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A Bayesian analysis of non-significant rehabilitation findings: Evaluating the evidence in favour of truly absent treatment effects - 29/07/21

Doi : 10.1016/j.rehab.2020.07.008 
Adam R. Kinney a, b, , Addie Middleton c, James E. Graham d
a Department of Veterans Affairs, Rocky Mountain Mental Illness Research, Education and Clinical Center (MIRECC), Aurora, CO, USA 
b Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, CO, USA 
c New England Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, MA, USA 
d Department of Occupational Therapy, Colorado State University, Fort Collins, CO, USA 

Corresponding author. Department of Veterans Affairs, Polytrauma/TBI Advanced Postdoctoral Fellow, Rocky Mountain MIRECC, 1700 N Wheeling street, 80045 Aurora, CO, USA.Department of Veterans Affairs, Polytrauma/TBI Advanced Postdoctoral Fellow, Rocky Mountain MIRECC1700 N Wheeling streetAurora, CO80045USA

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Abstract

Background

Relying solely on null hypothesis significance testing (NHST) to investigate rehabilitation interventions may result in researchers erroneously concluding the absence of a treatment effect.

Objective

We aimed to distinguish between truly null treatment effects and data that are insensitive to detecting treatment effects by calculating Bayes factors (BF01s) for non-significant findings in the rehabilitation literature. Additionally, to examine associations between BF01, sample size, and observed P-values.

Method

We searched the Cochrane Database of Systematic Reviews for meta-analyses with “rehabilitation” as a keyword that clearly evaluated a rehabilitation intervention. We extracted means, standard deviations, and sample sizes for treatment and comparison groups for individual findings within 175 meta-analyses. Two independent investigators classified the interventions into 4 categories using the Rehabilitation Treatment Specification System. We calculated t-statistics and associated P-values for each finding in order to extract non-significant results (P>0.05). We calculated BF01s for 5790 non-significant results and classified BF01s based on the strength of evidence in favour of the null hypothesis (i.e., anecdotal, moderate, and strong) across and within intervention types. We examined correlations between BF01, sample size, and P-values across and within intervention types.

Results

Across all intervention types, most (71.9%) findings were deemed anecdotal, and this pattern remained within distinct intervention types (58.4–76.0%). Larger sample sizes tended to be associated with greater strength in favour of the null hypothesis, both across and within intervention types. Larger P-values were not associated with greater strength in favour of the null hypothesis; this finding was present both across and within intervention types.

Conclusion

Our findings indicate that most non-significant rehabilitation findings are unable to distinguish between the true absence of a treatment effect and data that are merely insensitive to detecting a treatment effect. Findings also suggest that rehabilitation researchers may improve the strength of their statistical conclusions by increasing sample size and that Bayes factors may offer unique benefits relative to P-values.

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Keywords : Bayes factor, Statistical power, Null hypothesis significance testing, Meta-research, Meta-analysis, Bayesian analysis


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Vol 64 - N° 4

Article 101425- juillet 2021 Retour au numéro
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