Studies about medications published in the most influential medical journals are frequently designed in a way that yields misleading or confusing results, according to a UCLA Health release.
Investigators from UCLA and Harvard analyzed randomized medication trials published in the six highest-impact general medicine journals between June 1, 2008-Sept. 30, 2010. They sought to determine the prevalence of three types of outcome measures that make data interpretation difficult: surrogate outcomes, composite outcomes and disease-specific mortality.
They also reviewed abstracts to determine how many used relative rather than absolute numbers, which can also be a misleading.
Results showed 37 percent of studies used surrogate outcomes, which refer to intermediate markers, such as a heart medication's ability to lower blood pressure, that which may not be a good indicator of the medication's impact on more important clinical outcomes, like heart attacks.
Additionally, 34 percent of studies used composite outcomes, which consist of multiple individual outcomes of unequal importance lumped together, making it difficult to understand the effects on each outcome individually.
Lastly, 27 percent of studies relied on disease-specific mortality, which measures deaths from a specific cause rather than from any cause. This may be a misleading measure because, even if a given treatment reduces one type of death, it could increase the risk of dying from another cause to an equal or greater extent.
The researchers also suggest that commercial sponsors of research may promote the use of outcomes that are most likely to indicate favorable results for their products. The new study also showed 44 percent of study abstracts reported study results exclusively in relative — rather than absolute — numbers.
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Investigators from UCLA and Harvard analyzed randomized medication trials published in the six highest-impact general medicine journals between June 1, 2008-Sept. 30, 2010. They sought to determine the prevalence of three types of outcome measures that make data interpretation difficult: surrogate outcomes, composite outcomes and disease-specific mortality.
They also reviewed abstracts to determine how many used relative rather than absolute numbers, which can also be a misleading.
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Results showed 37 percent of studies used surrogate outcomes, which refer to intermediate markers, such as a heart medication's ability to lower blood pressure, that which may not be a good indicator of the medication's impact on more important clinical outcomes, like heart attacks.
Additionally, 34 percent of studies used composite outcomes, which consist of multiple individual outcomes of unequal importance lumped together, making it difficult to understand the effects on each outcome individually.
Lastly, 27 percent of studies relied on disease-specific mortality, which measures deaths from a specific cause rather than from any cause. This may be a misleading measure because, even if a given treatment reduces one type of death, it could increase the risk of dying from another cause to an equal or greater extent.
The researchers also suggest that commercial sponsors of research may promote the use of outcomes that are most likely to indicate favorable results for their products. The new study also showed 44 percent of study abstracts reported study results exclusively in relative — rather than absolute — numbers.
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