Simple Markov Model Research Articles

Review of open libraries for pharmacoeconomic analysis in R environment

Background. To reveal the full complexity of the relationship between medical intervention and disease outcome, new methods of analysis and modeling are actively being developed, and tools are becoming more complex, for the use of which it is important to understand their limitations and advantages.Objective: conducting a critical review of the main open-source packages in R environment for conducting pharmacoeconomic analysis.Material and methods. The selection of libraries used for pharmacoeconomic analysis in the R environment was carried out based on the keywords “health economic”, “DALY”, “QALY” in the CRAN repository. Only libraries that were valid on the date of the review were included in the study. The selected 10 R software libraries for pharmacoeconomic analysis were reviewed from the standpoint of the number of tools they support, the format of the data used, the possibilities of visualizing results and generating reports, the presence of vignettes and the possibilities of parallelizing calculations.Results. The selected libraries can be divided into three classes: packages for calculating various quality of life indices, libraries for calculating indicators and indices of economic effectiveness of medical interventions (DALY, QALY, ICER), libraries for performing sensitivity analysis of the effect of medical interventions based on decision tree algorithms and Markov models. The libraries “heemod”, “hesim”, “rdesign” allow building simple Markov and semi-Markov models, but preference should be given to “heemod” due to the presence of vignettes. To conduct an analysis using cohort Markov models, partitioned survival models, it is recommended to use the “hesim” library, if there are gaps in the results, it is recommended to use “missingHE”. The “rdesigin” library allows building decision trees indicating the risk of developing certain conditions and the cost of therapy. The “survHE” library for survival analysis, used specifically in health economics, allows you to carry out probabilistic sensitivity analysis based on survival models. To calculate the survival models themselves to identify predictors of a patient's transition from one health state to another, you will need to additionally install the “flexsurv” library. To visualize the results of pharmacoeconomic modeling, you should additionally install the “diagram” and “ggplot2” libraries. Conclusion. The conducted critical review of open source libraries in R environment can serve as a navigator for choosing a tool for performing pharmacoeconomic analysis.

Non-restrictive open vial policy combined with the home visit vaccinations for improving BCG coverage in a high-incidence outreach region: A model-based cost-effectiveness analysis for Indonesia

Bacillus Calmette-Guérin (BCG) vaccination is recommended at birth or in the first week of life to achieve the most beneficial effects in protecting against the most severe type of tuberculosis (TB) disease in children. However, delayed vaccination is commonly reported, especially in outreach or rural areas. We assessed the cost-effectiveness of combining non-restrictive open vial and home visit vaccination strategies in order to increase timely BCG vaccination in a high-incidence outreach setting. We applied a simplified Markov model for the Papua setting, which resembled a high-incidence outreach setting in Indonesia, to assess the cost-effectiveness of these strategies from a health care and a societal perspective. A moderate increase (75% wastage rate and 25% home vaccination) and a large increase (95% wastage rate and 75% home vaccination) scenario were assessed in the analysis. We calculated incremental cost-effectiveness ratios (ICER) based on the incremental costs and quality-adjusted life years (QALYs) gained by comparing the two strategies to the base case scenario (35% wastage rate and no home vaccination). The costs per vaccinated child were US$10.25 in the base case scenario, increasing slightly in the moderate (US$10.54) and large increase scenarios (US$12.38). The moderate increase scenario was predicted to prevent 5783 TB-related deaths and 790 TB cases while the large increase scenario predicted the prevention of 9865 TB-related deaths and 1348 TB cases for the entire lifespan of our cohort. From a health care perspective, the ICERs were predicted to be US$288/QALY and US$487/QALY, respectively, for the moderate and large increase scenarios. Using Indonesia's gross domestic product (GDP) per person as a threshold, both strategies were considered to be cost-effective. We found that the allocation of resources for timely BCG vaccination based on combining home vaccination and a less restrictive open vial strategy could substantially reduce childhood TB cases and TB-related mortality. Although outreach activities are more expensive than vaccination at a health care facility only, these activities proved to be cost-effective. These strategies might also be beneficial in other high-incidence outreach settings.

