• The template for this display is not available. Please contact a Site administrator.

Details for Interpreting null findings from trials of alcohol brief interventions

Name:Interpreting null findings from trials of alcohol brief interventions

Full title: Interpreting null findings from trials of alcohol brief interventions

By Nick Heather

The effectiveness of alcohol brief intervention (ABI) has been established by a succession of meta-analyses but, because the effects of ABI are small, null findings from randomized controlled trials are often reported and can sometimes lead to skepticism regarding the benefits of ABI in routine practice. This article first explains why null findings are likely to occur under null hypothesis significance testing (NHST) due to the phenomenon known as “the dance of the p-values.” A number of misconceptions about null findings are then described, using as an example the way in which the results of the primary care arm of a recent cluster-randomized trial of ABI in England (the SIPS project) have been misunderstood. These misinterpretations include the fallacy of “proving the null hypothesis” that lack of a significant difference between the means of sample groups can be taken as evidence of no difference between their population means, and the possible effects of this and related misunderstandings of the SIPS findings are examined. The mistaken inference that reductions in alcohol consumption seen in control groups from baseline to follow-up are evidence of real effects of control group procedures is then discussed and other possible reasons for such reductions, including regression to the mean, research participation effects, historical trends, and assessment reactivity, are described. From the standpoint of scientific progress, the chief problem about null findings under the conventional NHST approach is that it is not possible to distinguish “evidence of absence” from “absence of evidence.” By contrast, under a Bayesian approach, such a distinction is possible and it is explained how this approach could classify ABIs in particular settings or among particular populations as either truly ineffective or as of unknown effectiveness, thus accelerating progress in the field of ABI research.

Filename:Link to abstract
Filesize: Unknown
Filetype:00085/abstract (Mime Type: link)
Created On: 11/27/2014 15:04
Maintained by:goretti
Hits:3715 Hits
Last updated on: 12/04/2014 12:51
CRC Checksum:
MD5 Checksum: