TY - JOUR
T1 - Using the Payoff Time in Decision-Analytic Models
T2 - A Case Study for Using Statins in Primary Prevention
AU - Thompson, Alexander
AU - Guthrie, Bruce
AU - Payne, Katherine
N1 - Financial support for this study was provided National Institute for Health Research Health Services and Delivery Research Programme (Project No. 11/2003/27).
PY - 2017/10
Y1 - 2017/10
N2 - Background: The payoff time represents an estimate of when the benefits of an intervention outweigh the costs. It is particularly useful for benefit-harm decision making for interventions that have deferred benefits but upfront harms. The aim of this study was to expand the application of the payoff time and provide an example of its use within a decision-analytic model.Methods: Three clinically relevant patient vignettes based on varying levels of estimated 10-year cardiovascular risk (10%, 15%, 20%) were developed. An existing state-transition Markov model taking a health service perspective and a life-time horizon was adapted to include 3 levels of direct treatment disutility (DTD) associated with ongoing statin use: 0.005, 0.01, and 0.015. For each vignette and DTD we calculated a range of outputs including the payoff time inclusive and exclusive of healthcare costs.Results: For a 10% 10-year cardiovascular risk (vignette 1) with low-levels of DTD (0.005), the payoff time was 8.5 years when costs were excluded and 16 years when costs were included. As the baseline risk of cardiovascular increased, the payoff time shortened. For a 15% cardiovascular risk (vignette 2) and for a low-level of DTD, the payoff time was 5.5 years and 9.5 years, respectively. For a 20% cardiovascular risk (vignette 3), the payoff time was 4.2 and 7.2 years, respectively. For higher levels of DTDs for each vignette, the payoff time lengthened, and in some instances the intervention never paid off, leading to an expected net harm for patients.Conclusions: This study has shown how the payoff time can be readily applied to an existing decision-analytic model and be used to complement existing measures to guide healthcare decision making.
AB - Background: The payoff time represents an estimate of when the benefits of an intervention outweigh the costs. It is particularly useful for benefit-harm decision making for interventions that have deferred benefits but upfront harms. The aim of this study was to expand the application of the payoff time and provide an example of its use within a decision-analytic model.Methods: Three clinically relevant patient vignettes based on varying levels of estimated 10-year cardiovascular risk (10%, 15%, 20%) were developed. An existing state-transition Markov model taking a health service perspective and a life-time horizon was adapted to include 3 levels of direct treatment disutility (DTD) associated with ongoing statin use: 0.005, 0.01, and 0.015. For each vignette and DTD we calculated a range of outputs including the payoff time inclusive and exclusive of healthcare costs.Results: For a 10% 10-year cardiovascular risk (vignette 1) with low-levels of DTD (0.005), the payoff time was 8.5 years when costs were excluded and 16 years when costs were included. As the baseline risk of cardiovascular increased, the payoff time shortened. For a 15% cardiovascular risk (vignette 2) and for a low-level of DTD, the payoff time was 5.5 years and 9.5 years, respectively. For a 20% cardiovascular risk (vignette 3), the payoff time was 4.2 and 7.2 years, respectively. For higher levels of DTDs for each vignette, the payoff time lengthened, and in some instances the intervention never paid off, leading to an expected net harm for patients.Conclusions: This study has shown how the payoff time can be readily applied to an existing decision-analytic model and be used to complement existing measures to guide healthcare decision making.
KW - Journal article
KW - Payoff time
KW - Direct treatment disutility
KW - Cost-effectiveness
KW - Cardiovascular
KW - Decision-analytic modelling
U2 - 10.1177/0272989X17700846
DO - 10.1177/0272989X17700846
M3 - Article
C2 - 28441087
SN - 0272-989X
VL - 37
SP - 735
EP - 746
JO - Medical Decision Making
JF - Medical Decision Making
IS - 7
ER -