State-Transition Modeling: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-3
Open accessAbstract
State-transition modeling is an intuitive, flexible, and transparent approach of computer-based decision-analytic modeling including both Markov model cohort simulation and individual-based (first-order Monte Carlo) microsimulation. Conceptualizing a decision problem in terms of a set of (health) states and transitions among these states, state-transition modeling is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation. State-transition models have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs. Most frequently, state-transition models are used in the evaluation of risk factor interventions, screening, diagnostic procedures, treatment strategies, and disease management programs. The goal of this article was to provide consensus-based guidelines for the application of state-transition models in the context of health care. We structured the best practice recommendations in the following sections: choice of model type (cohort vs. individual-level model), model structure, model parameters, analysis, reporting, and communication. In each of these sections, we give a brief description, address the issues that are of particular relevance to the application of state-transition models, give specific examples from the literature, and provide best practice recommendations for state-transition modeling. These recommendations are directed both to modelers and to users of modeling results such as clinicians, clinical guideline developers, manufacturers, or policymakers.
AI snapshot
Written from the paper’s full text.
TL;DR
State-transition modeling (STM) is a flexible, transparent decision-analytic approach using health states and transitions, implemented as cohort/Markov models or individual-level first-order Monte Carlo microsimulations. This ISPOR-SMDM Task Force report provides 18 consensus best-practice recommendations for choosing, structuring, parameterizing, analyzing, validating, and reporting STMs in health care.
Key findings
- STMs are suited when a decision problem can be framed as mutually exclusive and collectively exhaustive states, transitions among them, and transition probabilities; common applications include primary prevention, screening, diagnosis, treatment, and disease management.
- Two common frameworks are distinguished: cohort (“Markov”) models, which are simpler, more transparent, and easier to debug/communicate when the number of states is manageable, but assume the memoryless Markovian property; and individual-level microsimulations, which avoid that property and track individual histories but are computationally intensive and harder to debug.
- The report presents 18 numbered best-practice recommendations (III-1 through III-18), covering model type, structure, data/parameters, analysis, reporting, and communication. Examples include choosing cohort simulation if states are manageable, using individual-level STM if state explosion would otherwise occur, applying half-cycle correction, and simulating enough individuals for stable estimates.
- Recommendations emphasize that validity should not be sacrificed for simplicity, states should reflect biological/theoretical understanding and capture intervention benefits/harms, and transition probabilities/intervention effects should come from the most representative data sources.
Methods
- Consensus-based Task Force report by ISPOR and SMDM, developed by expert model developers and users from academia, industry, and government across multiple countries; process included teleconferences, an in-person meeting in Boston in March 2011, a survey of members to agree/disagree with recommendations, public comment, and formal external review.
- Not an empirical study: no primary sample, dataset, or statistical analysis is reported; the article synthesizes examples from the literature and provides structured best-practice recommendations.
- Recommendations are organized into sections on choice of model type (cohort vs. individual-level), model structure, model parameters, analysis, reporting, and communication.
Limitations
- The paper states that not all models may be able to comply with all recommendations and that many more aspects than those described may have to be considered for good modeling practice.
- The paper states that rigorous studies evaluating alternative presentation methods are lacking, so its communication recommendations represent best judgments based on experience.
- Evident from the text: the report is consensus-based rather than empirically validated in this article; it focuses on two common frameworks that do not capture interactions, model a single closed cohort, and allow transitions only at specified time intervals, and it explicitly does not address multipurpose disease-specific models.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
5 of 32 cited sources checked · 4 supported · 0 flagged · accuracy not yet measured
- Premisecitation
STMs used to evaluate screening strategies consider two types: one-time screening or repeated interval screening.
“STMs used to evaluate screening strategies [16,17] consider two types: one-time screening, (e.g., of newborns [18] or genetic screening [19 -21]) or repeated (interval) screening (e.g., for cancer [22][23][24][25] or HIV [26,27]).”
Quote in the paperNot a substantial claim1 of 6 sources checked- cites [16] Eddy, 1980
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [17] Russel, 1994
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [18] Grill, 2005, Comparing the clinical effectiveness of different new-born hearing screening strategies: a decision analysis, BMC Public Health source
- cites [19] Gutierrez de Mesa, 2007, Modeling the impact of genetic screening technologies on healthcare: theoretical model for asthma in children, Mol Diag Ther source
- cites [20] Meckley, 2010, A policy model to evaluate the benefits, risks and costs of warfarin pharmacogenomic testing, Pharmacoeconomics source
- cites [21] Rogowski, 2009, The cost-effectiveness of screening for hereditary hemochromatosis in Germany: a remodeling study, Med Decis Making source
- Premisecitation
Variance reduction techniques (e.g., using common random numbers) can decrease the required numbers of individuals.
“Variance reduction techniques (e.g., using common random numbers) can decrease the required numbers [56].”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [56] Stout, 2008, Keeping the noise down: common random numbers for disease simulation modeling, Health Care Manage Sci source
- Premisecitation
STMs can be used to compare various types of interventions.
“STMs can be used to compare various types of interventions [14].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14] Siebert, 2003, When should decision-analytic modeling be used in the economic evaluation of health care?, Eur J Health Econom source
- Premisenone given
The primary disadvantage of cohort STMs is the underlying assumption that transition probabilities do not depend on history, neither on past states nor on the time spent in the current state.
“The primary disadvantage is the underlying assumption that transition probabilities do not depend on history-neither on past states nor on the time spent in the current state.”
Quote in the paperSubstantial claim - Premisecitation
STMs used to evaluate primary prevention strategies are concerned with reduction in the risk of developing a disease.
“STMs used to evaluate primary prevention strategies are concerned with reduction in the risk of developing a disease (e.g., [15]).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15] Col, 1997, Patient-specific decisions about hormone replacement therapy in postmenopausal women, JAMA source
- Premisereasoning
Individual-level STMs are not limited by the Markovian property because they simulate one individual at a time.
