You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Background {#Sec1}
==========

Acute coronary syndrome (ACS) is a common cardiovascular disease associated with high complication and mortality rates. From the Thai ACS Registry -- a survey conducted at 17 tertiary care centers in 2007 -- the in-hospital mortality rate was 12.6% \[[@CR1]\]. A later survey in 2012 showed a lower rate of in-hospital mortality (4.8%) but the mortality rate at one-year had not decreased (17.7%) \[[@CR2]\]. Moreover, a study of ACS patients under the Universal Coverage (UC) scheme and Civil Servant Medical Benefits Scheme (CSMBS) at all levels of hospital found that the in-hospital mortality rate was approximately 14%, and that ACS was associated with substantial health care use and costs \[[@CR3],[@CR4]\]. Various anti-platelet drugs have been proven to reduce cardiovascular events in ACS patients. Currently, there are several classes of anti-platelet drugs available on the market including aspirin (ASA), thienopyridine products such as clopidogrel or prasugrel, and a new chemical class, the cyclopentyltriazolopyrimidines, which includes the direct-acting oral antagonist of the adenosine diphosphate P2Y~12~ receptor ticagrelor.

According to the 2012 American College of Cardiology/American Heart Association guidelines, combined treatment with ASA plus a P2Y~12~ inhibitor such as clopidogrel, prasugrel or ticagrelor is recommended as standard anti-platelet treatment in ACS \[[@CR5]\]. However, clopidogrel is a pro-drug requiring transformation to an active metabolite, which results in a slower onset and less consistent inhibition of platelet aggregation compared with ticagrelor \[[@CR6]\]. Ticagrelor represents a new treatment option for ACS. The PLATelet inhibition and patient Outcomes (PLATO) study was conducted to determine whether ticagrelor is superior to clopidogrel for the prevention of vascular events and death in a broad population of patients presenting with an ACS \[[@CR7]\]. Thailand was a part of this multi-centre study. In the PLATO study, compared to clopidogrel in combination with ASA, ticagrelor in combination with ASA demonstrated superior efficacy in the prevention of thrombotic events for the composite endpoint of vascular death, myocardial infarction (MI), or stroke (9.8% vs 11.7%, hazard ratio \[HR\] = 0.84; 95% CI 0.77 to 0.92; p \< 0.001). Trial-defined major bleeding events were similar in both treatment groups, but there were statistically significantly more combined major and minor bleeding events in the ticagrelor arm compared with the clopidogrel arm (16.1% vs 14.6%, HR = 1.11; 95% CI 1.03 to 1.30; p = 0.008) and more non-coronary artery bypass grafting (non-CABG) related major bleeding events (4.5% vs 3.8%, HR = 1.19; 95% CI 1.02 to 1.38; p = 0.03).

Treatment of ACS imposes a substantial burden on Thai society as a whole. When a new treatment becomes available, especially one with a higher cost but greater benefit than usual care, it is necessary to critically appraise the cost-effectiveness of this new treatment to determine whether the improvement in treatment efficacy makes financial sense, especially in the era of limited healthcare resources. Therefore, this study aimed to evaluate the long-term cost-effectiveness of ticagrelor and ASA versus clopidogrel and ASA in ACS patients in Thailand.

Methods {#Sec2}
=======

A two-part construct model with a one-year decision tree and a Markov model developed by Nikolic et al. \[[@CR8]\] was used to compare ticagrelor with generic and branded clopidogrel. The model was designed to capture short- and long-term costs and outcomes. The clinical effectiveness data were obtained from the PLATO trial \[[@CR7]\] while cost data were derived from a Thai database. All costs and effects were discounted at 3% per annum as indicated by Thai guidelines \[[@CR9]\]. Costs were presented in the year 2013 and effectiveness was measured in terms of quality adjusted-life years (QALYs). The health care payer perspective was undertaken.

