TRANSLATING EVIDENCE FROM CONTINUOUS OUTCOMES INTO TRANSITION PROBABILITIES IN MARKOV MODELS: A SYSTEMATIC REVIEW OF CURRENT PRACTICE AND METHODOLOGICAL GAPS IN HEALTH TECHNOLOGY ASSESSMENT
Author(s)
Dandan Ai, PhD student1, Yuan Zhang, PhD2, Xinxin Deng, PhD student3, Liang Yao, PhD4.
1PhD student, Nanyang Technological University, Singapore, Singapore, 2Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada, 3Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 4Nanyang Technological University, Singapore, Singapore, Singapore.
1PhD student, Nanyang Technological University, Singapore, Singapore, 2Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada, 3Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 4Nanyang Technological University, Singapore, Singapore, Singapore.
OBJECTIVES: Markov models require transition probabilities (TPs) to represent disease progression and treatment effects, whereas evidence syntheses frequently report treatment effects for continuous outcomes as mean differences (MDs) or standardized mean differences (SMDs). How evidence derived from continuous outcomes is translated into TPs remains poorly described. This study aimed to identify and classify approaches used to incorporate continuous outcome evidence into Markov-based economic evaluations.
METHODS: A systematic review was conducted using the INAHTA database. HTA reports published between January 2018 and September 2025 were searched. Eligible studies included both a systematic review and an original economic evaluation. Reports using microsimulation, discrete-event simulation, or partitioned survival models, and studies without accessible full texts, were excluded. Markov models that did not use continuous outcomes to inform health-state transitions were also excluded. Included studies were reviewed to identify methods for translating continuous outcome evidence into TPs.
RESULTS: Of 3,323 records identified, 173 HTA reports met the eligibility criteria. Two broad evidence translation strategies were observed. Most evaluations transformed continuous outcomes into categorical response measures before modeling, whereas only a minority retained continuous treatment effects and translated MDs or SMDs into TPs through statistical or disease progression models. Five methodological approaches were identified: (1) response-based categorization using predefined thresholds; (2) risk-based conversion using prediction equations or relative-risk functions (e.g., LDL-C to cardiovascular risk); (3) effect-size transformation of SMDs into odds ratios; (4) distribution-based probability estimation using summary statistics and minimally important differences; and (5) disease-state mapping, in which changes in measures such as visual acuity informed health-state transitions. Some evaluations relied on expert opinion or structural assumptions despite available quantitative evidence.
CONCLUSIONS: Current HTA practice predominantly relies on categorizing continuous outcomes before economic modeling. Greater transparency, validation, and methodological guidance are needed to improve the robustness and consistency of HTA economic evaluations.
METHODS: A systematic review was conducted using the INAHTA database. HTA reports published between January 2018 and September 2025 were searched. Eligible studies included both a systematic review and an original economic evaluation. Reports using microsimulation, discrete-event simulation, or partitioned survival models, and studies without accessible full texts, were excluded. Markov models that did not use continuous outcomes to inform health-state transitions were also excluded. Included studies were reviewed to identify methods for translating continuous outcome evidence into TPs.
RESULTS: Of 3,323 records identified, 173 HTA reports met the eligibility criteria. Two broad evidence translation strategies were observed. Most evaluations transformed continuous outcomes into categorical response measures before modeling, whereas only a minority retained continuous treatment effects and translated MDs or SMDs into TPs through statistical or disease progression models. Five methodological approaches were identified: (1) response-based categorization using predefined thresholds; (2) risk-based conversion using prediction equations or relative-risk functions (e.g., LDL-C to cardiovascular risk); (3) effect-size transformation of SMDs into odds ratios; (4) distribution-based probability estimation using summary statistics and minimally important differences; and (5) disease-state mapping, in which changes in measures such as visual acuity informed health-state transitions. Some evaluations relied on expert opinion or structural assumptions despite available quantitative evidence.
CONCLUSIONS: Current HTA practice predominantly relies on categorizing continuous outcomes before economic modeling. Greater transparency, validation, and methodological guidance are needed to improve the robustness and consistency of HTA economic evaluations.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
MSR278
Topic
Economic Evaluation, Methodological & Statistical Research, Study Approaches
Disease
No Additional Disease & Conditions/Specialized Treatment Areas