Farid Foroutan1, Alfonso Iorio2, Lehana Thabane3, Gordon Guyatt2. 1. 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. Electronic address: farid.foroutan@uhn.ca. 2. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Department of Medicine, McMaster University, Hamilton, Ontario, Canada. 3. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Biostatistics Unit, St Joseph's Healthcare, Hamilton, Ontario, Canada.
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.
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.
Authors: Tayler A Buchan; Crizza Ching; Farid Foroutan; Abdullah Malik; Julian F Daza; Nicholas Ng Fat Hing; Reed Siemieniuk; Nathan Evaniew; Ani Orchanian-Cheff; Heather J Ross; Gordon Guyatt; Ana C Alba Journal: Heart Fail Rev Date: 2021-07-05 Impact factor: 4.214