“Individual-level STMs (Table 1) are not limited by the Markovian property as they simulate one individual at a time.”
Quote in the paperSubstantial claim - Interpretationreasoning
The Markovian property can be very limiting for clinical applications where past states or time in state tend to be strong determinants of what happens next.
“This assumption (the "Markovian" property) can be very limiting for clinical applications where these aspects tend to be strong determinants of what happens next.”
Quote in the paperSubstantial claim - Premisecitation
Efficacy derived from randomized clinical trials may have to be adjusted for compliance to reflect real-world effectiveness.
“Efficacy derived from randomized clinical trials may have to be adjusted for compliance to reflect real-world effectiveness [53].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [53] Cox, 2009, Value Health source
- Premisecitation
An assumption of additive rates can give very different results than a multiplicative one.
“Because an assumption of additive rates can give very different results than a multiplicative one [52], the impact of this assumption should be assessed.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [52] Kuntz, 1995, Life expectancy biases in clinical decision modeling, Med Decis Making source
- Interpretationnone given
Symmetric models ensure that the disease process is represented consistently across strategies.
“Symmetric models ensure that the disease process is represented consistently across strategies.”
Quote in the paperSubstantial claim - Premisecitation
Adjustment for time-varying confounding (confounders that simultaneously act as intermediate steps in the pathway between intervention and outcome) requires special methods such as marginal structural analysis or g-estimation.
“Adjustment for time-varying confounding (i.e., confounders that simultaneously act as intermediate steps in the pathway between intervention and outcome) require special methods such as marginal structural analysis or g-estimation [38,54].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [38] Robins, 2004, Estimations of the effects of multiple interventions
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [54] Johnson, 2009, Value Health source
- Interpretationreasoning
If a state-transition model is not symmetric, errors result when conducting sensitivity analysis on the underlying probability of a particular anatomy (e.g., left main disease).
“Otherwise, errors result when conducting sensitivity analysis on the underlying probability of any particular anatomy (e.g., left main disease).”
Quote in the paperSubstantial claim - Interpretationreasoning
When extrapolating beyond a trial's duration, reductions in all-cause mortality should not be applied directly because background mortality from other causes increases with age.
“When extrapolating beyond a trial's duration, reductions in all-cause mortality should not be applied directly since background mortality (from other causes) increases with age.”
Quote in the paperSubstantial claim- Rests on an assumption: Background mortality from other causes increases with age.
- Interpretationcitation
To achieve stable results in an individual-based simulation, sufficient individuals must be modeled.
“To achieve stable results in an individual-based simulation, sufficient individuals must be modeled.”
Quote in the paperSubstantial claim - Premisecitation
Life-table mortality can be subtracted from total mortality to estimate the reduction in disease-specific mortality.
“Alternatively, life-table mortality could be subtracted from total mortality to estimate the reduction in disease-specific mortality [55].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [55] Kuntz, 2001, Modelling in economic evaluation source
- Interpretationnone given
Shorter cycle lengths provide better approximations of life expectancy.
“Shorter cycle lengths provide better approximations of life expectancy.”
Quote in the paperNot a substantial claim - Interpretationnone given
For preventive and therapeutic interventions, if evidence is available for reduction in disease incidence, events, or progression and also for mortality, using both may double count.
“For preventive and therapeutic interventions, if evidence is available for reduction in disease incidence, events, or progression and also for mortality, using both may double count.”
Quote in the paperNot a substantial claim
- May combine two claims. 22
- Classified as probably not a substantial claim. 28
May combine two claims.
PremisecitationIndividual-level STMs require more computation time, which may be important if probabilistic sensitivity analyses or value-of-information analyses are performed.
“Individual-level STMs [36,37], however, require more computation time, which may be important if probabilistic sensitivity analyses or value-of-information analyses are performed.”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [36] Lee, 2008, Breast cancer screening in BRCA1 mutation carriers: effectiveness of MR imaging–Markov Monte Carlo decision analysis, Radiology source
- cites [37] Clarke, 2004, A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) outcomes model, Diabetologia source
May combine two claims.
PremisecitationThe cohort and individual-based state-transition frameworks focused on in this report do not capture interactions, model a single closed cohort, and allow transitions to occur only at specified time intervals.
“These frameworks do not capture interactions, model a single (closed) cohort, and allow transitions to occur only at specified time intervals.”
Quote in the paperSubstantial claim1 of 5 sources checked- cites [7] Beck, 1983, The Markov process in medical prognosis, Med Decis Making source
- cites [8] Sonnenberg, 1993, Markov models in medical decision making: a practical guide, Med Decis Making source
- cites [9] Spielauer, 2007, Dynamic microsimulation of health care demand, health care finance and the economic impact of health behaviours: survey and review, Int J Microsimuation source
Section 3.4 (page unavailable) says discrete-time models typically use transition tables, which supports the point about transitions at specified intervals for that class of models. The paper does not establish that the cohort and individual-based frameworks in the claim lack interactions or model a single closed cohort; Section 2.4 instead describes a LifePaths simulation made up of multiple cases. (LLM, conf=0.76)
Experimental — accuracy not yet measured - cites [10] Groot Koerkamp, 2010, Uncertainty and patient heterogeneity in medical decision models, Med Decis Making source
- cites [11] Weinstein, 2006, Recent developments in decision-analytic modelling for economic evaluation, Pharmacoeconomics source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOther methods such as Markov decision processes generalize STMs by allowing embedding of sequential decisions, so that multiple decisions can be made in multiple time periods.
“Other methods such as Markov decision processes generalize STMs by allowing embedding of sequential decisions, and thus, multiple decisions can be made in multiple time periods [39,40].”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [39] Puterman, 1994
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [40] Alagoz, 2010, Markov decision processes: a tool for sequential decision making under uncertainty, Med Decis Making source
Classified as probably not a substantial claim. May combine two claims.
Premisenone givenCohort models are analyzed as single cohorts progressing through states simultaneously and cannot distinguish one individual from another except by state descriptions, whereas individual-level STMs keep track of each individual's history using tracker variables.