The decision model {#Sec3}
------------------

To model the short-term cost-effectiveness of ticagrelor, a one-year decision tree covering four mutually exclusive health states (no further event, non-fatal MI, non-fatal stroke, and death from any cause) was constructed as shown in Figure [1](#Fig1){ref-type="fig"}. During this phase, each ACS patient received clopidogrel 75 mg once daily plus ASA 75--100 mg or ticagrelor 90 mg twice daily plus ASA 75--100 mg for 12 months. We assumed the treatment duration lasted only one year and that there was no rebound effect for treatment remaining in the Markov cycles. At the end of one year, patients were allocated to one of the six mutually exclusive health states in the Markov model: no further event, non-fatal MI, post MI, non-fatal stroke, post stroke and dead (Figure [1](#Fig1){ref-type="fig"}). The non-fatal MI and non-fatal stroke health states were tunnel states allowing for a worse prognosis for patients in the year in which a non-fatal event occurred compared to a subsequent year. Patients in the post-MI or post-stroke states would remain in those states for all succeeding cycles until they moved to death state. The cycle length was one year with a lifetime horizon. We assumed our Thai cohort of ACS patients had a median age of 62 years (based on PLATO data \[[@CR7]\] and consistent with the average age of ACS patients in the Thai Registry report \[[@CR2]\]) and had similar percentages of ST segment elevation myocardial infarction (STEMI), non-ST segment elevation myocardial infarction (NSTEMI), and unstable angina (UA) as patients enrolled in the PLATO trial (Table [1](#Tab1){ref-type="table"}) \[[@CR7]\].Figure 1**One-year decision tree model and long-term Markov model (adapted from PLATO) \[** [@CR7] **\].**Table 1**Baseline characteristics of patients in the PLATO study, by treatment group\*** \[[@CR7]\]**CharacteristicTicagrelor group (N = 9333)Clopidogrel group (N = 9291)**Age --- median (range)62.0 (19--97)62.0 (21--94)Female sex --- no. (%)2655 (28.4)2633 (28.3)BMI --- median (range)27 (13--68)27 (13--70)Cardiovascular risk factor --- no./total no. (%) Habitual smoker3360 (36.0)3318 (35.7) Hypertension6139 (65.8)6044 (65.1) Dyslipidemia4347 (46.6)4342 (46.7) Diabetes mellitus2326 (24.9)2336 (25.1)Other medical history --- no./total no. (%) MI1900 (20.4)1924 (20.7) Percutaneous coronary intervention1272 (13.6)1220 (13.1) Congestive heart failure513 (5.5)537 (5.8)Final diagnosis of ACS --- no./total no. (%) ST-elevation MI3496 (37.5)3530 (38.0) Non--ST-elevation MI4005 (42.9)3950 (42.5) Unstable angina1549 (16.6)1563 (16.8) Other diagnosis or missing data283 (3.0)248 (2.7)\*Adapted from PLATO \[[@CR7]\].

Effectiveness parameters {#Sec4}
------------------------

For a one-year decision tree, the effectiveness data were derived exclusively from the PLATO trial \[[@CR7]\], as Thailand was included in this multi-centre study. A parametric time-to-event survival model with a Weibull distribution was employed in order to determine the baseline risk of events with clopidogrel treatment and a hazard ratio (HR) of ticagrelor treatment effect. Hence, the ticagrelor event rates were the product of baseline risk and a ticagrelor HR (Table [2](#Tab2){ref-type="table"}).Table 2**Input parameters and values used in the economic modelInput parametersValueSourceOne-year decision tree* Ticagrelor***  No event0.894PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Non-fatal MI0.050PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Non-fatal stroke0.010PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Death any cause0.046PLATO \[[@CR7]\], Nikolic \[[@CR8]\]*** Clopidogrel***  No event0.875PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Non-fatal MI0.057PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Non-fatal stroke0.009PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Death any cause0.059PLATO \[[@CR7]\], Nikolic \[[@CR8]\]** Markov model** ^a^  Annual risk of MI in the no event state0.019PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Annual risk of stroke in the no event state0.003PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Risk of death in no event state^b^2.000Thai Life table \[[@CR10]\], Allen \[[@CR11]\], Taneja \[[@CR12]\]  Risk of death in the non-fatal MI state^b^6.000PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Risk of death in the post MI state^b^3.000PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Risk of death in the non-fatal stroke state^b^7.430Dennis \[[@CR13]\], Hanke \[[@CR14]\]  Risk of death in the post stroke state^b^3.000PLATO \[[@CR7]\], Nikolic \[[@CR8]\]^a^The values in the Markov model are the same for ticagelor and clopidgrel.^b^Hazard ratio over standard mortality.