Deconstructing insect herbivore trivial movement in a monoculture and triculture: Limitations of emigration

AbstractSignificantly higher population densities of a squash pest beetle, Acalymma vittatum, were observed in a squash monoculture than a triculture of squash, maize, and beans. One hypothesis for such differences is that non‐host plants in the triculture cause the herbivore to move more frequently resulting in higher emigration rates from the tricultures. Plant to plant movement of the beetle was studied in vegetative winter squash to evaluate if differences in components of trivial movement would generate a higher emigration rate from the triculture. Tenure times were shorter on plants in the triculture than on plants in the squash monoculture, there was no movement directionality, movement distances were greater in squash monocultures than in tricultures and were greater during July than August. In the tricultures, plant‐to‐plant transitions were primarily to maize from non‐host plants and to beans or squash from host plants during July, but during August, movement from any plant was primarily to maize. We integrated the values of the movement components from vegetative winter squash into a simple Markov model, and found that during July, emigration was estimated to be faster from the triculture than the monoculture, supporting the hypothesis. However, in August, emigration was estimated to be slower from the triculture. In August, maize plants were tall and, by acting like a fence, greatly reduced the movement distance of beetles. The effect of the differences in emigration rate on beetle population density was greatest for small patches, indicating that emigration may be important only in small‐scale production systems.

한국 신용등급 전이행렬에 대한 시계열성 연구

Many articles offer credit transition matrix is modelled by time-homogeneous Markov Chain. However, some articles in overseas provide the existence of time series patterns and relationship with economic situations in ratings changes. No empirical study regarding systematic approaches for time series analysis among articles related ratings changes in Korea, and that is a reason we conduct this article. This article uses time series data are credit ratings migrations matrices of all three ratings agencies in Korea from 1998 to 2020. We find time series patterns in most of ratings changes, especially rating downgrades changes. Additionally, we propose a time series model that is an autoregressive model and estimate cumulative default rates using a model for ratings changes. Our model shows a better estimated result compared to a simple Markov model.

Probabilistic Inference with Polymerizing Biochemical Circuits.

Probabilistic inference—the process of estimating the values of unobserved variables in probabilistic models—has been used to describe various cognitive phenomena related to learning and memory. While the study of biological realizations of inference has focused on animal nervous systems, single-celled organisms also show complex and potentially “predictive” behaviors in changing environments. Yet, it is unclear how the biochemical machinery found in cells might perform inference. Here, we show how inference in a simple Markov model can be approximately realized, in real-time, using polymerizing biochemical circuits. Our approach relies on assembling linear polymers that record the history of environmental changes, where the polymerization process produces molecular complexes that reflect posterior probabilities. We discuss the implications of realizing inference using biochemistry, and the potential of polymerization as a form of biological information-processing.

Ordinal synchronization and typical states in high-frequency digital markets

In this paper we study Algorithmic High-Frequency Financial Markets as dynamical networks. After an individual analysis of 24 stocks of the US market during a trading year of fully automated transactions by means of ordinal pattern series, we define an information-theoretic measure of pairwise synchronization for time series which allows us to study this subset of the US market as a dynamical network. We apply to the resulting network a couple of clustering algorithms in order to detect collective market states, characterized by their degree of centralized or decentralized synchronicity. This collective analysis has shown to reproduce, classify and explain the anomalous behavior previously observed at the individual level. We also find two whole coherent seasons of highly centralized and decentralized synchronicity, respectively. Finally, we model these states dynamics through a simple Markov model.

Markov modelling of treatment response in a 30-year cohort study of newly diagnosed epilepsy.

People with epilepsy have variable and dynamic trajectories in response to antiseizure medications. Accurately modelling long-term treatment response will aid prognostication at the individual level and health resource planning at the societal level. Unfortunately, a robust model is lacking. We aimed to develop a Markov model to predict the probability of future seizure-freedom based on current seizure state and number of antiseizure medication regimens trialled. We included 1795 people with newly diagnosed epilepsy who attended a specialist clinic in Glasgow, Scotland, between July 1982 and October 2012. They were followed up until October 2014 or death. We developed a simple Markov model, based on current seizure state only, and a more detailed model, based on both current seizure state and number of antiseizure medication regimens trialled. Sensitivity analyses were performed for the regimen-based states model to examine the effect of regimen changes due to adverse effects. The model was externally validated in a separate cohort of 455 newly diagnosis epilepsy patients seen in Perth, Australia, between May 1999 and May 2016. Our models suggested that once seizure-freedom was achieved, it was likely to persist, regardless of the number of antiseizure medications trialled to reach that point. The likelihood of achieving long-term seizure-freedom was highest with the first antiseizure medication regimen, at approximately 50%. The chance of achieving seizure-freedom fell with subsequent regimens. Fluctuations between seizure-free and not seizure-free states were highest earlier on but decreased with chronicity of epilepsy. Seizure-freedom/recurrence risk tables were constructed with these probability data, similar to cardiovascular risk tables. Sensitivity analyses showed that the general trends and conclusions from the base model were maintained despite perturbing the model and input data with regimen changes due to adverse effects. Quantitative comparison with the external validation cohort showed excellent consistency at Year 1, good at Year 3 and moderate at Year 5. Quantitative models, as used in this study, can provide pertinent clinical insights that are not apparent from simple statistical analysis alone. Attaining seizure freedom at any time in a patient's epilepsy journey will confer durable benefit. Seizure-freedom risk tables may be used to individualize the prediction of future seizure control trajectory.

