Recently, a lot of attention has been paid to so-called real world data. Briefly, real world data contrasts with clinical trial data in that it is not obtained in a controlled environment, but rather, as the name implies, in the real world. Patient records or hospital activity data are two examples of real-world data.
However, the potential for using real-world data is not always clear to those working outside the field of health economics. There is no doubt that when it comes to assessing the efficacy of a new drug, the gold standard is randomized controlled clinical trials.
Here's a simplified example that illustrates how real-world data can be an asset as a complement to clinical trial data. Let's imagine a clinical trial for an oncology drug, where patients are randomly allocated to one of two groups, the treatment group or the control group, and followed over five years.
The manufacturer's submission of evidence on the cost-effectiveness of the drug in question to the authorities responsible for health technology assessment (Infarmed in Portugal) typically includes an economic model that compares the incremental costs and benefits of the drug over a long time horizon (i.e. 30 or more years).
An important component of the incremental benefits of an oncology drug is a greater probability of survival over time for patients allocated to the treatment group, compared to the control group. The main source of data for estimating survival probabilities for each of the groups is the clinical trial itself: the observed survival curves can be extrapolated into the future using statistical models.
Estimating survival probabilities over time based on the clinical trial is a complex exercise. Real-world data can provide a reference point to anchor the control group's extrapolations . For example, if there is a registry of patients with a clinical profile similar to that of the clinical trial participants with data over 10 years, it is possible to calculate the probability of survival of these patients after 10 years and use this, as a first step, to validate the extrapolations of the survival curve for the control group. If the 10-year survival probability observed in the patient registry is significantly different from that suggested by the extrapolations obtained for the control group, it is possible, in a second step, to apply a restriction to the model, forcing the 10-year extrapolation to match the data observed in the real world at that point in time.
In short, by incorporating real-world data, it is possible to validate and correct extrapolations made on the basis of clinical trial data, to ensure that they are in line with what is observed in reality. The result is more reliable cost-effectiveness models, which allow for fairer decisions when it comes to allocating resources in the health sector.