Med Decis Making - Development of a framework for cohort simulation in cost-effectiveness analyses using a multistep ordinary differential equation solver algorithm in R.

Tópicos

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{ framework(1458) process(801) describ(734) }
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Resumo

TRODUCTION: Dynamic processes in cost-effectiveness analysis (CEA) are typically described using cohort simulations, which can be implemented as Markov models, or alternatively using systems of ordinary differential equations (ODEs). In the field of CEA, simple and potentially inaccurate single-step algorithms are commonly used for solving ODEs, which can potentially induce bias, especially if an incorrect step size is used. The aims of this project were 1) to implement and demonstrate the use of a modern and well-established hybrid linear multistep ODE solver algorithm (LSODA) in the context of CEA using the statistical scripting language R and 2) to quantify bias in outcome for a case example CEA as generated by a commonly used single-step ODE solver algorithm.METHODS: A previously published CEA comparing the adjuvant breast cancer therapies anastrozole and tamoxifen was used as a case example to implement the computational framework. A commonly used single-step algorithm was compared with the proposed multistep algorithm to quantify bias in the single-step method.RESULTS: A framework implementing the multistep ODE solver LSODA was successfully developed. When a single-step ODE solver with step size of 1 year was used, incremental life-years gained was underestimated by 0.016 years (5.6% relative error, RE) and ?158 (6.8% RE) compared with the multistep method.CONCLUSION: The framework was found suitable for the conduct of CEAs. We demonstrated how the use of single-step algorithms with insufficiently small step sizes causes unnecessary bias in outcomes measures of CEAs. Scripting languages such as R can further improve transparency, reproducibility, and overall integrity in the field of health economics.

Resumo Limpo

troduct dynam process costeffect analysi cea typic describ use cohort simul can implement markov model altern use system ordinari differenti equat ode field cea simpl potenti inaccur singlestep algorithm common use solv ode can potenti induc bias especi incorrect step size use aim project implement demonstr use modern wellestablish hybrid linear multistep ode solver algorithm lsoda context cea use statist script languag r quantifi bias outcom case exampl cea generat common use singlestep ode solver algorithmmethod previous publish cea compar adjuv breast cancer therapi anastrozol tamoxifen use case exampl implement comput framework common use singlestep algorithm compar propos multistep algorithm quantifi bias singlestep methodresult framework implement multistep ode solver lsoda success develop singlestep ode solver step size year use increment lifeyear gain underestim year relat error re re compar multistep methodconclus framework found suitabl conduct cea demonstr use singlestep algorithm insuffici small step size caus unnecessari bias outcom measur cea script languag r can improv transpar reproduc overal integr field health econom

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