Mount Sinai Paper Corrects Flaw in Most Common Way Health Care Researchers Statistically Adjust Their Findings
Mount Sinai paper describes an improved approach to data analysis that will reduce the potential for miscommunication about the size of an effect.
Lawrence C. Kleinman, MD, MPH, Vice Chair for Research and Education and Associate Professor of Health Policy and Pediatrics at Mount Sinai School of Medicine, has authored a paper that corrects the major flaw in the most common way that health care researchers statistically adjust their findings. The paper, which includes Edward C. Norton, PhD, a health economist at the University of Michigan, as co-author, is being published in the current (February) edition of Health Services Research.
The paper titled, What’s the Risk? A Simple Approach for Estimating Risk Measures from Nonlinear Models Including Logistic Regression, describes an improved approach to data analysis that will reduce the potential for miscommunication about how large an effect is, when researchers present their data to one another and to the lay public. The paper validates this approach and demonstrates shortcomings of current methods.
Frequently, researchers want to describe the impact of one thing on another, independent of other factors. For example, they may want to measure the impact of second hand smoke on admissions to the hospital, independent of other factors, such as age, race, and insurance type and whether or not the individuals live in a city. The most common way that researchers adjust for these factors is called logistic regression. Almost always, the results of these logistic regressions are presented as adjusted odds ratio.
The word adjusted means that the other factors were taken into account, said Dr. Kleinman. "An odds ratio is the odds of being hospitalized if you were exposed, in this example, to second hand smoke, divided by the odds of being hospitalized if you were not exposed."
The problem is that people often misunderstand odds and think in terms of risk (or percent chance). They often end up interpreting odds ratios as if they were risk ratios. Unfortunately odds ratios are often much larger than the adjusted risk ratios.
When a paper reports a forty percent reduction in hospitalizations by avoiding second hand smoke, they are almost always reporting a reduction in odds, not risk. When reported with odds, the size of the effect will always be larger than if you report a risk ratio. If only one percent of people exposed to second hand smoke are hospitalized, the odds ratio and risk ratio will be pretty similar. But if 10 or 20 percent are hospitalized, the odds and risk ratio will be very different.
It’s as if you knew that someone had put their thumb on the scale, but you had no idea how hard they were pressing, Dr. Kleinman noted. "This method will enhance the capacity for researchers to communicate their findings with one another and with the lay public. It can help the press to avoid errors or misinterpretations that heretofore have been common, and at times serious. It can also help patients and their doctors to make more informed decisions." Dr. Kleinman notes that his article also demonstrates the bias intrinsic in other methods used to try to calculate a risk ratio from an odds ratio. "We are getting requests for our computer code from researchers all over North America," he added.
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