Moreover, Robins’s statistical wizardry can explain why observational and randomized studies of the same health problem sometimes appear at first glance to yield conflicting results. For example, while results from a number of large observational studies in post-menopausal women have indicated that hormone replacement therapy (HRT) involving estrogen and progesterone can prevent heart attacks, results from the Women’s Health Initiative’s randomized trial found that HRT appears to cause heart attacks.
Similarly, a number of observational analyses of HIV-infected study subjects showed that highly active retroviral therapy (HAART) slowed the rate of their progression to AIDS or death only marginally. A randomized trial, however, revealed a much larger drop in the disease progression rate.
Such disparities have led some scientists to question the reliability and utility of observational trials. Explains Robins: “Usually it’s assumed that observational studies are biased, because they may not account for important, and possibly also unknown, common causal factors.” In the case of both HRT and HAART, however, reanalyzing the observational trials using Robin’s methods has yielded results consistent with the randomized trials.
that so much of research is observational, colleagues say Robins’s
statistical methods could steer biomedical research down a very different
road, influencing not only the precision of their findings but ultimately
also health practices and policies. HSPH Dean Barry R. Bloom foresees
nothing short of a revolution in the way observational studies are analyzed,
predicting that causal inferences derived from observational studies
using Robins’s methods “will
come to be regarded as a new standard, second only to the gold standard
of randomized trials.”
Though Robins’s insights are “respected
at the very highest level of statistical science,” says Jim Ware,
they have not yet caught on universally.
“The field of statistics is currently divided into parametric, nonparametric, and semiparametric schools of thought,” he says, launching into an explanation that quickly moves beyond the average listener’s grasp. “I just have a feeling that there should be one unified story for everything. I think it will allow for more accurate estimates of uncertainty. But I don’t know if it will be useful yet; that’s part of the research.”
Asked to comment on Robins’s chances of succeeding at this latest self-imposed challenge, Jim Ware smiles. “In physics, Einstein spent the latter part of his life trying to develop a unified theory,” he says. “Einstein didn’t actually succeed. But I’m not prepared to say Jamie won’t.”
Elizabeth Gehrman writes about science, medicine, and public health
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