Cost-Effectiveness Analysis and Decision Modelling: A Tutorial for Clinicians

∗ Address for correspondence: Shankar Prinja, Additional Professor of Health Economics School of Public Health, Post Graduate Institute of Medical Education and Research Sector-12, Chandigarh, 60012, India. moc.liamg@ajnirpraknahs

Copyright © 2019 Indian National Association for Study of the Liver. Published by Elsevier B.V. All rights reserved.

Abstract

Cost-effectiveness analysis (CEA) provides information on how much extra do we need to spend per unit gain in health outcomes with introduction of any new healthcare intervention or treatment as compared to the alternative. This information is crucial to make decision regarding funding any new drug, diagnostic test or determining standard treatment protocol. It becomes even more important to consider this evidence in resource constrained low-income and middle-income country settings. Generating evidence on costs and consequences of a treatment or intervention could be performed in the setting of a randomized controlled trial, which is the perfect platform to evaluate efficacy or effectiveness. However, we argue that randomized controlled trial (RCT) offers an incomplete setting to generate comprehensive data on all costs and consequences for the purpose of a CEA. Hence, it is needed to use a decision model, either in combination with the evidence from RCT or alone. In this article, we demonstrate the application of decision model–based economic evaluation using 2 separate techniques – a decision tree and a Markov model. We argue that application of a decision model allows computation of health benefits in terms of utility-based measure such as a quality-adjusted life year or disability-adjusted life year which is preferred for a CEA, measure distal costs and consequences which are much more downstream to the application of intervention, allows comparison with multiple intervention and comparators, and provides opportunity of making use of evidence from multiple sources rather than a single RCT which may have limited generalizability. This makes the use of such evidence much more acceptable for clinical use and policy relevant.

Abbreviations: BCLC, Barcelona Clinic Liver Cancer; BSC, Best Supportive Care; CAD, Coronary Artery Disease; CEA, Cost-Effectiveness Analysis; DALY, Disability Adjusted Life Year; EE, Economic Evaluation; HCC, Hepatocellular Carcinoma; HCV, Hepatitis C Virus; Hib, Hemophilus Influenza; HPV, Human Papillomavirus; ICER, Incremental Cost-Effectiveness Ratio; PD, Progressive Disease; PFS, Progression-Free State; QALY, Quality Adjusted Life Year; RCT, Randomized controlled trial; SNCU, Special Newborn Care Unit

Economic Evaluation (EE) or cost-effectiveness analysis (CEA) is one of the important aspects of a health technology assessment. Classically, CEA is defined as a comparative assessment of two or more interventions, in terms of their costs and consequences. 1 As the definition suggests, any CEA would comprise two measurements – costs and consequences, which has to be carried out for both the intervention and the comparator/s. Ultimately, it generates the evidence that how much extra do we need to spend per unit gain in health outcomes with introduction of any new healthcare intervention or treatment as compared with the alternative. In the entire process, the key factor which determines the quality of a CEA is how comprehensive are the methods to measure both costs and consequences and is the valuation standardized across interventions. In other words, do we measure all the important costs and consequences which accrue as a result of a given intervention? Consequently, such an assessment can be performed alongside any epidemiological or clinical study which is being used to measure the effectiveness or efficacy, if we also piggy-back measurement of costs alongside. However, an epidemiological study may not be able to measure all costs and consequences comprehensively, in a manner which may be considered appropriate for a CEA. This leads to the need for decision modelling.

Section 1 of this article describes the limitations of undertaking CEA alongside a clinical trial which necessitates use of a decision model. Subsequently, in section 2, we describe how a decision model is able to bridge the limitations of an epidemiological or clinical study in undertaking CEA. We also introduce the 2 types of decision models which are used for CEA, i.e., the decision tree and Markov model. Section 3 uses an illustration of each of the two types of decision models for explanation. A hypothetical example of implementation of special newborn care units (SNCUs) at district hospitals to treat sick newborns is used to explain a decision tree. Similarly, a published CEA of use of sorafenib – drug used for treatment of hepatocellular carcinoma (HCC) is used to explain the Markov model. Finally, we conclude on what caution should be exercised by the clinicians while undertaking a CEA.

