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The Law of Attrition Revisited – Author’s Reply

G. Eysenbach

Published 2006 in Journal of Medical Internet Research

ABSTRACT

I do however not agree that focusing on attrition means focusing on the “negative” side of eHealth interventions. To formulate a “law of attrition” was partly motivated by the observation that many authors (the letter writer not included) are not very explicit about high dropout or nonusage rates in their study. Sometimes we have the impression that authors attempt to “hide” high attrition rates, perhaps fearing that reviewers and editors would deem a manuscript unpublishable if too many participants did not use an intervention or drop out from a trial. To explicate a “Law of Attrition” is an attempt to elucidate the fact that high dropout rates and nonusage seem common experiences for eHealth researchers and practitioners, and to encourage them to be forthcoming with such information, enabling them to cite a “law”. Attrition data should not be hidden or buried somewhere in the manuscript, but explicitly stated (already in the abstract) and even better analyzed using multivariate models. Participant characteristics, intervention attributes, as well as external variables need to be incorporated in such models, to analyze and predict events such as dropouts or nonusage. We will not be learning about what works and what does not by concealing such data.

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