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Effect of metabolic syndrome or its components on cardiovascular disease events – Maintaining statistical power
diabetes research and clinical practice 102 (2013) e21
Contents available at ScienceDirect
Diabetes Research and Clinical Practice journ al h ome pa ge : www .elsevier.co m/lo cate/diabres
Letter to the Editor Effect of metabolic syndrome or its components on cardiovascular disease events – Maintaining statistical power
Their statistical procedure was the result of a lack of sufficient CVD events, and I recommend the authors continue their follow-up study to increase the number of CVD events. Their study limitation could also be solved by the enlargement of baseline data.
Keywords: Metabolic syndrome Cardiovascular disease
Disclosure statement
Number of events Adjustment
The author has indicated no financial support.
To the Editor,
Conflict of interest
I read with interest Luke and colleagues’ report of the predictive ability of several definitions of the metabolic syndrome for cardiovascular disease [1]. They also selected each metabolic component as independent variables. They conducted a 10-year follow-up study in 636 Aboriginal people aged 15–82. Among them, they detected 66 cardiovascular disease (CVD) events. CVD events were composed of coronary heart disease, stroke, peripheral vascular disease, or chronic heart failure. I have some concerns about the statistical procedures in their study. First, they did not select lifestyle habits such as smoking, drinking or physical activity as independent variables. Smoking status is a risk factor for CVD in many epidemiological studies and classic risk factors should be included in their analysis. In relation to the selection of independent variables, Peduzzi and colleagues reported that the number of events per independent variable in Cox regression analysis should be 10 or more to keep statistical power [2,3]. Luke and colleagues presented hazard ratios (HRs) for each dependent variable after adjusting for age and gender, but metabolic components such as hyperglycaemia, hypertension, dyslipidaemia, and obesity were not used for the adjustment. As the number of CVD events was 66, the number of independent variables is limited to 6. Presumably they could not use several important factors for the adjustment of HR in their study. Second, they could not conduct their sub-analysis by stratifying CVD. They defined CVD as coronary heart disease, stroke, peripheral vascular disease, or chronic heart failure, but these four diseases do not have the same risk factors for their occurrence. Mortality and morbidity were also handled as events with no differentiation. These procedures can interfere with risk assessment for CVD.
The author declares that he has no conflict of interest with the article.
references
[1] Luke JN, Brown A, Daniel M, O’Dea K, Best JD, Jenkins AJ, et al. The metabolic syndrome and CVD outcomes for a central Australian cohort. Diabetes Res Clin Pract 2013;100:e70–3. [2] Concato J, Peduzzi P, Holford TR, Feinstein AR. Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol 1995;48:1495–501. [3] Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 1995;48:1503–10.
Tomoyuki Kawada* Department of Hygiene and Public Health, Nippon Medical School, Japan *Corresponding author at: Department of Hygiene and Public Health, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-Ku, Tokyo 113-8602, Japan. Tel.: +81 3 3822 2131; fax: +81 3 5685 3065 E-mail address: [email protected] (T. Kawada) 16 June 2013 Accepted 5 August 2013 0168-8227/$ – see front matter # 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.diabres.2013.08.012
Contents available at ScienceDirect
Diabetes Research and Clinical Practice journ al h ome pa ge : www .elsevier.co m/lo cate/diabres
Letter to the Editor Effect of metabolic syndrome or its components on cardiovascular disease events – Maintaining statistical power
Their statistical procedure was the result of a lack of sufficient CVD events, and I recommend the authors continue their follow-up study to increase the number of CVD events. Their study limitation could also be solved by the enlargement of baseline data.
Keywords: Metabolic syndrome Cardiovascular disease
Disclosure statement
Number of events Adjustment
The author has indicated no financial support.
To the Editor,
Conflict of interest
I read with interest Luke and colleagues’ report of the predictive ability of several definitions of the metabolic syndrome for cardiovascular disease [1]. They also selected each metabolic component as independent variables. They conducted a 10-year follow-up study in 636 Aboriginal people aged 15–82. Among them, they detected 66 cardiovascular disease (CVD) events. CVD events were composed of coronary heart disease, stroke, peripheral vascular disease, or chronic heart failure. I have some concerns about the statistical procedures in their study. First, they did not select lifestyle habits such as smoking, drinking or physical activity as independent variables. Smoking status is a risk factor for CVD in many epidemiological studies and classic risk factors should be included in their analysis. In relation to the selection of independent variables, Peduzzi and colleagues reported that the number of events per independent variable in Cox regression analysis should be 10 or more to keep statistical power [2,3]. Luke and colleagues presented hazard ratios (HRs) for each dependent variable after adjusting for age and gender, but metabolic components such as hyperglycaemia, hypertension, dyslipidaemia, and obesity were not used for the adjustment. As the number of CVD events was 66, the number of independent variables is limited to 6. Presumably they could not use several important factors for the adjustment of HR in their study. Second, they could not conduct their sub-analysis by stratifying CVD. They defined CVD as coronary heart disease, stroke, peripheral vascular disease, or chronic heart failure, but these four diseases do not have the same risk factors for their occurrence. Mortality and morbidity were also handled as events with no differentiation. These procedures can interfere with risk assessment for CVD.
The author declares that he has no conflict of interest with the article.
references
[1] Luke JN, Brown A, Daniel M, O’Dea K, Best JD, Jenkins AJ, et al. The metabolic syndrome and CVD outcomes for a central Australian cohort. Diabetes Res Clin Pract 2013;100:e70–3. [2] Concato J, Peduzzi P, Holford TR, Feinstein AR. Importance of events per independent variable in proportional hazards analysis. I. Background, goals, and general strategy. J Clin Epidemiol 1995;48:1495–501. [3] Peduzzi P, Concato J, Feinstein AR, Holford TR. Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 1995;48:1503–10.
Tomoyuki Kawada* Department of Hygiene and Public Health, Nippon Medical School, Japan *Corresponding author at: Department of Hygiene and Public Health, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-Ku, Tokyo 113-8602, Japan. Tel.: +81 3 3822 2131; fax: +81 3 5685 3065 E-mail address: [email protected] (T. Kawada) 16 June 2013 Accepted 5 August 2013 0168-8227/$ – see front matter # 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.diabres.2013.08.012