An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time.

Abnormal behavioral changes in the regular daily mobility routine of a pregnant dairy cow can be an indicator or early sign to recognize when a calving event is imminent. Image processing technology and statistical approaches can be effectively used to achieve a more accurate result in predicting the time of calving. We hypothesize that data collected using a 360-degree camera to monitor cows before and during calving can be used to establish the daily activities of individual pregnant cows and to detect changes in their routine. In this study, we develop an augmented Markov chain model to predict calving time and better understand associated behavior. The objective of this study is to determine the feasibility of this calving time prediction system by adapting a simple Markov model for use on a typical dairy cow dataset. This augmented absorbing Markov chain model is based on a behavior embedded transient Markov chain model for characterizing cow behavior patterns during the 48 h before calving and to predict the expected time of calving. In developing the model, we started with an embedded four-state Markov chain model, and then augmented that model by adding calving as both a transient state, and an absorbing state. Then, using this model, we derive (1) the probability of calving at 2 h intervals after a reference point, and (2) the expected time of calving, using their motions between the different transient states. Finally, we present some experimental results for the performance of this model on the dairy farm compared with other machine learning techniques, showing that the proposed method is promising.

Analysis Estimating the Potential Cost Impact of Utilizing Flash Glucose Monitoring with Optional Alarms in Swedish Adults with Diabetes with Impaired Awareness of Hypoglycaemia, Using Intensive Insulin

IntroductionThe objective of this analysis was to estimate the costs associated with using flash glucose monitoring with optional alarms as a replacement for either traditional continuous glucose monitoring (CGM) or routine self-monitoring of blood glucose (SMBG) in adults with diabetes and impaired awareness of hypoglycaemia (IAH) who use intensified insulin therapy, from a Swedish payer perspective, applying assumptions to simulate hypothetical scenarios.MethodsA simple two-state cohort Markov model was used to calculate the cost per patient treated over a 3-year period, capturing the risk of severe hypoglycaemic events requiring medical assistance and non-adherence using quarterly Markov cycles. The costs considered were those for glucose monitoring and resource use to treat severe hypoglycaemic events. Cost inputs were sourced from Swedish price lists, manufacturer data and resource use reported in the control arm of the HypoDE study. Targeted literature searches were run in PubMed to source the clinical inputs. Uncertainty in the model was considered through one-way sensitivity analysis and scenario analysis.ResultsOver 3 years, flash monitoring with optional alarms resulted in potential cost-savings of Swedish krona (SEK) 7708 and SEK 69,908 per patient when compared to routine SMBG or CGM respectively. Sensitivity and scenario analyses were largely supportive of this conclusion with respect to SMBG, and large cost-savings were consistent across all sensitivity and scenario analyses with respect to CGM.ConclusionUtilizing flash monitoring with optional alarms is potentially a cost-saving treatment strategy compared to routine SMBG or traditional CGM in adults with diabetes using intensive insulin and IAH from a Swedish payer perspective. Future studies in the IAH population will help to assess more precisely the relative cost impact of flash glucose monitoring with optional alarms compared with SMBG and traditional CGM.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13300-021-01099-7.

