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Correspondence| Volume 54, ISSUE 1, P126, July 2017

Factors Associated With Lower Extremity Dysmorphia Caused by Lower Extremity Lymphoedema: Comment on Data Sparsity

  • E. Ayubi
    Affiliations
    Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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  • S. Safiri
    Correspondence
    Corresponding author. Managerial Epidemiology Research Centre, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran.
    Affiliations
    Managerial Epidemiology Research Centre, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran
    Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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Open ArchivePublished:June 03, 2017DOI:https://doi.org/10.1016/j.ejvs.2017.04.023
      We read the article by Yamamoto et al. with great interest.
      • Yamamoto T.
      • Yamamoto N.
      • Yoshimatsu H.
      • Narushima M.
      • Koshima I.
      Factors associated with lower extremity dysmorphia caused by lower extremity lymphoedema.
      The authors aimed to examine independent factors associated with lower extremity dysmorphia (LED) in patients with secondary lower extremity lymphoedema. They found that the odds of LED were considerably higher among patients with stage IV leg dermal backflow (LDB) compared with those with stage 0 LDB (crude odds ratio [OR] 43.00, 95% confidence interval [CI] 4.92–375.76; adjusted OR 76.79, 95% CI 8.13–725.20), which is questionable. The authors believe that the very large effect size estimate, such as OR with considerably wide CI, can be yielded from sparse data.
      • Greenland S.
      • Mansournia M.A.
      Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions.
      • Greenland S.
      • Mansournia M.A.
      • Altman D.G.
      Sparse data bias: a problem hiding in plain sight.
      Sparse data means that there are not enough observations in the different combinations of the exposure and outcome variables in the studied data.
      • Greenland S.
      • Mansournia M.A.
      • Altman D.G.
      Sparse data bias: a problem hiding in plain sight.
      We tabulated the associations between LDB stage IV and LED (nStage IV, LED+ = 43; nStage IV, LED– = 10; nStage 0, LED+ = 1; nStage 0, LED– = 10) and found that data sparsity is expected.
      • Yamamoto T.
      • Yamamoto N.
      • Yoshimatsu H.
      • Narushima M.
      • Koshima I.
      Factors associated with lower extremity dysmorphia caused by lower extremity lymphoedema.
      Hence, the authors' crude and adjusted ORs could be biased owing to the sparse data bias and should be decreased using the appropriate statistical methods.
      • Greenland S.
      • Mansournia M.A.
      Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions.
      • Greenland S.
      • Mansournia M.A.
      • Altman D.G.
      Sparse data bias: a problem hiding in plain sight.
      • Ayubi E.
      • Safiri S.
      • Mansournia M.A.
      Increased risk of thromboembolic events in adult congenital heart disease patients with atrial tachyarrhythmias: Bias due to the data sparsity.
      • Safiri S.
      • Ayubi E.
      Factors associated with thrombotic complications in pediatric patients with vascular malformations: Methodological issue.
      We used one of the effective methods to decrease this aforementioned bias, namely penalisation via data augmentation using the log-f (3.9, 3.9) prior distributions.
      • Greenland S.
      • Mansournia M.A.
      • Altman D.G.
      Sparse data bias: a problem hiding in plain sight.
      It was found that the penalised crude OR and its CI shrank and narrowed, respectively (OR 11.30; 95% CI 2.76–46.29). Hence, we respectfully suggest that the authors re-estimate their adjusted ORs on the association between LDB stage IV and LED in order to obtained unbiased ORs.

      References

        • Yamamoto T.
        • Yamamoto N.
        • Yoshimatsu H.
        • Narushima M.
        • Koshima I.
        Factors associated with lower extremity dysmorphia caused by lower extremity lymphoedema.
        Eur J Vasc Endovascular Surg. 2017; 54: 69-77
        • Greenland S.
        • Mansournia M.A.
        Penalization, bias reduction, and default priors in logistic and related categorical and survival regressions.
        Stat Med. 2015; 34: 3133-3143
        • Greenland S.
        • Mansournia M.A.
        • Altman D.G.
        Sparse data bias: a problem hiding in plain sight.
        BMJ. 2016; 352: i1981
        • Ayubi E.
        • Safiri S.
        • Mansournia M.A.
        Increased risk of thromboembolic events in adult congenital heart disease patients with atrial tachyarrhythmias: Bias due to the data sparsity.
        Int J Cardiol. 2017; 239: 20
        • Safiri S.
        • Ayubi E.
        Factors associated with thrombotic complications in pediatric patients with vascular malformations: Methodological issue.
        J Pediatr Surg. 2017; (In-Press)

      Linked Article

      • Factors Associated with Lower Extremity Dysmorphia Caused by Lower Extremity Lymphoedema
        European Journal of Vascular and Endovascular SurgeryVol. 54Issue 1
        • Preview
          Indocyanine green (ICG) lymphography has been reported to be useful for the early diagnosis of lymphoedema. However, no study has reported the usefulness of ICG lymphography for evaluation of lymphoedema with lower extremity dysmorphia (LED). This study aimed to elucidate independent factors associated with LED in secondary lower extremity lymphoedema (LEL) patients.
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