Ashrafi Hafez, Asghar

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  Novelty in Biomedicine       NBM 188   Novelty in Biomedicine 2017, 4, 188-9 Letter to Editor    Dear Editor-in-chief The P-value has no relation to the severity of effectiveness. P-value represents only the odds of observed correlation. So, for example, when its value equals to 0.0001, indicates that 0.0001 is the probability of  observed association might has been due to odds; and compared with P-value = 0.01, it doesn’t indicate greater severity of association. In the studies that evaluate effectiveness of an intervention compared to placebo about binary outcomes, three indices: Absolute Risk Reduction (ARR), Relative Risk Reduction (RRR), and Number Needed to Treat (NNT) -to avoid a bad outcome- are indicating indices of the effectiveness and benefits of treatment. To understand these concepts, let us assume that an article from a randomized controlled clinical trial on the efficacy of the drug “Y” we have in hand. In this article is mentioned that among the 8000 patients with hypertension treated with the drug “X”, 120 cases would experienced myocardial infarction (MI) after five years, and among 4000 patients treated with the drug “Y”, 30 cases of MI would have occurred; In the control group were treated with placebo, 150 MI occurred among 8000 cases. RRR indicates risk reduction ratio of unfortunate outcomes  (here: the risk of MI) in the intervention group versus the control group. To calculate it, difference in MI risk in the intervention group and the control group will be divided into the risk of MIs.  In the above example, the RRR is calculated as Table 1. RRR states that Drug “X” reduces the risk of MI by 20% compared to the control group. And administration of drug “Y” reduces this risk by 60% compared to the control group. A major imperfection of RRR is that cannot show outcome risk in the untreated group (control group) - the basic danger or CER. So it can not differentiate between effect sizes of large amount and small amount.  In contrast, ARR that calculated by subtracting the outcome risk in the control group and intervention group, clearly demonstrates the difference between these states when the baseline risk is high or low. So, it keeps the impact of baseline risk of outcome. For example, for the drug “X” and “Y”, ARR is calculated as table 2. ARR is more meaningful and suitable indicator to measure the effect size than the RRR. On the other hand, to memorize the ARR and what it means for all treatment conditions and the work isn’t very simple; therefore, a quantity called the NNT is defined. It is calculated as 1 diveded by ARR. In fact, NNT tells us that how many patients should be treated with an intended intervention to prevent occurrence of the one unfortunate outcome. RRR and ARR and NNT values for the above example are in the table 3. The 267 patients with hypertension should be treated with drug “X” for 5 years to be prevented of one MI; Table 1:  Risk  in control group Risk in exposure group RRR=  (CER –EER)/CER  150/8000 =1.875% X Y X Y 120/8000 = 1.5% 30/4000 = 0.75% 0.375/1.875 = 20% 1.8/1.875 = 60% EER: Experimental Event Rate   CER: Control Event Rate  Table 2:  Risk  in control group Risk in exposure group ARR=  (CER –EER)  150/8000 =1.875% X Y X Y 120/8000 = 1.5% 30/4000 = 0.75% 1.875-1.5 = 0.375% 1.875-0.75 = 1.125%  Table 3:  Risk in exposure group Risk  in control group RRR ARR NNT X Y  75/4000 =1.875% X Y X Y X Y 1.5% 0.75% 20% 60% 0.375% 1.125% 1/0.00375 = 267 1/0.01125 = 89  Peg-Interferon Alfa 2-b Related Cellulitis in a 40 Years Man                                                                           Sali et al. NBM 189 Novelty in Biomedicine 2017, 4, 188-9 nevertheless, it is 89 patients about drug “Y”. This indicates greater effectiveness of drug “Y” than “X”.  So, NNT is the most appropriate index to determine the effectiveness of an intervention. References 1. Sharon E. Straus, et al; Evidence-Based Medicine: How to Practice and Teach It, 4e (Straus, Evidence-Based Medicine), 2011. 2. Soltani A, et al; Evidence based Medicine. Tehran University of medical sciences Press, 2005. 3. Hill J, Bullock I, Alderson P. A summary of the methods that the National Clinical Guideline Centre uses to produce clinical guidelines for the National Institute for Health and Clinical Excellence. Ann Intern Med. 2011;154:752-7. 4. Kilkenny C, Parsons N, Kadyszewski E, et al.Survey of the quality of experimental design, statistical analysis and reporting of research using animals. PLoS One. 2009;4:7824 5. A-W Chan, F Song, A Vickers, et al. Increasing value and reducing waste: addressing inaccessible research. Lancet (2014) published online Jan 8. http://dx.doi.org/10.1016/S0140-6736(13):62296-5. 6. Paul Glasziou, Douglas G Altman, Patrick Bossuyt, Isabelle Boutron, Mike Clarke, Steven Julious, Susan Michie, David Moher, Elizabeth Wager, Reducing waste from incomplete or unusable reports of biomedical research, The Lancet. 2014;383(9913):18-24.  7. Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370:1453–7. 8. Ioannidis JP. Why most published research findings are false. PLoS Med. 2005;2:e124. TA Lang, M Secic How to report statistics in medicine (2nd edn.)American College of Physicians, Philadelphia. 9. TA Lang, M Secic. How to report statistics in medicine (2nd edn.) American College of Physicians, Philadelphia. 2006. 10. Murad M, Montori VM, Ioannidis JA, et al. How to read a systematic review and meta-analysis and apply the results to patient care: Users’ guides to the medical literature. JAMA. 2014;312(2):171-9. 11. Every‐Palmer S, Howick J. How evidence‐based medicine is failing due to biased trials and selective publication. J Eval Clin Pract. 2014.  12. PM Bossuyt, JB Reitsma, DE Bruns, for the Standards for Reporting of Diagnostic Accuracy, et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ. 2003;326:41–4 13. Turner EH, Matthews AM, Linardatos E, Tell R, Rosenthal R. Selective publication of antidepressant trials and its influence on apparent efficacy. N Engl J Med. 2008;358:252-60. 14. W Bouwmeester, NP Zuithoff, S Mallett, et al. Reporting and methods in clinical prediction research: a systematic review. PLoS Med. 2012;9:1–12. 15. Moynihan R, Doust J, Henry D. Preventing overdiagnosis: how to stop harming the healthy. BMJ. 2012;344:e3502. 16. D Fanelli. How many scientists fabricate and falsify research? A systematic review and meta-analysis of survey data. PLoS One. 2009;4:5738. 17. RHH Groenwold, AMM Van Deursen, AW Hoes, E Hak. Poor quality of reporting confounding bias in observational intervention studies: a systematic review. Ann Epidemiol. 2008;18:746–51.    Asghar Ashrafi Hafez MD, Candidate for PhD by Research, Cancer Research Center, Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Email: Ashrafi@sbmu.ac.ir, AshrafiHafez@gmail.com            

Indicating Indices of the effectiveness and benefits of treatment

Asghar Ashrafi Hafez

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Indicating Indices of the effectiveness and benefits of treatment

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