“Whereas cohort models are analyzed as single cohorts progressing through the states simultaneously (which does not allow distinguishing one individual from another except by state descriptions), individual-level STMs keep track of each individual's history ("tracker variables").”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationDiagnostic models are used to identify optimal diagnostic strategies among individuals who present with signs or symptoms of one or more suspected diseases.
“Diagnostic models are used to identify optimal diagnostic strategies among individuals who present with signs or symptoms of one or more suspected diseases [28].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [28] Trikalinos, 2009, Decision-analytic modeling to evaluate benefits and harms of medical tests: uses and limitations, Med Decis Making source
May combine two claims.
PremisecitationIn cohort STMs, continuous variables such as blood pressure have to be categorized, and some guidance exists for determining how many states to create.
“In cohort STMs, continuous variables (e.g., blood pressure) have to be categorized; some guidance exists for determining how many states to create [35].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] Bentley, 2009, Effects of categorizing continuous variables in decision-analytic models, Med Decis Making source
May combine two claims.
InterpretationreasoningA Markov model can handle memory by creating states that include history, but this can greatly increase the number of states, resulting in very large models that are difficult to manage (state explosion).
“A Markov model can handle memory by creating states that include history, but this can greatly increase the number of states, resulting in very large models that are difficult to manage (i.e., "state explosion").”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisereasoningThe starting cohort in primary prevention STMs is individuals free of disease or complications being modeled.
“The starting cohort is individuals free of disease or complications being modeled.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAn STM can capture many features present in the course of a disease or clinical process, such as disease risk over time, changing states, and episodic events, although it is not the only approach that can capture these features.
“An STM can capture many features present in the course of a disease or clinical process (e.g., disease risk over time, changing states, and episodic events), although this is not the only approach that can capture these features [13].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [13] Caro, 2005, Pharmacoeconomic analysis using discrete event simulation, Pharmacoeconomics source
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenThe principal advantage of cohort STMs is that they are relatively simple to develop, debug, communicate, and analyze using user-friendly software if the number of states is not too large.
“The principal advantage of cohort STMs is that they are relatively simple to develop, debug, communicate, and analyze using user-friendly software if the number of states is not too large.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningAn advantage of using an individual-level STM is the ability to model individual characteristics as continuous variables and to evaluate dynamic intervention strategies in which future decisions depend on current and past patient characteristics.
“An advantage of using an individual-level STM is the ability to model individual characteristics as continuous variables and to evaluate dynamic intervention strategies-ones in which future decisions depend on current and past patient characteristics.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationnone givenThe main disadvantages of individual-level STMs are that they are computationally intensive, often requiring simulation of millions of individuals to obtain stable values for outcomes of interest, and they are more difficult to debug.
“The main disadvantages are that they are computationally intensive, often requiring simulation of millions of individuals to obtain stable values for the outcomes of interest, and they are more difficult to debug.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisenone givenAn STM following one branch can have a different structure than one following another branch.
“An STM following one branch can have a different structure than one following another branch.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Premisenone givenMany characteristics that affect transition probabilities, such as age, sex, comorbidities, and disease stage, are known at the time of the decision and can be used to define the starting cohorts.
“Many characteristics that affect transition probabilities (e.g., age, sex, comorbidities, and disease stage) are known at the time of the decision and can be used to define the starting cohorts.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenAlthough it may be possible to describe natural history solely on the basis of health care utilization, this does not provide direct insight into health outcomes at the biological level and its value is limited for most decision problems.
“Although it may be possible to describe natural history solely on the basis of health care utilization, this does not provide direct insight into health outcomes at the biological level and its value is limited for most decision problems.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Premisenone givenIn models evaluating diagnostic strategies, it is typical to represent the testing pathways and their outcomes, such as true positive and false positive, in a pre-STM decision tree.
“In models evaluating diagnostic strategies, it is typical to represent the testing pathways and their outcomes (e.g., true positive, false positive) in a pre-STM decision tree.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationGiving a full reward at the start (i.e., transitions occur at cycle end) overestimates expected values; assigning no reward (i.e., transitions occur at cycle start) underestimates them.
“Giving a full reward at the start (i.e., transitions occur at cycle end) overestimates expected values; assigning no reward (i.e., transitions occur at cycle start) underestimates them [8].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] Sonnenberg, 1993, Markov models in medical decision making: a practical guide, Med Decis Making source
Probably not a substantial claim: may not pass S4 (based).
Premisenone givenExpected outcomes depend on values assigned to each state (e.g., quality-adjusted life-years can be derived if utilities are assigned).
“Expected outcomes depend on values assigned to each state (e.g., quality-adjusted life-years can be derived if utilities are assigned).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenDistributions are derived easily from individual-level STMs, but they can also be derived from cohort models, either by analyzing the Markov trace or by running the model individually (but without tracker variables).
“Distributions are derived easily from individual-level STMs, but they can also be derived from cohort models, either by analyzing the Markov trace or by running the model individually (but without tracker variables).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenA useful method of identifying programming errors in an STM is to check whether modelbuilding rules such as the use of symmetric branches or states in STMs are followed.
“A useful method of identifying programming errors in an STM is to check whether modelbuilding rules such as the use of symmetric branches or states in STMs are followed.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisenone givenMarkov cycle trees can display the transitions between states, and probabilities and transitions that are conditional upon other events or parameters.
“Markov cycle trees, often stylized, can display the transitions between states, and probabilities and transitions that are conditional upon other events or parameters.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenAlthough shorter cycles always yield more precise estimates, the error becomes very small when the number of cycles required increases.
“Although shorter cycles will always yield more precise estimates, the error gets very small when the number of cycles required increases.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Premisenone givenCommon approaches to the time horizon include modeling to an age of 120 years or tracking the cohort until more than 99.9% of the individuals are dead.
