Health Economics and Outcomes Research (HEOR) and Real-World Evidence

Health economics and outcomes research (HEOR) brings together evidence on healthcare costs, clinical outcomes, and patient-relevant outcomes to support decisions across development and access. This article explains how HEOR uses clinical and real-world evidence, where it fits across the product lifecycle, and the practical considerations involved in programming and analysing HEOR studies.

In Brief

• Health economics and outcomes research combines clinical, economic, patient-relevant, and real-world evidence to assess treatment value.
• HEOR can support evidence needs across development, launch, reimbursement, market access, and post-marketing use.
• Analyses may draw on clinical trial data, claims, electronic health records, patient-reported outcomes, and other real-world data.
  
• Real-world evidence depends on suitable data, appropriate study design, and careful interpretation of limitations in the underlying data.
• HEOR programming turns research questions into practical analyses by defining cohorts, extracting relevant data, and applying appropriate statistical methods.
  
  

What is health economics and outcomes research?

Health economics examines how healthcare resources are used and the costs and outcomes associated with different interventions. Outcomes research, also known as health outcomes research, is the study of the end results of specific healthcare practices and interventions. It focuses on understanding what happens in the real world to patients when they receive a certain treatment or method of care. By analysing data from real-world settings, outcomes research helps to assess the effectiveness, safety, and value of these interventions.

HEOR evaluates the value of healthcare treatments by examining their costs, benefits, and impacts on patients. It combines data from various fields, such as economics and patient experiences, to guide healthcare decisions, develop policies, and assess treatment cost-effectiveness. It helps determine whether a new drug offers better value than existing treatments by considering direct medical expenses, indirect costs like lost income, and intangible costs such as pain.

How does HEOR complement clinical trials and RWE?

Clinical trials provide evidence generated under controlled study conditions, while real-world (RWD) are collected through routine healthcare and other sources outside conventional clinical trials. Analysis of RWD can generate real-world evidence (RWE), which can complement clinical trial findings by providing information on treatment use and outcomes in real-world settings.

Where does HEOR fit across the product lifecycle?

HEOR plays a critical role throughout the entire lifecycle of drug development, from clinical trials to pricing and marketing strategies. HEOR activities can also continue after launch, where real-world evidence can contribute to the ongoing assessment of treatment use, outcomes, and value. It helps clinical research by guiding decision makers on patient access to specific drugs and services whereas traditional economic evaluations focus solely on cost-effectiveness.

Clinical trials are performed to evaluate the safety and effectiveness of new drugs in comparison to current treatments or placebos. Evidence from clinical trials can contribute to HEOR analyses alongside information on costs, resource use, and other outcomes. HEOR evidence can therefore inform development decisions without determining them in isolation.

HEOR helps to pinpoint patient groups that are most likely to benefit from a treatment and creating methods for evaluating health outcomes. HEOR also offers essential guidance for estimating prices, analysing cost-effectiveness, and supporting reimbursement decisions. These insights are vital for supporting commercialisation efforts, such as determining product positioning and planning launch strategies.

How does HEOR support HTA, pricing, reimbursement, and market access?

Health technology assessment (HTA) uses clinical, economic, and other evidence to inform decisions about the value and use of health technologies. HEOR can contribute evidence for HTA, pricing, reimbursement, coverage, and market access, although the evidence required and the way it is assumed can differ between healthcare systems.

Various organisations use HEOR to guide strategic decisions such as:

• Pharmaceutical and medical device companies use HEOR to decide which drugs to develop, how to price them, and how to effectively market them
• Healthcare providers, such as hospitals and clinics, apply HEOR insights to improve care delivery and patient outcomes.
• Health insurance companies rely on HEOR to determine which treatments to cover and at what reimbursement levels.
• Government agencies at local, state, and federal levels, including programs like Medicare and Medicaid, utilise HEOR to manage healthcare access while balancing budgetary constraints.
  
