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NetWellness

How to Make Sense of Medical Studies

A pocketful of simple questions may be the best protection consumers can have to cope with the flood of health and medical news that makes headlines every day.

Just how much is there? It's hard to know, but at least one estimate says the presses are spewing out at least 1,000 studies each day. But whatever the number, it leaves a lot of consumers feeling like they are drowning in data.

It is simply impossible to keep up with it all. It's not just the sheer number of studies and stories, which is overwhelming in and of itself, but it's also the appearance of conflicting results among studies examining the same phenomena. Does fiber prevent colon cancer or not? Does a diet rich in fresh fruit and vegetables really affect a woman's risk of breast cancer, or not? Trying to make sense of it all is sort of like mental whiplash.

So, what is a person to do?

For starters, ask a few questions. The answers will help you tell the good studies from the bad ones.

How big is the study? Size matters. Generally, the bigger, the better. A study examining the side effects of a treatment that has 1,000 people in it is going to be more meaningful than one evaluating the same thing in just 10 people.

Where was the study originally published? Good science is usually published in a journal, rather than publicized solely in a press release, presentation or self-published report. Journals have panels of scientists that review and criticize studies before they are published. If studies make it through the review process, publication in well-known journals like the New England Journal of Medicine, Science or Cancer give them even more credibility.

Who paid for the study? Generally, there is less chance that a study funded by the National Institutes of Health will be influenced by any conflict of interest than one funded by a private company. For example, a recent study showed that it is just as important to lower your C-reactive protein level as it is to lower your cholesterol level to reduce the chance of a heart attack. But this study was conducted by a scientist who developed a method to determine the level of C-reactive protein in blood. If it's important to lower C-reactive protein, he could collect royalties on every sample tested.

What is the study design? There are all sorts of designs, but the gold standards are case-control studies in epidemiology and randomized trials in evaluating treatments. In a case-control study, information about cases or people with disease is compared with the same information on controls, or people without the disease. In clinical trials evaluating treatments, participants should be randomly assigned to different groups, given a treatment, and the results compared. The study gets bonus points if neither the clinicians nor the patients know who is getting what, a design referred to as "double-blind." If a study is double-blinded, it helps eliminate possible bias on the part of all participants.

How should I understand findings involving risk? This is a toughie. There are two types of risk: relative risk and absolute risk. They are both important, but different, and sometimes, it is hard to tell which one the author or reporter is talking about. For example, a recent study showed that eating a lot of red meat increased one's risk of rectal cancer by 43 percent, compared to those who ate little red meat. That is relative risk – comparing one group with another. However, of the 148,610 adults in the same study, 470 developed rectal cancer, so their absolute risk is 470 in 148,610, or 0.3 percent – or about 1 in 300. Reporters and writers generally refer to findings in terms of relative risk because it has a bigger "feel" to it. But often, it's the absolute risk that puts findings in better perspective.

As usual, when in doubt, ask the doctor!

This article is based on information provided by The Ohio State University Medical Center Media Relations Office and was adapted for use on NetWellness with permission, 2009.

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Last Reviewed: Apr 03, 2009

Donn Young, PhD
Former Biostatistician and Research Scientist
College of Medicine
The Ohio State University