Real-world data analytics
Real-world data are a rich source of information on health and economic outcomes of diverse patient populations. But unlike in clinical trials, healthcare interventions are usually not administered randomly in real-world clinical practice. This makes it much more difficult to draw conclusions about the impact of interventions on outcomes. For example, a last-line cancer treatment may be administered to only the most severely ill patients; as such, comparing these patients with those given other therapies may result in a misleading picture of the last-line treatment’s benefits.
PHE studies often start from a customized, thoughtful consideration of the potential sources of bias present in real-world data and the ability to control for these sources of bias using observed data elements. PHE leverages the expertise of its staff of diverse experts and academic consultants to identify the best analytical approaches for each challenge, drawing from cutting-edge techniques in economics and other disciplines.
Analytic approaches such as regression analysis and propensity score matching can control for differences in observed characteristics. Further, PHE has used quasi-experimental approaches to simulate the randomization of a clinical trial to draw conclusions about the causal impact of treatments and policy changes on outcomes. Such quasi-experimental techniques are useful for controlling for unobserved sources of bias, which could otherwise confound the analysis and lead to faulty conclusions.
PHE uses a wide range of real-world retrospective databases to conduct analyses. In some cases, multiple data sources are combined to improve the validity of the analysis findings.
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