“Common approaches include modeling to an age of 120 years or tracking the cohort until more than 99.9% of the individuals are dead.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
InterpretationcitationBecause capturing screening history in Markov states can lead to state explosion, it may be necessary to use an individual-level STM with screening history included as tracker variables.
“As capturing screening history in the Markov states can lead to state explosion, it may be necessary to use an individual-level STM, with screening history included as tracker variables [22].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [22] Goldie, 2006, Cost-effectiveness of cervical cancer screening, Vaccine source
May combine two claims.
PremisereasoningAs STMs allow deriving the time at which particular transitions occur, the results can be represented as (modeled) probability or survival curves and directly compared with survival curves from empirical studies.
“As STMs allow deriving the time at which particular transitions occur, the results can be represented as (modeled) probability or survival curves and directly compared with survival curves from empirical studies.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenInspection of the Markov trace can help find errors, by setting parameters in such a way that how the trace will look can be predicted (e.g., so that no modeled individual will visit a given state during the simulation).
“Inspection of the Markov trace can also help find errors, by setting parameters in such a way that how the trace will look can be predicted (e.g., so that no modeled individual will visit a given state during the simulation).”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationVariables that affect transition probabilities but are not known at decision time, such as genetic mutation and undiagnosed infection, can create heterogeneity bias, and inclusion of such variables should be considered.
“Variables that affect transition probabilities but are not known at decision time (e.g., genetic mutation and undiagnosed infection) can create "heterogeneity bias" [45,46], and inclusion of such variables should be considered.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [45] Kuntz, 2002, Assessing the sensitivity of decision-analytic results to unobserved markers of risk: defining the effects of heterogeneity bias, Med Decis Making source
- cites [46] Zaric, 2003, The impact of ignoring population heterogeneity when Markov models are used in cost-effectiveness analysis, Med Decis Making source
Probably not a substantial claim: may not pass S4 (based).
Premisenone givenThe model outputs for a single cohort allow for the comparison of alternative strategies for that cohort.
“The model outputs for a single cohort allow for the comparison of alternative strategies for that cohort.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningComplicated models consisting of multiple states cannot feasibly be represented by displaying all states and transitions.
“While a relatively simple model with few states may be fully represented in a single diagram, complicated models consisting of multiple states cannot feasibly be represented by displaying all states and transitions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationWhen results from observational studies are used in a model, causal graphs can be used to explicitly state causal assumptions.
“When results from observational studies are used in the model, causal graphs can be used to explicitly state causal assumptions [53].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [53] Cox, 2009, Value Health source
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
Premisenone givenEach state in a cohort STM is homogeneous: every individual in that state has the same transition probabilities, implying that any characteristics that determine those probabilities must not differ within the state.
“Each state is homogeneous-every individual in that state has the same transition probabilities-implying that any characteristics that determine those probabilities must not differ within the state.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningSuch complicated models are invariably cluttered, and the resultant tangle of arrows and states often impairs communication, rather than enhancing it.
“Such models are invariably cluttered, and the resultant tangle of arrows and states often impairs communication, rather than enhancing it.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Premisenone givenDynamic interval screening strategies may depend on screening history, for example, some algorithms for cervical cancer screening recommend extension of the interval after repeated negative tests.
“Dynamic interval screening strategies ("individualized screening") may depend on screening history (e.g., some algorithms for cervical cancer screening recommend extension of the interval after repeated negative tests).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenIn a cohort STM, all individuals in a given state are indistinguishable in terms of their transition probabilities.
“In a cohort STM, all individuals in a given state are indistinguishable in terms of their transition probabilities.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
Premisenone givenLong-term treatment effectiveness and costs often depend on time-varying heterogeneous patient characteristics, and many treatments are personalized and follow dynamic rules such as dose and second-line treatments, and compliance and treatment success depend on current treatment response and side effects.
“Long-term treatment effectiveness and costs often depend on time-varying heterogeneous patient characteristics, and many treatments are "personalized" and follow dynamic rules (e.g., dose and second-line treatments, and compliance and treatment success depend on current treatment response and side effects).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningIt is not appropriate to take an empirical estimate of the probability of a positive screen from a study because this does not explicitly incorporate the underlying disease probability.
“It is not appropriate to take an empirical estimate of the probability of a positive screen from a study because this does not explicitly incorporate the underlying disease probability.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationStability of model results is assessed by calculating variance from multiple runs with identical number of individuals, which should be much smaller than the smallest difference expected between strategies.
“Stability of model results is assessed by calculating variance from multiple runs with identical number of individuals [55], which should be much smaller than the smallest difference expected between strategies.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [55] Kuntz, 2001, Modelling in economic evaluation source
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenIf disease-specific mortality is not available, a relative reduction can be applied to disease-specific mortality, providing a conservative estimate of treatment benefit.
“If disease-specific mortality is not available, a relative reduction can be applied to disease-specific mortality, providing a conservative estimate of treatment benefit.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisereasoningIn an individual-level STM, characteristics and other parameters can be heterogeneous within a state but must be tracked throughout, and transition probabilities must be defined as a function of these characteristics.
“In an individual-level STM, these characteristics and other parameters can be heterogeneous within a state but must be tracked throughout and transition probabilities must be defined as a function of these characteristics.”
Quote in the paperSubstantial claim
The paper relies on this without support.
State-transition modeling is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation.
“State-transition modeling is one of the most widespread modeling techniques in clinical decision analysis, health technology assessment, and health-economic evaluation.”
The paper relies on this without support.
State-transition models have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs.
“State-transition models have been used in many different populations and diseases, and their applications range from personalized health care strategies to public health programs.”
The paper relies on this without support.
Many clinical situations can be described in terms of the conditions that individuals can be in (states), how they can move among such states (transitions), and how likely such moves are (transition probabilities).
“Many clinical situations can be described in terms of the conditions that individuals can be in ("states"), how they can move among such states ("transitions"), and how likely such moves are ("transition probabilities").”
The paper relies on this without support.
