Vol 23 ◽  
pp. S615-S616
Author(s):  
T. Srivastava ◽  
N.R. Latimer ◽  
P. Tappenden
Keyword(s):  
State Transition ◽  
Transition Probabilities ◽  
Transition Models

scholarly journals rPrism – A Software for Reactive Weighted State Transition Models

2019 ◽  
pp. 165-174
Author(s):  
Daniel Figueiredo ◽  
Eugénio Rocha ◽  
Manuel António Martins ◽  
Madalena Chaves
Keyword(s):  
State Transition ◽  
Transition Models

scholarly journals Partitioned Survival and State Transition Models for Healthcare Decision Making in Oncology: Where Are We Now?

2020 ◽  
Vol 23 (12) ◽  
pp. 1613-1621
Author(s):  
Beth S. Woods ◽  
Eleftherios Sideris ◽  
Stephen Palmer ◽  
Nick Latimer ◽  
Marta Soares
Keyword(s):  
Decision Making ◽  
State Transition ◽  
Healthcare Decision ◽  
Healthcare Decision Making ◽  
Transition Models
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