Due to patients no longer being on study medications after one year according to our assumption, the identical transition probabilities for the second year onwards were employed in both treatment strategies in the Markov model. The difference in both groups occurred for the proportion of patients in the different start states in the Markov model. The transition probabilities from no event to non-fatal MI or non-fatal stroke in the Markov model were estimated with an extrapolation of the observed HRs in the clopidogrel arm of PLATO beyond 1 year \[[@CR7],[@CR8]\]. The annual mortality rate (MR) in the no-event state was estimated using age- and gender-specific MR from Thai lifetables \[[@CR10]\]. To incorporate an increased risk of mortality associated with ACS, a HR was applied to the age- and gender-specific MR of the Thai population. Patients suffering an ACS event have a relatively high risk of a fatal event occurring within one year and the risk declines over time the longer that patients survive without a subsequent ACS event \[[@CR11],[@CR12]\]; hence, the risk of death for patients in the no-event state was double that of standard mortality risk from Thai lifetables. The mortality risks of non-fatal MI, post-MI, and post-stroke were estimated by extrapolation of PLATO trial data \[[@CR7]\]. The mortality risks of non-fatal stroke were derived from the literature \[[@CR13],[@CR14]\].

QALY is the product of utility and life-years gained. Utilities within the one-year decision tree were based on EQ-5D data collected within the PLATO study \[[@CR7],[@CR8]\]. In the Markov model, the baseline utility of the no-event state was also derived from the PLATO trial \[[@CR7],[@CR8]\]. A decrement due to each health state was applied based on a previous study conducted in Thailand \[[@CR15]\] (Table [3](#Tab3){ref-type="table"}).Table 3**Base-case utility valuesParametersValueSourcesOne-year decision tree* Ticagrelor***  No event0.873PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Non-fatal MI0.811PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Non-fatal stroke0.735PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Death any cause0.247PLATO \[[@CR7]\], Nikolic \[[@CR8]\]*** Clopidogrel***  No event0.876PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Non-fatal MI0.814PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Non-fatal stroke0.738PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Death any cause0.250PLATO \[[@CR7]\], Nikolic \[[@CR8]\]** Markov model** ^a^  No event aged less than 70 years0.875PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  No event aged 70--79 years0.843PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  No event aged over 79 years0.781PLATO \[[@CR7]\], Nikolic \[[@CR8]\]  Annual utility decrement MI 1^st^ year Markov model and 2^nd^ year onward0.147Tamteeranon \[[@CR15]\]  Annual utility decrement stroke 1^st^ year Markov model and 2^nd^ year onward0.226Tamteeranon \[[@CR15]\]^a^The values in the Markov model are the same for ticagelor and clopidogrel.

Resource and cost parameters {#Sec5}
----------------------------

This study was carried out from the viewpoint of the payer; hence, only direct medical costs were taken into consideration. These included study medication, concomitant medications, hospitalizations, investigations, interventions, and bleeding-related resources. For the one-year decision tree, healthcare costs incurred in each treatment arm were based on resource used data from the PLATO trial \[[@CR7],[@CR8]\] and unit costs of those resources from data in Thailand (Additional file [1](#MOESM1){ref-type="media"}: TableS1) \[[@CR16],[@CR17]\]. The cost of ticagrelor used was based on the manufacturer's price (94.16 THB per day). Clopidogrel costs were derived from the Drug and Medical Supply Information Center (DMSIC) in Thailand (branded = 72.53 THB per day and the cheapest generic = 0.94 THB per day) \[[@CR18]\]. The drug costs included 7% VAT. For year 2 onwards, the individual total cost of each health state was exclusively based on data from Thailand \[[@CR4],[@CR15]\]. All costs were adjusted to the year 2013 using the consumer price index (medical care component) \[[@CR19]\]. Table [4](#Tab4){ref-type="table"} presents the total costs used in this study.Table 4**Total costs used in the model** ^**a**^**ItemsCost (THB)SourcesOne-year decision tree* Ticagrelor***  Annual drug cost34,368Manufacturer's price  No event117,404Nikolic \[[@CR8]\], Riewpaiboon \[[@CR16]\]  Non-fatal MI204,851Nikolic \[[@CR8]\], Riewpaiboon \[[@CR16]\], Central office for Healthcare Information \[[@CR17]\]  Non-fatal stroke141,918Nikolic \[[@CR8]\], Riewpaiboon \[[@CR16]\]  Death any cause120,132Nikolic \[[@CR8]\], Riewpaiboon \[[@CR16]\]*** Clopidogrel***  Annual drug cost (branded price)26,473DMSIC \[[@CR17]\]  Annual drug cost (generic price)344DMSIC \[[@CR17]\]  No event120,307Nikolic \[[@CR8]\], Riewpaiboon \[[@CR16]\]  Non-fatal MI207,753Nikolic \[[@CR8]\], Riewpaiboon \[[@CR16]\], Central office for Healthcare Information \[[@CR17]\]  Non-fatal stroke144,821Nikolic \[[@CR8]\], Riewpaiboon \[[@CR16]\]  Death any cause123,034Nikolic \[[@CR8]\], Riewpaiboon \[[@CR16]\]** Markov model**  Total cost MI 1^st^ year Markov model168,196Anukoolsawat \[[@CR4]\]  Total cost MI 2^nd^ year and onward35,926Anukoolsawat \[[@CR4]\]  Total cost stroke 1^st^ year Markov model79,800Tamteeranon \[[@CR15]\]  Total cost stroke 2^nd^ year and onward12,642Tamteeranon \[[@CR15]\]  Total cost of no event21,866Anukoolsawat \[[@CR4]\]^a^Detailed table of Thai hospital charges and unit cost (Baht) for the resource use collected in the PLATO trial provided in the Additional file [1](#MOESM1){ref-type="media"}: Table S1.