Other modeling techniques are also suitable for situations requiring time-dependent parameters, time to an event, or repeated events, for example, discrete event simulation.
“Other modeling techniques are also suitable for these situations (e.g., discrete event simulation).”
The paper relies on this without support.
Background mortality from other causes increases with age.
“When extrapolating beyond a trial's duration, reductions in all-cause mortality should not be applied directly since background mortality (from other causes) increases with age.”
The paper relies on this without support.
Graphical representation of an STM helps communicate key structure and most important assumptions made regarding states and allowable transitions.
“Graphical representation of an STM helps communicate key structure and most important assumptions made regarding states and allowable transitions.”
The paper relies on this without support.
Presenting intermediate results can be helpful for demonstrating face validity for clinical experts, epidemiologists, and decision makers.
“Presenting intermediate results can be helpful for demonstrating face validity for clinical experts, epidemiologists, and decision makers.”
The paper relies on this without support.
Effectiveness estimates derived from observational studies are subject to confounding.
“Effectiveness derived from observational studies must be adjusted for confounding (e.g., using multivariate regression techniques or propensity scoring).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A lifetime time horizon was chosen because it is aligned with best practices and is needed to capture all health effects and costs for chronic conditions like T2D.
“We chose the lifetime as the time horizon for the costeffectiveness analysis.This choice was aligned with the best practices as a long-time horizon is needed to capture all the health effects and costs pertained to chronic conditions like T2D [34].”
From Economic evaluation of wearable fitness trackers versus metformin for preventing type 2 diabetes in the UK population (2025) · cites this paper as [34]
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GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology
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Markov Models in Medical Decision Making: A Practical Guide
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Caro, J., Briggs, A., Siebert, U., & Kuntz, K. M. (2012) · Value in Health · doi:10.1016/j.jval.2012.06.012 · Cited by 1,006 · see citing works
Self-citationThe Markov process in medical prognosis
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Model Transparency and Validation
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Economic Evaluation in Health Care: Merging Theory with Practice
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“Other articles in this series [1] [2] [3] [4] [5] [6] describe best practices for conceptualizing models, building and applying other types of models, and addressing uncertainty, transparency, and validations.”
BackgroundClarke, P. M., Gray, A. M., Briggs, A., Farmer, A. J., Fenn, P., Stevens, R. J., Matthews, D. R., Stratton, I. M., Holman, R. R., & on behalf of the UK Prospective Diabetes Study (UKPDS) Group (2004) · Diabetologia · doi:10.1007/s00125-004-1527-z · Cited by 615 · see citing works
Cost-Effectiveness of Screening for HIV in the Era of Highly Active Antiretroviral Therapy
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Self-citationCost-Effectiveness of Implantable Cardioverter–Defibrillators
Sanders, G. D., Hlatky, M. A., & Owens, D. K. (2005) · New England Journal of Medicine · doi:10.1056/nejmsa051989 · Cited by 543 · see citing works
Self-citationCaro, J. J., Briggs, A. H., Siebert, U., & Kuntz, K. M. (2012) · Medical Decision Making · doi:10.1177/0272989x12454577 · Cited by 520 · see citing works
Self-citationOwens, D. K., Lohr, K. N., Atkins, D., Treadwell, J. R., Reston, J. T., Bass, E. B., Chang, S., & Helfand, M. (2010) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.03.009 · Cited by 513 · see citing works
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Decision Making in Health and Medicine: Integrating Evidence and Values
Hunink, M. G. M., Weinstein, M. C., Wittenberg, E., Drummond, M. F., Pliskin, J. S., Wong, J. B., & Glasziou, P. P. (2014) · doi:10.1017/cbo9781139506779 · Cited by 425 · see citing works
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Modeling Using Discrete Event Simulation
Karnon, J. D., Stahl, J. E., Brennan, A., Caro, J. J., Mar, J., & Möller, J. (2012) · Medical Decision Making · doi:10.1177/0272989x12455462 · Cited by 344 · see citing works
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Self-citationMarkov decision processes: a tool for sequential decision making under uncertainty
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The Cost-effectiveness of Preventing AIDS-Related Opportunistic Infections
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Conceptualizing a Model: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-2
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Cost-Effectiveness of Sirolimus-Eluting Stents for Treatment of Complex Coronary Stenoses
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Self-citationDecision Making in Health and Medicine: Integrating Evidence and Values
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Patient-specific decisions about hormone replacement therapy in postmenopausal women
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Recent developments in decision-analytic modelling for economic evaluation
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Screening for Cancer: Theory, Analysis, and Design.
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Determining Transition Probabilities
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Pharmacoeconomic Analyses Using Discrete Event Simulation
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When should decision-analytic modeling be used in the economic evaluation of health care?
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Self-citationCost-Effectiveness of Alternative Management Strategies for Patients with Solitary Pulmonary Nodules
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Radiation-Related Cancer Risks From CT Colonography Screening: A Risk-Benefit Analysis
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A policy model to evaluate the benefits, risks and costs of warfarin pharmacogenomic testing
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Decision-analytic modeling to evaluate benefits and harms of medical tests: uses and limitations
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Self-citationModelling in economic evaluation
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Self-citationChapter 19: Cost-effectiveness of cervical cancer screening
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Toward a Peer Review Process for Medical Decision Analysis Models
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Cost-Effectiveness of HIV Screening in Patients Older than 55 Years of Age
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Educated Guesses: Making Policy about Medical Screening Tests
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Self-citationPatient-specific decisions about hormone replacement therapy in postmenopausal women
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Self-citationThe cost-effectiveness of screening for hereditary hemochromatosis in Germany: a remodeling study
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Self-citationKeeping the noise down: common random numbers for disease simulation modeling
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Cost-effectiveness of primary HPV screening for cervical cancer in Germany—a decision analysis
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Self-citationCost Effectiveness of Prophylaxis for Opportunistic Infections in AIDS
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“[1–6] relates to the application of modeling techniques to the area”
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“Other articles in this series [1–6] describe best practices for conceptualizing models, building and applying particular types of models, and transparency and validation.”