Can Randomized Controlled Trial Alone be Used to do an Economic Evaluation?

As introduced previously, the measurement of costs and consequences in a CEA can be undertaken alongside an epidemiological or clinical study. Classically, a randomized controlled trial (RCT) is considered the epidemiological design with highest degree of rigour for internal validity, hence the word RCT will be used as a proxy for an epidemiological study.

An RCT is generally carried out to evaluate the clinical efficacy of a drug, device, treatment or healthcare intervention ( Table 1 ). If alongside the measurement of the health consequences, which is used to measure efficacy, data on cost of delivering the intervention and comparator is also collected, this information can then be synthesized to produce the results for CEA. This appears to be a very good approach for undertaking CEA, as there are numerous RCTs carried out to assess clinical efficacy, and all it needs is an additional data collection for cost of care. However, there are several limitations to using an RCT for doing CEA.

Table 1

Differences in the Approach of Randomized Controlled Trial and Economic Evaluation.

RCT: randomized controlled trial, QALY: quality-adjusted life year.

First, the focus of RCT is to determine the clinical efficacy. In view of this objective, careful selection criteria are applied to recruit subjects and the interventions are delivered in the most optimal manner. Although this may be perfectly justifiable to produce results which have high internal validity, there may be some limitation to generalizability. For example, a trial performed to evaluate the vaccine efficacy ensured that all the kids who were immunized were previously healthy, vaccine was potent and injected in the recommended manner in correct dose and route of administration. However, in reality, when immunization is introduced in a public health program setting, not all children may be vaccinated. Similarly, there may be breakdowns of cold chain leading to lowered potency of vaccine, and some babies may be given vaccine using suboptimal dose or incorrect route. Hence, the effectiveness may be lower than the efficacy reported in RCT. For a CEA which is dealing with a policy question of whether to introduce the vaccine in national immunization schedule, the data on pragmatic real-world effectiveness is more useful than efficacy.

Second, several trials may be carried out for determining clinical effectiveness in terms of outcomes which may be perfectly rational to a particular health condition but may not solve the needs for a CEA. For example, an RCT for determining clinical effectiveness of new antihypertensive drug compared with the existing treatment measured its effectiveness in terms of reduction in blood pressure. However, the appropriate outcome measure which is recommended for a CEA is a generic utility-based measure such as quality-adjusted life year (QALY) or disability-adjusted life year (DALY). Use of such utility-based outcome measures allows comparison of efficiency across a range of different types of interventions applicable for different diseases in different types of patient population. This makes evidence useful for policymaking at a macro level. Hence again, RCT falls short of providing solution for CEA.

Third, on grounds of feasibility, most of the trials are run for short periods which is appropriate enough to demonstrate clinical effectiveness. However, a CEA aims at measuring all the costs and consequences which are a result of the intervention. For example, a clinical trial which may be carried out for a hemophilus influenza type ‘b’ (Hib) vaccine (given to children at 6,10 and 14 weeks of age) which offers protection against pneumonia and meningitis due to the said organism, measured the episodes of Hib disease among vaccinated and unvaccinated cohorts during a 1-year period after vaccine administration. Although this may be sufficient for measuring the vaccine efficacy, however, the protection against Hib disease continues as long as child is susceptible, which is generally about 5 years, and to a lesser degree as long as 15 years. 2 Hence there is a reduction of disease episodes much longer than the trial period. So, although a trial in this case may measure all costs accurately – as all costs related to vaccination are incurred in year 1, it underestimates the overall downstream health benefits as well as cost savings (due to decrease in treatment costs). To overcome this problem of measuring benefits, RCTs will need to be extended till the time intervention continues to be beneficial, so that all costs and consequences are valued credibly. However, this can sometime become unfeasible because of constraints of funding a long-term RCT. This may become even more difficult when the effects of an intervention are much more distant in time, since the application of intervention. For example, in case of a preventive intervention such as vaccine for human papillomavirus (HPV) to protect against cervical cancer among women, although the vaccination is recommended to be carried out around the age of 10–12 years, reduction in the cancer cases continues to happen as late as 60 or 70 years or even later. 3 And it may not be feasible to have resources to follow-up a trial cohort for a lifetime. Hence, RCTs may not offer the medium to generate data for CEA.

Fourth, a trial is generally conducted to evaluate a few alternative options for treatment or addressing a particular health problem. However, decision-making in the field of policy is full of possible scenarios which need to be evaluated for potential implementation. For example, a single question of which is the most appropriate way to screen women for cervical cancer can be further stratified into several scenarios based on which method should be used (pap smear, visual inspection with acetic acid or HPV DNA), which population should be screened (30–65 years, 40–65 years and 50–65 years), how frequently (annual, 3 yearly, 5 yearly, 10 yearly, once in a lifetime). Together these can constitute 16 possible scenarios. However, it may be difficult to have a single RCT with 16 arms to evaluate all possible scenarios. In view of this limitation again, RCT alone cannot be used to generate evidence for CEA.

Bridging the Limitations of RCT: R ole for Decision Modelling

A solution to bridge the limitations of RCT is to either undertake decision modelling alone, or use decision model alongside the evidence generated in RCT. A decision model used for CEA is a biologically plausible sequence of occurrence of health consequences as a result of the decision of undertaking an intervention. The model so prepared does not only shows relationships but also mathematically quantifies the probability of occurrence of such a health consequence or outcome as a result of an intervention. In the mathematical parameterization of a decision model, the researcher can use pragmatic data on effectiveness from a real-word study rather than an RCT. Alternatively, an assumption which justifies the constraints of program implementation or treatment administration in real-world could be incorporated to generate an output which is more acceptable. For example, one may consider findings of a national evaluation which shows that the coverage of routine immunization is not likely to be more than 90% in the best possible scenario, and hence the efficacy of treatment derived from RCT could be modelled on only 90% of the intervention cohort to generate the health consequences. Similarly, data from a universal treatment program of HCV treatment could be used to determine sustained virological response, rather than efficacy data from trial.

Second, the evidence from a 1-year trial of antihypertensive drug on reduction in blood pressure could then be used along with evidence from other studies for effect of lowering blood pressure on long-term consequences such as coronary artery disease (CAD) or mortality or quality of life, to model long-term consequences of the antihypertensive drug on survival, life years and QALY.

The third limitation of an RCT was its inability to have a longer time horizon to capture all cost consequences satisfactorily. A decision model can use a lifetime study horizon to capture all costs and consequences which can accrue as a result of the intervention. Having said that, however, it does not mean that this can be generated without a previous evidence. Hence, a model synthesizes evidence from various inputs to predict long-term costs and consequences. Finally, a model construction is not limited in terms of the number of scenarios which it can potentially evaluate. Thus, it overcomes the last limitation of an RCT by enabling comparison with several possible treatment or program interventions to deal with a given health problem.

Two most commonly used decision models in CEA are a decision tree and a Markov model. Classically, a decision tree is a unidirectional flow of events which begins with the decision of giving an intervention or not. This is followed by occurrence of different sequence of outcomes which may continue to happen with a given probability or chance at each step in a unidirectional way. The tree ultimately ends with a terminal event in which individual may return to full health or may die. The major limitation of a decision tree is its unidirectional flow. This may be suitable for acute disease conditions which follow a particular course because their onset and the patient may either recover completely and live or may live with some long-term sequelae or may die.

However, this may not be the case with chronic noncommunicable diseases. For example, a patient diagnosed with hypertension may not necessarily remain hypertensive all his life. He may recover back to be normotensive with treatment or may progress to a worse off health state such as CAD. Modelling such chronic diseases requires application of a Markov model which differs from a decision tree in allowing transition from any one health state to any other health state, which is biologically plausible as per the scientific understanding of disease course.

The subsequent sections illustrate the use of a decision tree and a Markov model for better understanding.

Decision Model 1: Decision Tree

Let us consider a policy choice between whether to construct a SNCU – a level II intensive care unit, at the level of district hospitals and continue routine care through existing paediatric services. The following hypothetical example illustrates estimation of incremental cost per QALY gained with implementation of a strategy to create SNCU at the level of district hospitals, against a comparator of routine management of sick newborns in these hospitals. A decision tree was constructed for comparing these 2 policy options as shown in Figure 1 .