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Abstract
Objective: A recent study suggested that placebo effects are a source of bias in non-blinded hearing-aid trials. Given the potential impact of this finding on the interpretation of non-blinded trials and design of future research trials, the objective of the present study was to investigate the reliability of this effect. Design: Using the same procedure as an earlier study, participants were told that they were taking part in a trial of new hearing-aid technology. Participants compared two devices that were acoustically identical, except one was described as “new” and the other as “conventional”. Participants completed a speech-in-noise test, sound quality ratings, and rated overall personal preference for both hearing aids. Study sample: Sixteen adult hearing-aid users. Results: Participants had significantly better mean speech-in-noise performance (70.9% versus 66.8%, Z = 2.30, p = 0.02, effect size Pearson's r = 0.15) and sound quality ratings for the “new” hearing aid (8.1 versus 7.4, Z = − 2.99, p = 0.003, r = 0.28). A significant proportion of participants (75%) expressed an overall preference for the “new” hearing aid (p = 0.001, effect size φc = 0.66). Conclusion: Placebo effects reliably impact on hearing-aid trials. In order to control for placebo effects, double-blind methodology is optimal. However, when double-blinding is not possible other strategies may be appropriate.
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Acknowledgements
The authors thank the patients and staff at the Audiology Department of Withington Community Hospital, South Manchester, for their support in this research. The authors also thank Keith Wilbraham for technical support and Starkey Laboratories Ltd (UK) for providing the hearing instruments and funding for participant transport. This research was presented at the British Society of Audiology annual conference, September 2012, Nottingham, UK.
Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
Notes
1. Double-blind studies are those where neither the participant nor the experimenter is aware which the experimental condition is, and thus control for the possible affect of expectation.
2. The absolute magnitude of Pearson's r varies between 0 and 1, with 1 indicating a perfect relation between the two variables and 0 indicating no relation. As a guide, effects of 0.2 are considered ‘small’, 0.5 ‘medium’, and 0.8 ‘large’.
3. Cramér's V is applied to goodness-of-fit chi-squared models when there is a 1xk table (i.e. degrees of freedom are greater than 1). Like the Pearson r statistic, it provides a measure of association between two variables that varies in size between 0 and 1.