Modeling Cervical Cancer Screening Strategies With Varying Levels of Human Papillomavirus Vaccination

Cervical cancer screening is a lifesaving intervention, with an array of approaches, including liquid-based cytology (LBC), molecular testing for human papillomavirus (HPV) infection, and combinations via parallel cotesting or sequential triage. Maximizing screening efficacy while minimizing overtreatment is vital, especially when considering how the HPV vaccine will affect the interpretation of results. To estimate the likely outcomes of different screening modalities and to model how the increasing uptake of the HPV vaccine could affect the interpretation of screening results. This decision analytic model established a simple Markov model to compare the outcomes of different cervical cancer screening modalities on a simulated population of women (aged ≥25 years), considering different levels of HPV vaccination. The number of cases of cervical intraepithelial neoplasia (CIN) grade 2 and 3 detected and missed, the number of false positives, and the number of tests required to achieve a given level of accuracy. Positive and negative predictive values of different modalities were simulated under varying levels of HPV vaccination and therefore HPV prevalence. In a simulated population of 1000 women aged 25 years and older with an HPV prevalence of 2%, HPV-based modalities outperformed LBC-based approaches, detecting 19% more true positives (HPV test sensitivity, 89.9% [95% CI, 88.6%-91.1%]; LBC test sensitivity, 75.5% [95% CI, 66.6%-82.7%]). While cotesting markedly reduced missed cases, detecting 29% more true positives than LBC alone (19.5 [95% CI, 19.3-19.7] per 1000 women screened vs 15.1 [95% CI, 13.3-16.5] per 1000 women screened), it unacceptably increased excess colposcopy referral by 94% (184.4 [95% CI, 181.8-188.0] false positives per 1000 women screened vs 95.1 [95% CI, 93.1-97.0] false positives per 1000 women screened). By contrast, triage testing with reflex screening substantially reduced false positives by a factor of approximately 10 (eg, HPV with LBC triage, 9.6 [95% CI, 9.3-10.0] per 1000 women screened). Over a lifetime of screening, reflex approaches with appropriate test intervals maximized therapeutic efficacy; as HPV vaccination rates increased, HPV-based screening approaches resulted in fewer unnecessary colposcopies than LBC approaches (HPV testing, 80% vaccine coverage: 44.1 [95% CI, 40-45.9] excess colposcopies; LBC testing, 80% vaccine coverage: 96.9 [95% CI, 96.8-97.0] excess colposcopies). In this decision analytic model, the effectiveness of cervical cancer screening was dependent on the prevalence of cervical dysplasia and/or HPV infection or vaccination in a population as well as the sensitivity and specificity of various modalities. Although screening is lifesaving, overtesting or modalities inappropriate to the target population may cause significant harm, including overtreatment.

Emergence of effective temperatures in an out-of-equilibrium model of biopolymer folding.

We investigate the possibility of extending the notion of temperature in a stochastic model for the RNA or protein folding driven out of equilibrium. We simulate the dynamics of a small RNA hairpin subject to an external pulling force, which is time-dependent. First, we consider a fluctuation-dissipation relation (FDR) whereby we verify that various effective temperatures can be obtained for different observables, only when the slowest intrinsic relaxation timescale of the system regulates the dynamics of the system. Then, we introduce a different nonequilibrium temperature, which is defined from the rate of heat exchanged with a weakly interacting thermal bath. Notably, this "kinetic" temperature can be defined for any frequency of the external switching force. We also discuss and compare the behavior of these two emerging parameters, by discriminating the time-delayed nature of the FDR temperature from the instantaneous character of the kinetic temperature. The validity of our numerics are corroborated by a simple four-state Markov model which describes the long-time behavior of the RNA molecule.

Single-shot number-resolved detection of microwave photons with error mitigation

Single-photon detectors are ubiquitous and integral components of photonic quantum cryptography, communication, and computation. Many applications, however, require not only detecting the presence of any photons, but distinguishing the number present with a single shot. Here, we implement a single-shot, high-fidelity photon number-resolving detector of up to 15 microwave photons in a cavity-qubit circuit QED platform. This detector functions by measuring a series of generalized parity operators which make up the bits in the binary decomposition of the photon number. Our protocol consists of successive, independent measurements of each bit by entangling the ancilla with the cavity, then reading out and resetting the ancilla. Photon loss and ancilla readout errors can flip one or more bits, causing nontrivial errors in the outcome, but these errors have a traceable form which can be captured in a simple hidden Markov model. Relying on the independence of each bit measurement, we mitigate biases in ensembles of measurements, showing good agreement with the predictions of the model. The mitigation improves the average total variation distance error of Fock states from $13.5\%$ to $1.1\%$. We also show that the mitigation is efficiently scalable to an $M$-mode system provided that the errors are independent and sufficiently small. Our work motivates the development of new algorithms that utilize single-shot, high-fidelity PNR detectors.

