The Sentinel System for Industrial Use: Strengths and Weaknesses

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Currently, the Sentinel Distributed Database (SDD) is comprised largely of administrative and claims data from health insurers. These large administrative claims databases have become increasingly popular sources of data for comparative safety research (observational studies) because they have several important strengths including a large size, longitudinal data on prescription drug use, generalizability, and low operational costs.

However, use of these claims data is also criticized due to the lack of important information not measured in the claims data such as lifestyle factors, underlying disease severity, over-the-counter medication use, and laboratory or imaging test results. Specifically for the new to market drugs, general concerns could include the potential for residual confounding and selection bias that may impact the interpretability of observational post-marketing studies. Additionally, patients prescribed the new biologic may be sicker than those receiving an existing comparator treatment, or patients may switch from existing treatments to a new one due to poor clinical response or adverse events in the early course of treatment.

Taking into these considerations, there are great challenges for industrial use of Sentinel System data sources. Since SDD comes from different sources with different strengths and limitations, whatever database is selected, it is important for the investigators to understand its limitations. Factors such as whether or not the database contains an appropriate population, captures key variables required to answer the study question, and the time to release the data is reasonable are just some of those to consider. Thus, an experienced study team with appropriate expertise in the selected database is crucial to the successful execution of the study.

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