USC Researchers Uncover Potential For Bias In Drug Comparison Studies Using Pre-Existing Data

Main Category: Pharmacy / Pharmacist
Article Date: 25 Sep 2008 - 3:00 PDT

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In a recent Value in Health article, researchers from the University of Southern California School of Pharmacy have documented the sensitivity of head-to-head drug comparisons based on existing data to sample selection criteria and missing data.

Over 230,000 episodes of drug therapy were used to document how time-to-all-cause discontinuation (TTAD) differed with the disease states included in the study and the treatment history of the patient. Study authors include Thomas S. Marshall, Jeffrey S. McCombs and Dana Stafkey-Mailey.

According to Dr. McCombs, "Study results can be manipulated by selecting patients known to do better on the study sponsor's drug or by ignoring important patient characteristics. For example, TTAD using antipsychotic drugs is shorter in bipolar disorders relative to schizophrenia. Including both diagnostic groups in a study sample favors antipsychotics used more frequently to treat schizophrenia. Likewise, patients using an antipsychotic as augmentation therapy achieve longer TTAD. Ignoring patient drug treatment history would bias results in favor of antipsychotic drugs used more frequently as augmentation therapy."

McCombs suggests that comparative effectiveness research based on existing data must specify the disease state being treated and should include all patients in the study sample while controlling for drug use history.

Value in Health (ISSN 1098-3015) publishes papers, concepts, and ideas that advance the field of pharmacoeconomics and outcomes research and help health care leaders to make decisions that are solidly evidence-based. The journal is published bi-monthly and has a regular readership of over 4,000 clinicians, decision-makers, and researchers worldwide. ISPOR is a nonprofit, international organization that strives to translate pharmacoeconomics and outcomes research into practice to ensure that society allocates scarce health care resources wisely, fairly, and efficiently.

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