BackgroundEddy, D. M., Hollingworth, W., Caro, J. J., Tsevat, J., McDonald, K. M., & Wong, J. B. (2012) · Value in Health · doi:10.1016/j.jval.2012.04.012 · Cited by 466 · see citing works
<p>Synthetic and External Controls in Clinical Trials – A Primer for Researchers</p>
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“[3] Siebert U, Alagoz O, Bayoumi AM, et al.”
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Conceptualizing a Model: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-2
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Epigenome-based cancer risk prediction: rationale, opportunities and challenges
Widschwendter, M., Jones, A., Evans, I., Reisel, D., Dillner, J., Sundström, K., Steyerberg, E. W., Vergouwe, Y., Wegwarth, O., Rebitschek, F. G., et al. (2018) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2018.30 · Cited by 160 · see citing works
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The Changing Burden of Hepatitis C Virus Infection in the United States: Model-Based Predictions
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Cost-effectiveness analyses of genetic and genomic diagnostic tests
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Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers
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Concepts of ‘Personalization’ in Personalized Medicine: Implications for Economic Evaluation
Rogowski, W. H., Payne, K., Schnell‐Inderst, P., Manca, A., Rochau, U., Jahn, B., Alagöz, O., Leidl, R., & Siebert, U. (2014) · PharmacoEconomics · doi:10.1007/s40273-014-0211-5 · Cited by 98 · see citing works
“Taking into account the need to analyse highly complex pathways, patient histories, and treatment options, as well as the need to analyse the impact of PM on service capacity, means that it may be desirable to replace cohort-based models by individual-level models such as state-transition microsimulation or discrete-e…”
Self-citationLeal, J., Gray, A. M., Hawley, S. J., Prieto‐Alhambra, D., Delmestri, A., Arden, N., Cooper, C. C., Javaid, M. K., Judge, A., & and the REFReSH Study Group (2016) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.2995 · Cited by 88 · see citing works
“An individual-based (ie, microsimulation) Markov model would have facilitated tracking of each individual’s history and time-varying utility values, but would have been more time-consuming to construct and computationally intensive to run.((30)) Finally, we only included health care and care home costs, and so exclude…”
BackgroundModelling for policy: The five principles of the Neglected Tropical Diseases Modelling Consortium
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Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions
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Limitations of Basing Screening Policies on Screening Trials
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“Moreover, the four factors can increase the complexity of the model structure such that the number of health states required to represent the structure as a cohort model becomes unmanageable [4].”
BackgroundPrabhu, V. S., Dubberke, E. R., Dorr, M. B., Elbasha, E. H., Cossrow, N., Jiang, Y., & Marcella, S. (2017) · Clinical Infectious Diseases · doi:10.1093/cid/cix809 · Cited by 66 · see citing works
Developing WHO guidelines: Time to formally include evidence from mathematical modelling studies
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Modeling the Effects of E-cigarettes on Smoking Behavior
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“21,22 in this study, we develop a computational model of traditional cigarette smoking (smoking) and e-cigarette use to examine how different levels of e-cigarette effects on adult smoking behaviors (i.e., smoking initiation and cessation) generates changes to population-level smoking patterns…”
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Addressing Challenges of Economic Evaluation in Precision Medicine Using Dynamic Simulation Modeling
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Economic Return From the Women's Health Initiative Estrogen Plus Progestin Clinical Trial
Roth, J. A., Etzioni, R., Waters, T. M., Pettinger, M. B., Rossouw, J. E., Anderson, G. L., Chlebowski, R. T., Manson, J. E., Hlatky, M. A., Johnson, K., et al. (2014) · Annals of Internal Medicine · doi:10.7326/m13-2348 · Cited by 58 · see citing works
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Krebs, E., Enns, B., Evans, E., Urada, D. I., Anglin, M. D., Rawson, R. A., Hser, Y., & Nosyk, B. (2017) · Annals of Internal Medicine · doi:10.7326/m17-0611 · Cited by 58 · see citing works
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“State‐transition models assume that individuals can be in different (health) states and move (transition) between them.(21) They are typically described using the framework of either Markov models or individual‐based models.”
BackgroundPredicted Lifetime Health Outcomes for Aducanumab in Patients with Early Alzheimer’s Disease
Herring, W. L., Gould, I. G., Fillit, H. M., Lindgren, P., Forrestal, F., Thompson, R., & Pemberton-Ross, P. J. (2021) · Neurology and Therapy · doi:10.1007/s40120-021-00273-0 · Cited by 56 · see citing works
Advantages and disadvantages of discrete-event simulation for health economic analyses
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“…in particular, individuals are often defined as belonging to one of a finite number of mutually exclusive and collectively exhaustive states (e.g. 37 Conceptualized in this way, one may see parallels between the data-generating process represented by a DAG and the process by which individuals…”
BackgroundInfluentialWolcherink, M. O. O., Behr, C., Pouwels, X., Doggen, C., & Koffijberg, H. (2023) · PharmacoEconomics (Auckland) · doi:10.1007/s40273-023-01287-2 · Cited by 54 · see citing works
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Developing WHO guidelines: Time to formally include evidence from mathematical modelling studies
Egger, M., Johnson, L. F., Althaus, C. L., Schoeni, A., Salanti, G., Low, N. N., & Norris, S. L. (2018) · F1000Research · doi:10.12688/f1000research.12367.2 · Cited by 51 · see citing works
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“A cohort-based Markov state transition model [23] was developed to evaluate the long-term costs, and effectiveness of adding bedaquiline to a BR, compared with BR alone, in the treatment of adult patients with pulmonary MDR-TB.”
