Risk framing alters perception of benefit; doctors just as prone as patients, study finds

How risk information is posed influences perceptions of treatment benefit, with absolute survival rates creating the perception of weakest benefit and relative mortality reduction creating the perception of greatest benefit, researchers found in a recent study.

Comprehensive information that combines absolute mortality, absolute survival, and relative mortality reduction produces the most accurate decisions. Surprisingly, the study found that framing bias was similar in doctors and patients.

To determine which risk framing format best conveys information, and to compare framing bias in doctors and in patients, researchers mailed 1,431 randomized surveys to every doctor in Geneva, Switzerland (56% response rate), and to 1,121 recently hospitalized patients (65% response rate).

Respondents were asked to interpret the results of a hypothetical clinical trial comparing an old drug to a new one. They were randomly assigned to framing formats of absolute survival (96% for the new drug vs. 94% for the old drug), absolute mortality (4% vs. 6%), relative mortality reduction (reduction by a third) or all three (fully informed condition). Results were published online July 27 by the Journal of General Internal Medicine.

The risk presentation format influenced whether doctors rated the new drug as more effective (absolute survival, 51.8%; absolute mortality, 68.3%; relative mortality reduction, 93.8%; and fully informed condition, 69.8%; P<0.001). These proportions were similar in patients (absolute survival, 51.7%; absolute mortality, 66.8%; relative mortality reduction, 89.3%; and fully informed condition, 71.2%; P<0.001). None of the differences between doctors and patients were significant (all P>0.1).

The fully informed condition was similar to the absolute risk format for both doctors (P=0.72) and patients (P=0.23), but it differed significantly from the other conditions (all P<0.01). In comparison to the fully informed condition, the odds ratio of greater perceived effectiveness was 0.45 for absolute survival (P<0.001), 0.89 for absolute mortality (P=0.29), and 4.40 for relative mortality reduction (P<0.001).

Absolute risks constitute the least biased risk format, the authors concluded. In contrast, relative risk reductions create an optimistic bias of a more than fourfold increase in the odds of a positive assessment of the new treatment. Absolute survival proportions caused a pessimistic bias, with a more than twofold decrease in the odds of a favorable assessment.

That doctors and patients had similar vulnerabilities to framing bias was unexpected, the authors wrote: “[W]e thought that doctors would be more sophisticated than patients in interpreting the scenario, less likely to be convinced by a relative mortality reduction with no absolute risk to anchor the comparison, and more apt to deduce the proportion of patients who died when the proportion who survived was given. This illustrates the difficulty that many doctors have in applying quantitative analysis skills in their practice.”

The authors continued that doctors' understanding of various terms used in medical literature, such as relative risk, absolute risk, or the number needed to treat, does not translate to forming an objective, criterion-based assessment. Most doctors, they wrote, misunderstand numerical data about test accuracy, regardless of whether they are presented as sensitivity and specificity or likelihood ratios, and fail to use relevant numerical information, such as disease prevalence, when they interpret diagnostic test results.

The solution, the authors concluded, is to present risk and benefit information in absolute and relative scales and to report absolute risks in medical research reports and other original sources of medical information used by doctors.