FAQs

There are two ways to calculate productivity loss - using the human capital or friction method. In the human capital approach, costs associated with productivity losses/gains are estimated by multiplying lost work time by wage (salary). In the friction cost approach, costs associated with productivity losses are estimated by multiplying the lost work hours during the period that the patient must take of work and until a new employee can be hired and trained to replace them by the wage (salary) for the patient. If used explicitly as stated, these approaches place importance on paid work only so can be applied more broadly to value 'productive time lost' (not just time in paid employment). There may also be issues with differences in wages across age groups and genders that should be considered, and potentially adjusted for, when estimating such costs. 

For more information about calculating productivity losses, see this Factsheet.

Incremental cost-effectiveness ratios (ICERs) or economic evaluations can be the primary endpoint within a trial, but this is rarely the case. Trials usually use an efficacy outcome (e.g. reduction in tumour size, reduced viral load) as their primary endpoint. Sample sizes within a trial are usually calculated on the primary outcome; thus with an efficacy measure as the primary outcome, sample size calculations are relatively straight forward and generally produce sample sizes that are achievable. Attempting to power a study based on the ICER as the primary outcome has clear implications for those sample sizes. ICERs comprise a number of variables - cost of the intervention, cost of the comparator, efficacy of the intervention and the efficacy of the comparator. This provides four inputs for the estimate of the sample size, typically resulting in much larger sample sizes than those associated with efficacy endpoints. Lastly, using an ICER as a primary endpoint implies that a target or threshold ICER is available; Australian reimbursement agencies do not have an explicit ICER threshold. 

For more information about sample size in economic evaluations, see this Factsheet.

A social return on investment analysis (SROI) aims to capture the broader social impacts of a health technology, beyond the clinical and immediate patient-relevant outcomes, to include social well-being outcomes.¹

SROI is applied in the evaluation of complex programs that have wide-ranging, long-term impacts on multiple stakeholders. It is not typically used in evaluations of new medicines. Conducting an SROIs is resource intensive, requiring ongoing stakeholder engagement and data collection across a range of variables often outside of the context of a clinical trial.

If you are interested in collecting broader social wellbeing outcomes in your study, without conducting an SROI, reach out to crest@uts.edu.au and we can provide advice on other methods or tools that may be suitable.

^1.     NSW Department of Communities and Justice. (2020). Social Return on Investment (SROI) approach. NSW Department of Communities and Justice, Family and Community Services Insights, Analysis and Research (FACSIAR). https://dcj.nsw.gov.au/documents/about-us/facsiar/facsiar-publications-and-resources/FACSIAR-Guide-Social-Return-on-Investment-SROI-Approach.pdf

In the confines of a clinical study where patients are randomised to a treatment arm, the comparator would be a complete a replacement. In clinical practice often, the comparator (i.e. the current standard of care) would be displaced i.e., existing therapy would be used as a later line in therapy.

In this situation, the comparator for an economic evaluation is 'usual care' or what happens to patients in terms of costs and outcomes in the absence of direct intervention. 

If you intend to conduct an economic evaluation, resource use data from both an intervention and comparator arm is required to inform costs. In some cases, researchers might want to conduct an economic evaluation using the results from a single arm study. This would necessitate using data from a historical comparative cohort - most likely for both costs and outcomes of the comparator arm. If neither is available, then an economic evaluation won’t be possible. 

If the trial was conducted overseas you can still use the efficacy results for your (Australian focused) economic evaluation. If there were a sufficient number of Australian patients, then resource use for those patients could be used to calculate costs. If there were an insufficient number of Australian patients, you would need to review the pattern of resource use data from the trial to determine if it is applicable to the Australian setting. Trial based resource use, could then be costed using local, Australian prices for those inputs (e.g., PBS prices for drugs, MBS fees for medical and other health care services, and AR-DRG for in-patient hospital care). 

This is a fee that will be charged by Services Australia at the time they provide the data. The fee depends on time for data collection, the data variables requested and the number of people in the trial for whom data will be extracted. Most of the cost is associated with the scripting of the extraction. Most investigators budget approximately $5,000 AUD per Medicare data extraction.

