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Cannabis exposure as an interactive cardiovascular risk factor and accelerant of organismal ageing: a longitudinal study
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  • Published on:
    Response to Lane
    • Albert S. Reece, Professor University of Western Australia
    • Other Contributors:
      • Gary K. Hulse, Professor

    We wish to thank Dr Lane for his interest in our study. We are pleased to see statistical input to the issues of cannabis medicine as we feel that sophisticated statistical methodologies have much to offer this field.

    Most of the concerns raised are addressed in our very detailed report. As described our research question was whether, in our sizeable body of evidence (N=13,657 RAPWA studies), we could find evidence for the now well-described cannabis vasculopathy and what such implications might be. As this was the first study of its type to apply formal quantitative measures of vascular stiffness to these questions it was not clear at study outset if there would be any effect, much less an estimate of effect size. In the absence of this information power calculations would be mere guesswork. Nor indeed are they mandatory in an exploratory study of this type. Similarly the primary focus of our work was on whether cannabis exposure was an absolute cardiovascular risk factor in its own right, and how it compared to established risk factors. Hence Table 2 contains our main results. The role of Table 1 is to illustrate the bivariate (uncorrected) comparisons which can be made, show the various groups involved, and compare the matching of the groups. It is not intended to be a springboard for effect-size-power calculations which are of merely esoteric interest. Calculations detailing the observed effect size are clearly described in our text being 11.84% and 8....

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    Conflict of Interest:
    None declared.
  • Published on:
    Re: Cannabis exposure as an interactive cardiovascular risk factor and accelerant of organismal ageing: A longitudinal study
    • Ian A Lane, Clinical & Population Health Researcher University of Massachusetts Medical School

    I have read with interest Reece and colleagues (2016) paper, but some questions remain.

    First, I think the authors have done a commendable job detailing most of their statistical methodology. However, some things are left to be desired, such as a description of the Statistical Power analysis. Using the Benjamini-Hochberg Procedure (a modified Bonferroni Procedure) on the data listed in their Supplementary materials, one can conclude that the authors' results were indeed statistically significant. What remains to be seen, however, is what the magnitude of these effects were, precisely, and what happened to the Power as these multiple comparisons were assessed, since Power decreases with increasing univariate statistical tests. One might assume Power to be sufficient given the N = 1,553, but these data have been parsed in many different ways, and it would be helpful to know the authors’ anticipated effect sizes and any Power analyses for these comparisons that were conducted prior to the start of the study.

    Second, due to issues with boundary conditions and computational modeling, the method used in this paper for the mixed-effects linear model may not be quite right [1, 2]. There is often a misapplication of traditional AIC selection criteria in linear mixed effects (LME) modeling, owing to poor justification for use in longitudinal data analysis, due in part to error variance estimates [2], which is partially how this seemed to have been used here, in th...

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    Conflict of Interest:
    None declared.