Model analyses {#Sec6}
--------------

Costs and QALY were calculated over a lifetime horizon and presented as the incremental cost-effectiveness ratio (ICER) which was the ratio of an incremental cost and an incremental QALY. The model was constructed and run using Microsoft Excel version 2007.

Sensitivity analyses {#Sec7}
--------------------

A series of sensitivity analyses were carried out to address uncertainty of parameters in the model. One-way sensitivity analysis was conducted to assess the uncertainty surrounding each parameter individually. The variables tested in the one-way sensitivity analysis included all transition probabilities, utilities and cost parameters. Transition probabilities were varied first by ±20%, and then by a reduction of 50% and an increase of 200%. All cost parameters were varied by 20% except drug costs, the ticagrelor cost was varied by 10% while the cost of the least expensive generic clopidogrel was set at 10% lower than current prices, and the cost of branded clopidogrel was set at 10% higher than current price. The results were presented as a tornado diagram. Probabilistic sensitivity analysis (PSA) was also undertaken. With the assigned distribution to each model parameter, uncertainty was then propagated through the model using Monte Carlo Simulation with parameter values drawn at random. The decision analysis model was simulated on a thousand iterations. The PSA results of ticagrelor vs branded clopidogrel and ticagrelor vs generic clopidogrel were presented as cost-effectiveness acceptability curves. For the Thai context, an ICER of less than 160,000 THB/QALY is considered cost effective \[[@CR20]\]. This is consistent with WHO recommendations that an ICER of 1GDP per capita demonstrates acceptable cost-effectiveness, while an ICER of over 3GDP per capita is justified to be considered not cost-effective \[[@CR21]\]. The GDP per capita in Thailand in 2013 was 174,658 THB \[[@CR22],[@CR23]\].

Results {#Sec8}
=======

The results showed that, compared with either generic or branded clopidogrel, ticagrelor treatment had a higher total cost with higher QALY. However, the difference in cost was less marked in analyses using branded versus generic clopidogrel (6,553 vs 31,918 THB respectively) resulting in a lower ICER of branded clopidogrel than generic clopidogrel (60,055 vs 292,504 THB/QALY respectively) compared with ticagrelor (Table [5](#Tab5){ref-type="table"}).Table 5**Base-case resultTicagrelorClopidogrelIncrementalICERTicagrelor vs generic clopidogrel** Costs (THB)368,747336,82931,918 Life-years9.2019.0790.122261,197 QALYs7.7117.6020.109292,504**Ticagrelor vs branded clopidogrel** Costs (THB)368,747362,1946,553 Life-years9.2019.0790.12253,627 QALYs7.7117.6020.10960,055QALYs: Quality adjusted-life years; ICER: Incremental cost-effectiveness ratio.