  

Working with Longitudinal Databases, Big Data & SnowflakeSQL

To assess what is happening in the real world, rather than using clinical trials to collect data, outcomes research often uses retrospective, non-interventional studies performed on huge databases which contain de-identified medical records. These databases are often called longitudinal databases as they are able to track patients over multiple years.

Examples include administrative medical and pharmacy claims databases, such as the Truven Health MarketScan® Research Database, which includes longitudinal claims data covering more than 300 million cumulative lives and data dating back to 1995, and the Clinformatics Data Mart from Optum, a longitudinal claims database covering patients across the United States. Clinical databases, like the Premier Healthcare Database, incorporate data from more than 1,400 hospitals and healthcare systems.

Due to their huge size and differing structures, working with these databases presents many challenges. In particular, data extraction times and the resulting extracted datasets can become unmanageably large. With traditional SAS programming techniques, the process of querying, extracting, and processing data can be inefficient and slow, leading to long turnaround times.

Therefore, it requires an appropriate environment which would make programming on these large datasets more efficient.  Raw data obtained from vendors are pre-processed to correct inconsistencies, transform data into a usable format, and integrate information from various sources. This pre-processed data is then loaded into local systems (data warehouse) designed to manage big data.

At Quanticate we have been using the Snowflake database and find it to be more efficient than the standard SAS datasets as it is designed in a way to handle the large volumes of big data compared to typical SAS data structure. Snowflake is a data warehouse solution that offers scalable, high-performance data management and analytics, making it well-suited for handling extensive datasets and complex queries.

A programmer needs to demonstrate a technical proficiency of handling large datasets and be familiar with Snowflake SQL syntax in order to process the data directly into the database. This way, handling huge datasets is less time consuming. Only final extracts are downloaded into SAS datasets for further analysis.

Outcomes Research Study Design and Data Usage

When programming an outcomes research project, a sponsor may be looking for experienced programming resource with an understanding of statistics and database design. Data needs to be handled carefully, keeping in mind all limitations.

Instead of the typical clinical trial structure where you are testing against a predicted hypothesis to validate the efficacy of a drug, outcomes research can use real-world data to investigate patterns, treatment use, and health outcomes according to the research question. You can perform this type of research even before a clinical trial has started to assess if the potential study population is suitable for the desired trial.

An Outcomes Research study can consist of any number of the following parts:

• Measuring presence of a disease in a population
• Tracking usage of given drug among selected population across years
• Comparing selected drugs
• Analysing Health Care Resource Utilisations and costs
• Analysing potential clinical trial population
• Comparing case and control cohorts
• Analysing treatment patterns (persistence on given drug, switches to competitors, uses of generics)
• Conducting sponsor’s specific analysis
  
  

Requested analysis depends on the study design. In addition to descriptive statistics, more sophisticated analysis can be performed (i.e. logistic regressions and survival analysis).

A study may involve the following programming stages:

1. Defining/extracting cohort (e.g. list of patients meeting inclusion/exclusion criteria)
2. Performing requested analyses for selected patients. Most common are: demographic characteristics, comorbidities analysis, drug persistence, cost and HCRU
3. Extracting required data for the patients in cohort at the request of the sponsor’s hypothesis: inpatient/outpatient claims, drug claims etc.

Usually there is one main programmer and one quality control programmer which means that a single programmer is independently taking care of all the phases. For more complicated studies, input from the main programmer has an impact on the final shape of the study. It requires close cooperation with the sponsor and statistician.

What are the limitations of HEOR and RWD?

Real-world data need to be suitable for the research question and interpreted in the context of how the data were collected. Data completeness, quality, study design, and transparency can affect confidence in the resulting evidence. These considerations sit alongside the practical challenges of extracting and analysing large datasets described above.

Conclusion

In conclusion, health economics and outcomes research brings together evidence on treatment value, while outcomes research programming supports the practical analysis of the data needed to answer specific research questions.

Quanticate's statistical programming team supports sponsors with health economics and outcomes research studies, including cohort development, real-world data analysis, healthcare resource utilisation, cost analyses, and statistical programming across large longitudinal datasets. Request a consultation and a member of our team will be in touch.