Predictive Modeling Needs Drug-Trial Data
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Opinion: Predictive modeling can be a force for the common good, and it requires vast volumes of data—some of it proprietary—in order to be effective.
Hospitals would like to peer into the future, and the Centers for Disease Control and Prevention is making these glimpses possible, in a sense. It has developed software programs that help hospitals and other health care agencies predict how many staff, beds and supplies a hospital will need during a flu pandemic. Though the programs were designed for U.S. health planners, a senior health economist at the CDC reports that several other countries have used the software in planning their response to an influenza pandemic.
Copies of the programs, called FluAid and FluSurge, are available for free from the Department of Health and Human Services National Vaccine Office.
This software is a straightforward example of how predictive modeling can be a force for the common good. The FDA is also moving into the prediction realm for the benefit of public health, but its route will be convoluted.
Click here to read more about the Critical Path Initiative.
For the Critical Path Initiative, the FDA is collaborating with the industry and actively soliciting its input. But other moves are under way to force drug companies to publicly disclose data from certain late-stage clinical trials publicly.
GlaxoSmithKline and Forest Pharmaceuticals have both been accused of fraud for failing to disclose results showing that their antidepressants were ineffective while publicizing other results showing that they worked.
Around the same time, a group of medical-journal editors threatened not to publish results of clinical trials unless the trial sponsor had ensured both favorable and unfavorable results could be disclosed. The American Medical Association called for the creation of a national database in which drug companies would have to submit clinical-trial results for marketed products.
Patients and physicians want to see trial results to be more confident that they are using the most appropriate drug currently available. And the biomathematicians and biostatisticians who create and use software to model clinical trials say a database of clinical-trial results could be used to predict the effects of combinations of drugs, optimize dosing, and help prove the safety and efficacy of experimental drugs faster.
Click here to read more about these possibilities.
Last week, three Democratic senators asked the FDA and National Institutes of Health what resources—financial and legal—they would need to create a national database of clinical trials, and the World Health Organization said it was investigating plans to create a worldwide version.
Of course, the requirement for publicly disclosed data would only be a boon if drug companies continue to collect that data in clinical trials. Disclosure mandates could encourage drug companies to learn only the bare minimum necessary to get a drug approved for market. After all, drug companies have paid top dollar to test their drug against those of competitors and are not keen to reveal results that favor a competitor.
To truly foster the power of prediction, the government will need not only a mandate to make data public, but also the means to encourage companies to collect data in the first place.
Check out eWEEK.coms Health Care Center at http://healthcare.eweek.com for the latest news, views and analysis of technologys impact on health care. 
