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Calculation of absolute risk for important outcomes in patients with and without a prognostic factor of interest

  • Farid Foroutan
    Correspondence
    Corresponding author: Ted Rogers Centre for Heart Research, Multi-Organ Transplant Program, Toronto General Hospital, University Health Network, 11 PMB 137; 585 University Ave, ON, Toronto, Ontario M5G 2C4, Canada. Tel.: +(416) 340 – 3111 #7923; fax: 416-340-4134.
    Affiliations
    Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada

    Ted Rogers Centre for Heart Research, Multi-Organ Transplant Program, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
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  • Alfonso Iorio
    Affiliations
    Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada

    Department of Medicine, McMaster University, Hamilton, Ontario, Canada
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  • Lehana Thabane
    Affiliations
    Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada

    Biostatistics Unit, St Joseph's Healthcare, Hamilton, Ontario, Canada
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  • Gordon Guyatt
    Affiliations
    Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada

    Department of Medicine, McMaster University, Hamilton, Ontario, Canada
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      Abstract

      Background and Objectives

      Primary studies and systematic reviews of prognostic factors commonly analyze and report relative measures of association between the factor(s) and outcome(s) of interest. For decision making, however, guideline panelists, systematic reviewers, and health care professionals at the point of care will ultimately need the absolute risk of the outcome(s) in those with and without the prognostic factor(s) of interest. The objective of the study was to develop a framework for calculating the absolute risk of the outcome(s) in those with and without the prognostic factor(s) of interest.

      Methods

      We developed a mathematical approach to calculate the absolute risk of events from the relative measure of association, the total number of events and patients at risk, and the prevalence of the prognostic factor, all of which are usually reported in cohort studies assessing prognostic factors. We demonstrate how simpler approximations lead to biased estimates of absolute risk and thus the need for these formulas. We explain our logical framework using the simplest case, in which the measure of association is a relative risk, and provide extensions of the formula to odds ratios and hazard ratios. The same formulas can be applied to reports providing only the relative measure of association (e.g., case-control studies) by using external evidence regarding prevalence of the prognostic factor and overall risk of events.

      Conclusions

      Our proposed formulas facilitate accurate calculation of measures of absolute risk in those with and without prognostic factors of interest for studies reporting the total number of events and patients at risk, the prevalence of the prognostic factor and a relative risk, odds ratio, or hazard ratio.

      Keywords

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