Extinction time for the weaker of two competing SIS epidemics

We consider a simple Markov model for the spread of a disease caused by two virus strains in a closed homogeneously mixing population of size $N$. The spread of each strain in the absence of the other one is described by the stochastic SIS logistic epidemic process, and we assume that there is perfect cross-immunity between the two strains, that is, individuals infected by one are temporarily immune to re-infections and infections by the other. For the case where one strain is strictly stronger than the other, and the stronger strain on its own is supercritical, we derive precise asymptotic results for the distribution of the time when the weaker strain disappears from the population. We further extend our results to certain parameter values where the difference between the basic reproductive ratios of the two strains may tend to 0 as $N\to\infty$. In our proofs, we illustrate a new approach to a fluid limit approximation for a sequence of Markov chains in the vicinity of a stable fixed point of the limit differential equation, valid over long time intervals.

Patterns of RNA Editing in Newcastle Disease Virus Infections.

The expression of accessory non-structural proteins V and W in Newcastle disease virus (NDV) infections depends on RNA editing. These proteins are derived from frameshifts of the sequence coding for the P protein via co-transcriptional insertion of one or two guanines in the mRNA. However, a larger number of guanines can be inserted with lower frequencies. We analysed data from deep RNA sequencing of samples from in vitro and in vivo NDV infections to uncover the patterns of mRNA editing in NDV. The distribution of insertions is well described by a simple Markov model of polymerase stuttering, providing strong quantitative confirmation of the molecular process hypothesised by Kolakofsky and collaborators three decades ago. Our results suggest that the probability that the NDV polymerase would stutter is about 0.45 initially, and 0.3 for further subsequent insertions. The latter probability is approximately independent of the number of previous insertions, the host cell, and viral strain. However, in LaSota infections, we also observe deviations from the predicted V/W ratio of about 3:1 according to this model, which could be attributed to deviations from this stuttering model or to further mechanisms downregulating the abundance of W protein.

The Markov model for base-stock control of an inventory system with Poisson demand, non-crossing lead times and lost sales

We study base-stock control of a continuous review single-item inventory system with Poisson demand and lost sales. The item is supplied by an exogenous and sequential system with stochastic lead times (LTs) specified by their mean and standard deviation (SD). Define R as the square of mean/SD and define r as the smallest integer which is at least R. When R<r, we introduce a novel tractable LT distribution called Extended Erlangian. When R=r then Extended Erlangian is the same as pure Erlangian and the simple Markov model presented by Johansen (2005) makes it easy to compute and minimize the long-run average cost per unit time. We present an algorithm to compute the steady state distribution of a new Markov model for base-stock control with Extended Erlangian LTs. For fixed base-stock S, this algorithm can be applied to compute the average cost and it is straightforward (but burdensome for large r) to compute the optimal S and the minimum average cost. We also suggest a reasonable S computed in closed form from simple models with pure Erlangian LTs. The reasonable S is easy to compute and it performs well. Our numerical study illustrates that the average cost goes up as SD goes up, which is in sharp contrast to independence of SD when the LTs are i.i.d.

A Markov chain method for counting and modelling migraine attacks

To ensure reproducibility in research quantifying episodic migraine attacks, and identifying attack onset, a sound theoretical model of a migraine attack, paired with a uniform standard for counting them, is necessary. Many studies report on migraine frequencies—e.g. the fraction of migraine-days of the observed days—without paying attention to the number of discrete attacks. Furthermore, patients’ diaries frequently contain single, migraine-free days between migraine-days, and we argue here that such ‘migraine-locked days’ should routinely be interpreted as part of a single attack. We tested a simple Markov model of migraine attacks on headache diary data and estimated transition probabilities by mapping each day of each diary to a unique Markov state. We explored the validity of imputing migraine days on migraine-locked entries, and estimated the effect of imputation on observed migraine frequencies. Diaries from our patients demonstrated significant clustering of migraine days. The proposed Markov chain model was shown to approximate the progression of observed migraine attacks satisfactorily, and imputing on migraine-locked days was consistent with the conceptual model for the progression of migraine attacks. Hence, we provide an easy method for quantifying the number and duration of migraine attacks, enabling researchers to procure data of high inter-study validity.

Modeling the onset advantage in musical instrument recognition.

