Reporting Bias Inflates the Reputation of Medical Treatments: A Comparison of Outcomes in Clinical Trials and Online Product Reviews

Mícheál De Barra

Research output: Contribution to journalArticle

8 Citations (Scopus)
4 Downloads (Pure)



People often hold unduly positive expectations about the outcomes of medicines and other healthcare products. Here the following explanation is tested: people who have a positive outcome tend to tell more people about their disease/treatment than people with poor or average outcomes. Akin to the file drawer problem in science, this systematically and positively distorts the information available to others.


If people with good treatment outcomes are more inclined to tell others, then they should also be more inclined to write online medical product reviews. Therefore, average treatment outcomes in these reviews should be more positive than those found in randomised controlled trials (RCTs). Data on duration of treatment and outcome (i.e., weight/cholesterol change) were extracted from user-generated health product reviews on and compared to RCT data for the same treatments using ANOVA. The sample included 1675 reviews of cholesterol reduction (Benecol, CholestOff) and weight loss (Orlistat) treatments and the primary outcome was cholesterol change (Bencol and CholestOff) or weight change (Orlistat).


In three independent tests, average outcomes reported in the reviews were substantially more positive than the outcomes reported in the medical literature (η2 = 0.01 to 0.06; p = 0.04 to 0.001). For example, average cholesterol change following use of Benecol is −14 mg/dl in RCTs and −45 mg/dl in online reviews.


People with good treatment outcomes are more inclined to share information about their treatment, which distorts the information available to others. People who rely on word of mouth reputation, electronic or real life, are likely to develop unduly positive expectations.
Original languageEnglish
Pages (from-to)248-255
Number of pages8
JournalSocial Science & Medicine
Early online date10 Feb 2017
Publication statusPublished - Mar 2017



  • health informatics
  • eHealth
  • medical overuse
  • word of mouth
  • cultural evolution

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