Yes, this is what is referred to as a linked data-set. Patient data would be merged using a unique identifier. To do this will usually require consent from the patient to undertake data linkage. This would allow researchers to obtain a more comprehensive understanding of resource use. Within trials, linkage can often be performed at the time of analysis (providing that access to hospital records and administrative data has been obtained). There are also specialised linkage units across Australia that can assist with the linkage of large data sets - see the Population Health Research Network for more information. 

Strictly speaking, no. Medicare (MBS and PBS) report on services provided in the outpatient setting, or to private hospital patients. AR-DRGs, reflect in-patient care. In some cases, there may be some overlap. For example, Medicare data may report services that there used in the hospital setting i.e. services used by patients who chose to be admitted as a ‘private’ patient in a public hospital. There are strategies to manage this data in the analysis to prevent ‘double-counting’.

For more information you can visit https://www.ihacpa.gov.au/

It is important to separate resource use from costing. Resource use measures tell us what types of services are being delivered, the quantum, and importantly, what is required for the delivery of those services. We can then attach a cost to each of those services based on the price of its components. Having an MBS code for a service means that we know there is a pre-existing MBS fee that we can use as an indicator of the cost of providing that service (the actual cost may be higher, particularly where practitioners charge above the MBS scheduled fee). For services for which an MBS code is not available, we can sometimes collect information on service level use based on hospital records. We might collect information as part of the trial on the resources involved in delivering that service and estimate our own cost (this is called bottom-up costing; and while it is the most accurate of costing methods, it is time consuming). Alternatively, we can consider the use of an existing MBS fee for a similar service (if we are confident that the requirements for delivery of those services are similar) as a proxy for the service of interest.

Post-market monitoring continues to evaluate the safety and efficacy of a medicine after it is available on the market. Monitoring works through:

•  adverse drug reaction reporting from healthcare professionals, state and territory health departments, hospitals, industry and consumers;
•  risk management plans completed by Sponsors as part of the registration process; and
• environmental scanning through review of medical literature (such as post-approval studies), other regulatory bodies and media.

Real-world data from post-market surveillance can inform Health Technology Assessment (HTA) agencies and regulatory bodies about:

• Long-term outcomes of clinical endpoints or safety outcomes,
• New safety concerns that arise,
• Patterns of long-term healthcare utilisation,
• Adherence, uptake of medicines or discontinuation rates,
• Patterns of prescribing that can inform the clinical pathway.

In some instances, regular assessment of health-related quality of life or other patient-reported outcome measures may also be collected, which can inform the patient’s long-term quality of life and other relevant patient outcomes.

Post-market data also tells us about how the medicine is used in the real-world setting and how or why that differs from the trial setting. These data can confirm or challenge assumptions made in economic analyses presented as part of applications for funding to HTA agencies.

In particular, those data can be used to provide information about what is happening in practice, which is critical in defining the relevant comparator within an economic evaluation.  It can also provide information about the patients being treated, which is important when shaping the context to which an economic evaluation applies, and in understanding whether any patient-specific characteristics (e.g., age, sex, ethnicity, employment status, rurality) affect current care and therefore likely cost-effectiveness.

A quality adjusted life year (QALY) is the standard metric used in cost-utility analysis. It is calculated by combining overall survival with quality of life weights. QALYs tell us not just how long people are surviving, but how well they are living. Using QALYs as an outcome in an economic evaluation allows us to make comparisons of cost-effectiveness across interventions and conditions.

For more information about how QALY weights are derived, see this Factsheet.

Different questionnaires will capture quality of life domains differently. When thinking about collecting quality of life to estimate quality adjusted life years (QALYs), the instrument you choose depends on which domain you think your intervention or condition will have the greatest impact, and choosing a questionnaire that is more sensitive to changes in that domain. For example, if you are looking at an intervention that is likely to affect psychological outcomes, you may choose an instrument like the Assessment of Quality of Life (AQOL). For more information about an appropriate quality of like instrument contact cquest@uts.edu.au.

