FAQs

If the investigators anticipate differences in resources and better outcomes that may potentially translate into a change in practice, then it is reasonable to consider conducting an economic evaluation. This provides an assessment of the 'value for money' associated with the new intervention; an important piece of information in being able to convince payors or colleagues to change practice. For example, the Pharmaceutical Benefits Advisory Committee (PBAC) and the Medical Services Advisory Committee (MSAC) require economic evidence, generally in the form of a cost utility analysis, for reimbursement purposes in Australia.

For more on conducting economic evaluations alongside a trial see this Factsheet.

Resource use data related to the treatment, administration of the intervention, use of any services related to the intervention and management of adverse effects should be collected. This includes healthcare providers, technicians and lab staff time, all medicines used by patients, prostheses (if relevant) and use of medical equipment. 

If differences are anticipated in terms of indirect costs like travel and clinic time (i.e., time away from usual activities), it may also be reasonable to capture this via a patient completed questionnaire. 

For more information about costing an economic evaluation, see this Factsheet.

The use of hospital resources can be collected from patients' hospital records or case report forms (CRF) or via linkage to hospital records for access to hospital specific data. The use of medicines and healthcare services in outpatient settings can be estimated from administrative data sources, such as Medicare data in Australia (i.e. MBS/PBS).

It is recommended that resource utilisation be collected from randomisation until the trial’s conclusion for both the intervention and control groups.

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.

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.   

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. 

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.