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Practising evidence-based medicine in an era of high placebo response: number needed to treat reconsidered

  • Steven P. Roose (a1), Bret R. Rutherford (a1), Melanie M. Wall (a1) and Michael E. Thase (a2)

Summary

The number needed to treat (NNT) statistic was developed to facilitate the practice of evidence-based medicine. Placebo was assumed to be therapeutically inert when the NNT was originally conceived, but more recent data for conditions such as major depressive disorder (MDD) suggest that the placebo control condition can have considerable therapeutic effects. Complications arise because the NNT calculated from randomised controlled trials (RCTs) reflects a comparison between medication plus clinical management and placebo plus clinical management, whereas, in the clinical setting, physicians choose between prescribing open medication, observing a patient over time with a supportive approach, and doing nothing. Thus, NNTs derived from clinical trials are not directly relevant to clinical decision-making, because they are based on control conditions that do not exist in standard practice. Additional difficulties may arise when using NNTs to compare alternative treatments for MDD, such as medication and psychotherapy, since these comparisons require the control conditions upon which the respective NNTs are based to be similar. Whereas pill placebo conditions include intensive clinical management and elicit expectations of improvement, attention control conditions for psychotherapy research are less well developed. Often the effects of psychotherapy are gauged against a wait-list control condition, which has substantially fewer therapeutic components than a pill placebo control condition. To improve the clinical utility of NNTs for the treatment of MDD, we advocate effectiveness studies that include treatment conditions resembling actual clinical practice, rather than using placebo-controlled RCTs for this purpose. Until such studies are performed, the effect of bias in comparing NNTs across treatments can be controlled by ensuring that the RCT control conditions upon which the NNTs are based are comparable.

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Copyright

Corresponding author

Dr Steven P. Roose, Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, 1051 Riverside Drive, Box 98, New York, NY 10032, USA. Email: spr2@cumc.columbia.edu

Footnotes

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Declaration of interest

S.P.R. has served as a consultant to Lundbeck. B.R.R. has received honoraria from MedAvante and Pfizer and served as a consultant to RCT Logic. M.E.T. has acted as an advisor/consultant to AstraZeneca, Bristol-Myers Squibb Company, Cerecor, Inc., Eli Lilly & Company, Forest Laboratories, Johnson & Johnson (includes Ortho-McNeil Pharmaceuticals), Merck and Co. Inc., Novartis, Otsuka, Pfizer, and Shire US Inc.

Footnotes

References

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Practising evidence-based medicine in an era of high placebo response: number needed to treat reconsidered

  • Steven P. Roose (a1), Bret R. Rutherford (a1), Melanie M. Wall (a1) and Michael E. Thase (a2)

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Practising evidence-based medicine in an era of high placebo response: number needed to treat reconsidered

  • Steven P. Roose (a1), Bret R. Rutherford (a1), Melanie M. Wall (a1) and Michael E. Thase (a2)
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eLetters

Number needed to treat and number needed to harm remain helpful: A response to Roose

Leslie Citrome, Clinical Professor of Psychiatry and Behavioral Sciences, New York Medical College, Valhalla, NY USA
Terence A Ketter, Professor of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA USA
18 May 2016

We read with interest the commentary by Dr. Roose et al. regarding number needed to treat (NNT) and the concern that this metric is difficult to interpret given the high placebo response rates observed in contemporary clinical trials (1). Dr. Roose and associates’ principal objection is that “NNTs derived from clinical trials are not directly relevant to clinical decision-making, because they are based on control conditions that do not exist in standard practice.” Although we agree that this can limit utility of NNTs from some studies, we contend that NNTs commonly remain “indirectly” relevant, as explained below.

Indirect comparisons of effect size among different medication choices can be quite helpful in ranking interventions for both efficacy and common tolerability challenges, provided that the studies used for these calculations are similar enough. Number needed to harm (NNH) values may be even more helpful when distinguishing among treatments that are relatively otherwise similar (2). The NNH can be for overall tolerability (discontinuation because of an AE) or the occurrence of specific AEs of concern for individual patients being treated (such as sedation, weight gain, or akathisia). Moreover, ratios of NNH to NNT can provide overall estimates of the risk versus benefit trade-offs involved. Finally, we suggest that all of these above concepts are straightforward enough for average clinicians to calculate and understand (3, 4).

References

1.Roose SP, Rutherford BR, Wall MM, Thase ME. Practising evidence-based medicine in an era of high placebo response: number needed to treat reconsidered. Br J Psychiatry 2016;208:416-20.

2.Ketter TA, Miller S, Dell'Osso B, Calabrese JR, Frye MA, Citrome L. Balancing benefits and harms of treatments for acute bipolar depression. J Affect Disord 2014;169 (Suppl 1):S24-33.

3.Citrome L, Ketter TA. When does a difference make a difference? Interpretation of number needed to treat, number needed to harm, and likelihood to be helped or harmed. Int J Clin Pract 2013;67:407-11.

4.Citrome L, Ketter TA. Teaching the philosophy and tools of evidence-based medicine: misunderstandings and solutions. Int J Clin Pract 2009;63:353-9.

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Conflict of interest: In the past 36 months Leslie Citrome has engaged in collaborative research with, or received consulting or speaking fees, from: Acadia, Alexza, Allergan, Alkermes, AstraZeneca, Avanir, Bristol-Myers Squibb, Eli Lilly, Forum, Genentech, Janssen, Jazz, Lundbeck, Merck, Medivation, Mylan, Neurocrine, Novartis, Noven, Otsuka, Pfizer, Reckitt Benckiser, Reviva, Shire, Sunovion, Takeda, Teva, and Valeant. In the past 36 months Terence Ketter has engaged in collaborative research with, or received consulting or speaking fees, from: Abbott, Allergan, AstraZeneca, Avanir, Cephalon, Depotmed, Eli Lilly, GlaxoSmithKline, Janssen, Merck, Otsuka, Pfizer and Sunovion. In addition, Dr. Ketter’s spouse is an employee and stockholder of Janssen.

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