Sensitivity analysis {#Sec9}
--------------------

Of the 26 variables tested in one-way sensitivity analyses, we found that the cost of clopidogrel, the cost of the no-event state within the trial of both ticagrelor and clopidogrel, and the HR of standard mortality in the no-event state had the greatest impact on ICER (Figure [2](#Fig2){ref-type="fig"}). Compared with branded clopidogrel, ticagrelor had more than 99% probability of being cost-effective within the predefined threshold of 160,000 THB/QALY. However, ticagrelor was less cost-effective compared with generic clopidogrel (Figure [3](#Fig3){ref-type="fig"}).Figure 2**Tornado diagram for one-way sensitivity analysis of ticagrelor vs. clopidogrel.**Figure 3**Cost-effectiveness acceptability curves.**

Discussion {#Sec10}
==========

We conducted a cost-effectiveness study using an analytical model to examine whether the new treatment, ticagrelor in combination with ASA, was cost-effective compared with either generic or branded clopidogrel in combination with ASA for treating ACS patients from a healthcare payer perspective. Our model was composed of a one-year decision tree and a Markov model to capture short- and long-term outcomes. We found that the price of clopidogrel had the greatest impact on ICER. Compared with generic clopidogrel (0.94 THB per day), ticagrelor had a cost per QALY gained of 292,504 THB which was greater than the threshold of 160,000 THB/QALY. However, when branded clopidogrel was used as the comparator, ticagrelor treatment was cost-effective within the predefined threshold for Thailand.

This study is predicated on the assumption that generic clopidogrel is equally as efficacious and safe as the branded counterpart. However, Gomez et al. reported that most clopidogrel copies are not of equivalent quality \[[@CR24]\]. A high number of impurities were found in many copies; over 60% of the generic copies contained more than four times the amount of hydrolysis product or R-enantiomer compared with branded clopidogrel. In addition, 50% of the samples did not comply with the 95--105% limits for content. As a consequence, the results of ACS treatment with generic clopidogrel might not be the same as branded clopidogrel, especially in a very high-risk group, which would affect the conclusions of our analysis.

Several limitations of this study need to be mentioned. Firstly, the information on short-term resource use for the treatment of ACS was based on data from the PLATO trial \[[@CR7]\], which was conducted in accordance with a strict protocol, and may not accurately reflect real practice in ACS management in Thailand. We reconciled this overestimated cost by using the unit cost item from Thai data.

Secondly, due to a paucity of data from Thailand, we estimated the cost of the no-event state in the Markov model from outpatient visit and investigation costs from the 1-year cost of MI reported in Anukoolsawat \[[@CR4]\], but excluding inpatient and intervention costs. As a result, the no-event total cost was higher than the total cost of 2^nd^ year stroke in the Markov model. This limitation was due to the fact that available published reports on the cost of MI and stroke were based on data collected from two different centers, a medical school (for MI) and a government neurological institute (for stroke). The costs might not be comparable because of different treatment and resource use patterns at different centers. To address this, we tested the impact of stroke cost parameters on the model results in the sensitivity analyses. We varied the cost of stroke during the first year and from year 2 onwards in the Markov model by 100% and found that the ICER results were not sensitive to these two parameters.

A third limitation arises from the structure of the Markov model, which does not explicitly allow patients to suffer multiple cardiovascular events in their lifetime. Furthermore, this study is based on a payer perspective and limited only to direct medical costs. Direct non-medical costs such as transportation or indirect costs such as lost productivity are not taken into consideration. The finding from Anukoolsawat \[[@CR4]\] reported that ACS was associated with a high economic burden for patients and their families resulting from loss of productivity, and that the indirect cost of ACS was even higher than the direct health care costs. Therefore, excluding these types of costs underestimates the true total cost of ACS to Thai society.

Conclusions {#Sec11}
===========

This health economic analysis supports the cost effectiveness of ticagrelor compared with branded clopidogrel in Thailand. The ICER of ticagrelor compared with branded clopidogrel is 60,055 THB/QALY. Therefore, ticagrelor appears to be an economically valuable treatment for ACS compared with branded clopidogrel within the Thai context.

Additional file {#Sec12}
===============

Additional file 1: Table S1.Costs used in the model.

**Competing interests**

Sirana Tinmanee was a former employee of AstraZeneca (Thailand) Ltd.

**Authors' contributions**

YS, SP, TS and PU designed the study. YS and PU contributed to draft of the paper. All authors read and approved the final manuscript.