BackgroundAguiar, M., Andronis, L., Pallan, M. J., Högler, W., & Frew, E. J. (2019) · European Journal of Clinical Nutrition · doi:10.1038/s41430-019-0486-x · Cited by 49 · see citing works
Judge, A., Javaid, M. K., Leal, J., Hawley, S. J., Drew, S. E., Sheard, S. B., Prieto‐Alhambra, D., Gooberman‐Hill, R., Lippett, J., Farmer, A., et al. (2016) · Health Services and Delivery Research · doi:10.3310/hsdr04280 · Cited by 48 · see citing works
Cost-Effectiveness of a Pharmacogenetic Test to Guide Treatment for Major Depressive Disorder
Groessl, E. J., Tally, S. R., Hillery, N., Maciel, A., & Garces, J. A. (2018) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2018.24.8.726 · Cited by 48 · see citing works
“Following recommendations, a half-cycle correction was applied in the model.(31) Response Rates Response rates were taken from Bradley et al.”
MethodDiscretely Integrated Condition Event (DICE) Simulation for Pharmacoeconomics
Jaime, J. (2016) · PharmacoEconomics · doi:10.1007/s40273-016-0394-z · Cited by 47 · see citing works
A Systematic Review of Research Guidelines in Decision-Analytic Modeling
Peñaloza-Ramos, M. C., Barton, P. M., Jowett, S. M., & Sutton, A. J. (2015) · Value in Health · doi:10.1016/j.jval.2014.12.014 · Cited by 45 · see citing works
“Under these circumstances, the identification of explicit characteristics of the disease area that affect model selection, for example, the unit of representation, is considered important [11,12,17,19,20].”
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Cost-effectiveness of osimertinib in the UK for advanced EGFR-T790M non-small cell lung cancer
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Closed-Loop Vagus Nerve Stimulation Based on State Transition Models
Ugalde, H. M. R., Rolle, V. L., Bonnet, J.-L., Henry, C., Mabo, P., Carrault, G., & Hernandez, A. I. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2759667 · Cited by 39 · see citing works
“In other contexts, STMs have also been used for optimization problems [21]–[23] and decision making [20], [24].”
Mowatt, G., Hernández, R. A., Castillo, M., Lois, N., Elders, A., Fraser, C. M., Aremu, O., Amoaku, W. M. K., Burr, J. M., Lotery, A. J., et al. (2014) · Health Technology Assessment · doi:10.3310/hta18690 · Cited by 38 · see citing works
Hall, J. A., Konstantinou, K., Lewis, M., Oppong, R., Ogollah, R. O., & Jowett, S. M. (2019) · Applied Health Economics and Health Policy · doi:10.1007/s40258-019-00471-w · Cited by 38 · see citing works
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“Furthermore, STMs are also one of the most widespread modelling techniques in clinical decision analysis to capture transitions in disease status of individual patients (Siebert et al. 2012).”
BackgroundDecision-Analytic Models: Current Methodological Challenges
Jaime, J., & Möller, J. (2014) · PharmacoEconomics · doi:10.1007/s40273-014-0183-5 · Cited by 37 · see citing works
“Growing understanding of the problems inherent in the cohort approach [6] has prompted interest in simulation approaches that decompose the cohort into individual members, whether still using a statetransition framework or not [7]; moving beyond that to incorporate events in a discrete event simulation [8]; or even fu…”
MethodFagery, M., Khorshidi, H. A., Wong, S. Q., Vu, M., & IJzerman, M. J. (2023) · PharmacoEconomics · doi:10.1007/s40273-023-01292-5 · Cited by 36 · see citing works
Barrett, K. A., Hawkins, N., & Fan, E. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000003465 · Cited by 37 · see citing works
Schiller-Fruehwirth, I., Jahn, B., Einzinger, P., Zauner, G., Urach, C., & Siebert, U. (2017) · Value in Health · doi:10.1016/j.jval.2017.04.009 · Cited by 35 · see citing works
Self-citationOlariu, E., Cadwell, K. K., Hancock, E., Trueman, D., & Chevrou‐Séverac, H. (2017) · ClinicoEconomics and Outcomes Research · doi:10.2147/ceor.s135445 · Cited by 35 · see citing works
Effectiveness, benefit harm and cost effectiveness of colorectal cancer screening in Austria
on behalf of the Austrian Colorectal Cancer Screening Model Group, Jahn, B., Sroczynski, G., Bundo, M., Mühlberger, N., Puntscher, S., Todorovic, J., Rochau, U., Oberaigner, W., Koffijberg, H., et al. (2019) · BMC Gastroenterology · doi:10.1186/s12876-019-1121-y · Cited by 34 · see citing works
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“, those decision-analytic modelling including Markov model cohort simulations and individual-based (first-order Monte Carlo) microsimulations [17], b) multistage life table models capable of estimating incidence, prevalence, remission, mortality based on population life tables, which predict the demographic consequenc…”
BackgroundCriteria and Process for Initiating and Developing an ISPOR Good Practices Task Force Report
Malone, D. C., Ramsey, S. D., Patrick, D. L., Johnson, F. R., Mullins, C. D., Roberts, M. S., Willke, R. J., & Marshall, D. A. (2020) · Value in Health · doi:10.1016/j.jval.2020.03.001 · Cited by 32 · see citing works
A multi-phase DES modelling framework for patient-centred care
Gillespie, J., McClean, S. I., Garg, L., et al. (2016) · Journal of the Operational Research Society · doi:10.1057/jors.2015.114 · Cited by 32 · see citing works
Avoiding and Identifying Errors and Other Threats to the Credibility of Health Economic Models
Tappenden, P., & Chilcott, J. B. (2014) · PharmacoEconomics · doi:10.1007/s40273-014-0186-2 · Cited by 31 · see citing works
“Whilst most modelling texts include detailed guidance on the implementation of particular modelling methodologies and techniques [1, 3, 27, 28], little attention is devoted to how one should make and report prospective model development choices.”