Sound onsets provide particularly valuable cues for musical instrument identification by human listeners. It has remained unclear whether this onset advantage is due to enhanced perceptual encoding or the richness of acoustical information during onsets. Here this issue was approached by modeling a recent study on instrument identification from tone excerpts [Siedenburg. (2019). J. Acoust. Soc. Am. 145(2), 1078-1087]. A simple Hidden Markov Model classifier with separable Gabor filterbank features simulated human performance and replicated the onset advantage observed previously for human listeners. These results provide evidence that the onset advantage may be driven by the distinct acoustic qualities of onsets.

Analytics for directed contact networks

Directed contact networks (DCNs) are temporal networks that are useful for analyzing and modeling phenomena in transportation, communications, epidemiology and social networking. Specific sequences of contacts can underlie higher-level behaviors such as flows that aggregate contacts based on some notion of semantic and temporal proximity. We describe a simple inhomogeneous Markov model to infer flows and taint bounds associated with such higher-level behaviors, and also discuss how to aggregate contacts within DCNs and/or dynamically cluster their vertices. We provide examples of these constructions in the contexts of information transfers within computer and air transportation networks, thereby indicating how they can be used for data reduction and anomaly detection.

PNS79 DETERMINING THE BASELINE STRATEGY IN A COST-EFFECTIVENESS ANALYSIS WITH TREATMENT SEQUENCES

To investigate the impact of the baseline strategy on the cost-effectiveness results, when a sequence of treatments is used. We reviewed submissions to the UK National Institute for Health and Care and Excellence (NICE) to understand how economic models that used comparisons of treatment sequences defined the baseline strategy. We then built a simple Markov model to use as a case study. The analysis we conducted contained four hypothetical treatments of varying cost-effectiveness relationships to a fixed control (best-supportive care): TxA was cost effective, TxB was extendedly dominated by TxA, TxC was cost-effective, but had a greater cost than both TxA and TxB, and TxD was not cost-effective. Our review of the NICE submissions showed that, in most cases, authors relied on clinical guidelines, expert opinion or previously developed models to define the baseline strategy (n=31). In several cases the choice of a baseline strategy was not explained (n=9). Several studies used the model to identify the optimal position for the new intervention (n=5). Using the model, all possible permutations between the hypothetical treatments were generated and ranked by their net monetary benefit (NMB). We showed that (i) a non-cost-effective treatment would never be part of an optimal sequence and (ii) the choice of baseline treatment sequence can change the cost-effectiveness estimate of a new intervention. If the aim of the decision maker is the efficient distribution of health-care resources based on cost-effectiveness, then the baseline strategy should be created based on the ranking of the NMB. Ignoring the cost-effectiveness of individual treatments when defining the baseline strategy, may lead to spurious results.

Determining the Baseline Strategy in a Cost-Effectiveness Analysis with Treatment Sequences.

In response to a growing number of treatment options in many disease areas, health technology assessments need to evaluate sequences of treatments instead of individual interventions. This study investigated the impact of the baseline strategy on the cost-effectiveness results, when a sequence of treatments was used. First, we reviewed submissions to the UK National Institute for Health and Care and Excellence to understand how economic models that used comparisons of treatment sequences defined the baseline strategy. We then built a simple Markov model to use as a case study. The analysis we conducted contained four hypothetical treatments of varying cost-effectiveness relationships to a fixed control (best supportive care): Treatment A was cost effective, Treatment B was extendedly dominated by Treatment A, Treatment C was cost effective, but had a greater cost than both Treatment A and Treatment B, and Treatment D was not cost effective. Our review of the National Institute for Health and Care and Excellence submissions showed that, in most cases, authors relied on clinical guidelines, expert opinion or previously developed models to define the baseline strategy (n = 31). In several cases, the choice of a baseline strategy was not explained (n = 9). Several studies used the model to identify the optimal position for the new intervention (n = 5). Using the model, all possible permutations between the hypothetical treatments were generated and ranked by their net monetary benefit. We showed that (1) a non-cost-effective treatment would never be part of an optimal sequence and (2) the choice of baseline treatment sequence can change the cost-effectiveness estimate of a new intervention. If the aim of the decision maker is the efficient distribution of healthcare resources based on cost effectiveness, then the baseline strategy should be created based on the ranking of the net-monetary benefit. Ignoring the cost effectiveness of individual treatments when defining the baseline strategy, may lead to spurious results.