Outcomes research aka health outcomes research, is the study of the end results of particular health care practices and interventions, in other words it is the study of what happens in the real world to patients when they are given a certain treatment or a certain method of care. Outcomes research studies are used to improve the quality and value of healthcare for patients.
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. Each database has its own structure, advantages and limitations. There are a few possible data sources:
All analysed databases are updated on a regular basis (varying from 4 times per year to annual basis), allowing for investigation of trends in treatment pattern using current data.
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.
Therefore it requires an appropriate environment which would make programming on these large datasets more efficient. Raw data obtained from vendors are pre-processed and then loaded into local systems (data warehouse) designed to manage big data.
At Quanticate we have been using the Teradata 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.
A programmer needs to demonstrate a technical proficiency of handling large datasets and be familiar with Teradata 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. Other common companies providing the required environment for programming big data are Oracle, IBM and Microsoft.
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. Outcomes Research programmers provide all required information and advise on best approaches to reach the realisation of the client request.
Instead of the typical clinical trial structure where you are testing against a predicted hypothesis to validate the efficacy of a drug, Outcomes Research study design requires less specific terms and a more general approach that looks to monitor the real world data to find patterns and health outcomes. 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:
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).
Typical Outcomes Research studies consist of 3 phases:
Usually there is one main programmer and one QCer 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.
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