BackgroundCost-effectiveness of minimally invasive sacroiliac joint fusion
Cher, D., Frasco, M. A., Arnold, R. J. G., & Polly, D. W. (2015) · ClinicoEconomics and Outcomes Research · doi:10.2147/ceor.s94266 · Cited by 31 · see citing works
Kuhlmann, A., & von der Schulenburg, J. M. G. (2016) · The European Journal of Health Economics · doi:10.1007/s10198-016-0770-9 · Cited by 31 · see citing works
Corneal Collagen Cross-Linking in the Management of Keratoconus in Canada
Leung, V. C., Pechlivanoglou, P., Chew, H. F., & Hatch, W. V. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2017.03.019 · Cited by 31 · see citing works
Degeling, K., Franken, M. D., May, A. M., et al. (2018) · Cancer Epidemiology · doi:10.1016/j.canep.2018.09.008 · Cited by 31 · see citing works
Palmer, A. J., van der Mei, I. A. F., Taylor, B. V., et al. (2019) · Multiple Sclerosis Journal · doi:10.1177/1352458519831213 · Cited by 31 · see citing works
Incerti, D., Summers, N., Ton, T. G. N., et al. (2018) · MDM Policy & Practice · doi:10.1177/2381468318781811 · Cited by 30 · see citing works
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“The cycle length needs to be short enough so that an event occurs at most once per cycle.(47) A cycle length of 6 months could be considered as appropriate according to the timing of the events as used by Brown et al.”
BackgroundMethodVandepitte, S., Putman, K. D., Noortgate, N. V. D., Verhaeghe, N., & Annemans, L. (2020) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.5276 · Cited by 29 · see citing works
Miksch, F., Jahn, B., Espinosa, K. J., Chhatwal, J., Siebert, U., & Popper, N. (2019) · PLoS ONE · doi:10.1371/journal.pone.0221564 · Cited by 28 · see citing works
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“models [6] and discrete event simulation (DES) models [7].”
BackgroundContinuous-Time Semi-Markov Models in Health Economic Decision Making
Cao, Q., Buskens, E., Feenstra, T., et al. (2015) · Medical Decision Making · doi:10.1177/0272989x15593080 · Cited by 27 · see citing works
“Similar to the routinely applied discrete-time models, this type of model entails defining a set of discrete health states reflecting the different conditions that a patient can be in and a set of transition probabilities governing the transitions between these health states.(5) However, compared with a discrete-time…”
ResultBorisenko, O., Beige, J., Lovett, E. G., Hoppe, U. C., & Bjessmo, S. (2013) · Journal of Hypertension · doi:10.1097/hjh.0000000000000071 · Cited by 27 · see citing works
“next health state, the model does not ’remember’ the timing of transition or from which health state the patient moved from [58].”
BackgroundSanyal, C., Aprikian, A. G., Cury, F., et al. (2016) · Cancer · doi:10.1002/cncr.29892 · Cited by 27 · see citing works
Willis, M. S., Fridhammar, A., Gundgaard, J., Nilsson, A., & Johansen, P. (2020) · PharmacoEconomics · doi:10.1007/s40273-020-00922-6 · Cited by 27 · see citing works
Model-Based Economic Evaluation of Treatments for Depression: A Systematic Literature Review
Kolovos, S., Bosmans, J. E., Riper, H. M., Chevreul, K., Coupé, V. M. H., & van Tulder, M. W. (2017) · PharmacoEconomics - Open · doi:10.1007/s41669-017-0014-7 · Cited by 26 · see citing works
“Shorter cycle lengths give a better approximation of the events in real life but increase the computational burden in terms of data requirements and running time [38].”
BackgroundMethodInfluentialThe Burden of Chronic Hepatitis C in China From 2004 to 2050: An Individual‐Based Modeling Study
Wu, J., Zhou, Y., Fu, X., et al. (2018) · Hepatology · doi:10.1002/hep.30476 · Cited by 26 · see citing works
“We developed an individual-based microsimulation Markov model(17) that simulated disease progression of HCV-infected patients in China from 2004 to 2050.”
MethodDecision Support for Medication Change of Parkinson's Disease Patients
Boshkoska, B. M., Miljković, D., Valmarska, A., et al. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105552 · Cited by 26 · see citing works
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Interpretation of trial-based economic evaluations of musculoskeletal physical therapy interventions
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“If some aspect of the health problem cannot be reproduced in an appropriate way, an ISM modeling exercise should be chosen, but sequential decisions on treatment options should not be considered in the same model [18].”
Background
- Authors
- Siebert, U., Alagoz, O., Bayoumi, A. M., Jahn, B., Owens, D. K., Cohen, D. J., & Kuntz, K. M.
- Affiliations
- Harvard University (US)
- St. Michael's Hospital (CA)
- UMIT TIROL - Private University for Health Sciences and Health Technology (AT)
- VA Palo Alto Health Care System (US)
- University of Missouri–Kansas City (US)
- University of Minnesota (US)
- Corresponding author
- Siebert, Uwe
- Venue
- Value in Health
- Publisher
- Elsevier BV
- Published
- 2012-09
- Volume
- 15
- Issue
- 6
- Pages
- 812-820
- ISSN
- 1098-3015 (ISSN-L)
- OpenAlex
- W3143265745
- Semantic Scholar
- 0cbaf9614def29a027d412e18f8a38de48fd0d12
- Cited by
- 495
- References
- 60
- Citations by year
- 2026: 39 · 2025: 38 · 2024: 41 · 2023: 37 · 2022: 29 · 2021: 42 · 2020: 47 · 2019: 41 · 2018: 34 · 2017: 39 · 2016: 38 · 2015: 28 · 2014: 27 · 2013: 7 · 2012: 8
- FWCI
- 52.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Value in Health (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Social Sciences › Sociology and Political Science › Delphi Technique in Research
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Clinical practice guidelines implementation
- Keywords
- state transition modeling, Markov model, microsimulation, cohort simulation, health economic evaluation, health technology assessment, clinical decision analysis, model structure, model parameters, reporting guidelines
- MeSH terms
- Decision Making, Computer-Assisted, Markov Chains, Models, Theoretical, Guidelines as Topic, Advisory Committees, Consensus, Evidence-Based Practice, Comparative Effectiveness Research
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