From a health economics perspective, and in the oncology setting, instruments such as the EQ-5D-5L, EORTC QLQ-C30 or FACT-G can be used to provide values for use in an economic evaluation.

For more information about health related quality of life for economic evaluations in cancer, see this Factsheet.

Quality of life should always be collected at baseline. How often during the trial that this is repeated depends on what you are doing to the patient, how often, and what is the progression of the disease in which you are interested. For example, if the patient requires more frequent treatment, then ideally the questionnaire would be repeated more frequently. The quality of life questionnaires should also be repeated during the follow-up period. Frequent administration of the questionnaire is going to capture changes in quality of life during and after treatment, but has to be balanced against patient burden. 

There are two things to note here.

• First, because QALYs combine length of life and quality of life, we would expect that QALY estimates would vary based on age because that affects the number of years that we can adjust for quality.
• Second, age can contribute to how a person responds while using a medicine, in terms of efficacy, adverse effects, and/or co-morbidities, and this consequently impacts their quality of life whilst on treatment. 

It is common for trials to recruit patients within a wide age bracket e.g., 18 to 65 years. The aim in larger comparative trials is to randomise patients to the intervention and comparator arms so that the distribution of participants amongst different age groups is similar (or reflects what we expect to see in the non-trial population).

So, while age can affect the quality of life weights used in QALYs, the internal validity of the trial i.e., through randomisation of participants, aims to minimise potential biases on the resulting QALY calculations. It might be that subgroup analyses, can be used to test the importance of any difference based on age in either survival or quality of life weights. 

Yes, the Cancer Quality of life Expert Service Team (CQUEST) have developed a conceptual framework that provides a step-by-step guide to help you determine which concepts you want to measure in your trial and select the best patient-reported outcome measure (PROM).

The modular approach is a way to reduce questionnaire burden on study participants. In this approach, investigators can select the domains or items of interest within an existing patient reported outcome measure that are most relevant for their study. 

The potential benefits of a modular approach must be weighed against challenges such as introducing potential bias by only selecting domains that may favour the intervention being investigated, or reducing comparability with other trials by excluding certain domains. Additionally, selecting domains from an existing tool can affect the order in which questions are presented to study participants, which may affect how well the tool works in capturing changes in the outcomes of interest. These and other issues are discussed in this publication.

If you are interested in adopting a modular approach to the collection of health-related quality of life outcomes in your study, reach out to cquest@uts.edu.au.

It is common for trial follow-up to end before all patients in the trial have died. In these cases, conducting an in-trial economic evaluation, as well as an extrapolated 'beyond-trial' cost-effectiveness analysis is common. Model-based analyses can be used to extrapolate from the trial data to form a comparative assessment of costs and outcomes beyond the trial period. In these model-based analyses, assumptions have to be made about how long patients live, the care that they receive and their ongoing quality of life. We can then test the impact of these assumptions on our cost-effectiveness results using sensitivity analyses i.e., testing different assumptions to assess how robust the results are to the variations we make. 

There are a few tools that people use for assessment of published economic evaluations. The one used most commonly is by Drummond (which is referred to in the following - https://www.valueinhealthjournal.com/article/S1098-3015(18)36201-6/pdf). This is very straightforward to use and will produce essentially a count of items.

A similar tool that produces a count of items and is commonly used is the CHEERs checklist (https://www.equator-network.org/reporting-guidelines/cheers/). These are reporting guidelines but are often used as an assessment of quality.

Reimbursement agencies like PBAC or MSAC will evaluate a new health technology compared to the intervention/service likely to be replaced most in practice. Each technology is considered separately for a particular condition, and not in the context of other conditions that use other interventions. Cost-effectiveness is only one part of that decision making process. The reimbursement agencies will also consider medical need, safety, efficacy, the predicted utilisation and the impact on clinical practice.

Yes, economic evaluations can be used to make decisions about disinvestment.