In a Lancet Microbe Comment, Piero Olliaro and colleagues suggest that reporting relative risk reduction (RRR) for vaccination does not reflect entirely its therapeutic performance and consider the solw use of RRR a reporting bias. In addition, they propose that absolute risk reduction (ARR) should be reported as a measure of the vaccine's effectiveness. The authors end up comparing the numbers needed to vaccinate to prevent one case of COVID-19 among the vaccines, which derives from the absolute reductions.However, this suggestion might have a paradoxical effect in misleading perception of treatment performance. This approach disregards three epidemiological facts.First, number needed to treat (NNT) is not an intrinsic property of a treatment, it is rather a property of the population that receives a treatment: for a constant relative risk reduction, populations of different baseline risks will have different absolute reductions. Therefore, NNT comparison of different treatments across studies should be avoided, because sample populations will always have baseline risk variations. Indeed, this approach is the actual reporting bias.Second, the authors raise a concern that different levels of background risk might change relative risk reduction of studies. This statement disregards the constant property of relative risk repeatedly demonstrated by subgroup analysis of clinical trials and meta-scientific evaluations of a treatment across studies of different baseline risks. For example, statins,3, 4 anti-hypertensive therapy, and aspirin have the same relative risk reduction across the baseline risks of primary or secondary prevention.Finally, effectiveness—a real-world property—is about clinical decision making, and not to be derived from efficacy studies (randomised controlled studies). As a clinician or an epidemiologist, one should multiply the RRR (intrinsic property of a treatment) by the baseline risk of a given population or patient, individualising the ARR and NNT. They are not scientific concepts, they are circumstantial information.We declare no competing interests.
Authors: Robert A Phillips; Jiaqiong Xu; Leif E Peterson; Ryan M Arnold; Joseph A Diamond; Adam E Schussheim Journal: J Am Coll Cardiol Date: 2018-03-07 Impact factor: 24.094
Authors: Salim Yusuf; Jackie Bosch; Gilles Dagenais; Jun Zhu; Denis Xavier; Lisheng Liu; Prem Pais; Patricio López-Jaramillo; Lawrence A Leiter; Antonio Dans; Alvaro Avezum; Leopoldo S Piegas; Alexander Parkhomenko; Katalin Keltai; Matyas Keltai; Karen Sliwa; Ron J G Peters; Claes Held; Irina Chazova; Khalid Yusoff; Basil S Lewis; Petr Jansky; Kamlesh Khunti; William D Toff; Christopher M Reid; John Varigos; Gregorio Sanchez-Vallejo; Robert McKelvie; Janice Pogue; Hyejung Jung; Peggy Gao; Rafael Diaz; Eva Lonn Journal: N Engl J Med Date: 2016-04-02 Impact factor: 91.245
Authors: Colin Baigent; Lisa Blackwell; Rory Collins; Jonathan Emberson; Jon Godwin; Richard Peto; Julie Buring; Charles Hennekens; Patricia Kearney; Tom Meade; Carlo Patrono; Maria Carla Roncaglioni; Alberto Zanchetti Journal: Lancet Date: 2009-05-30 Impact factor: 79.321