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

2016 ◽  
Vol 208 (5) ◽  
pp. 416-420 ◽  
Author(s):  
Steven P. Roose ◽  
Bret R. Rutherford ◽  
Melanie M. Wall ◽  
Michael E. Thase
Keyword(s):  
Evidence Based Medicine ◽  
Clinical Management ◽  
Clinical Decision Making ◽  
Placebo Response ◽  
Clinical Decision ◽  
Placebo Control ◽  
Number Needed To Treat ◽  
Therapeutic Effects ◽  
Evidence Based ◽  
Based Medicine

SummaryThe 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.

The ‘Number Needed to Treat’ and the ‘Adjusted NNT’ in Health Care Decision-Making

1998 ◽  
Vol 3 (1) ◽  
pp. 44-49 ◽  
Author(s):  
Jack Dowie
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Evidence Based Medicine ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Clinical Decision ◽  
Population Based ◽  
Number Needed To Treat ◽  
Evidence Based ◽  
Based Medicine

Within ‘evidence-based medicine and health care’ the ‘number needed to treat’ (NNT) has been promoted as the most clinically useful measure of the effectiveness of interventions as established by research. Is the NNT, in either its simple or adjusted form, ‘easily understood’, ‘intuitively meaningful’, ‘clinically useful’ and likely to bring about the substantial improvements in patient care and public health envisaged by those who recommend its use? The key evidence against the NNT is the consistent format effect revealed in studies that present respondents with mathematically-equivalent statements regarding trial results. Problems of understanding aside, trying to overcome the limitations of the simple (major adverse event) NNT by adding an equivalent measure for harm (‘number needed to harm’ NNH) means the NNT loses its key claim to be a single yardstick. Integration of the NNT and NNH, and attempts to take into account the wider consequences of treatment options, can be attempted by either a ‘clinical judgement’ or an analytical route. The former means abandoning the explicit and rigorous transparency urged in evidence-based medicine. The attempt to produce an ‘adjusted’ NNT by an analytical approach has succeeded, but the procedure involves carrying out a prior decision analysis. The calculation of an adjusted NNT from that analysis is a redundant extra step, the only action necessary being comparison of the results for each option and determination of the optimal one. The adjusted NNT has no role in clinical decision-making, defined as requiring patient utilities, because the latter are measurable only on an interval scale and cannot be transformed into a ratio measure (which the adjusted NNT is implied to be). In any case, the NNT always represents the intrusion of population-based reasoning into clinical decision-making.

scholarly journals Tips for teaching evidence-based medicine in a clinical setting: lessons from adult learning theory. Part one

2008 ◽  
Vol 101 (10) ◽  
pp. 493-500 ◽  
Author(s):  
Kausik Das ◽  
Sadia Malick ◽  
Khalid S Khan
Keyword(s):  
Evidence Based Medicine ◽  
Clinical Decision Making ◽  
Adult Learning Theory ◽  
Clinical Decision ◽  
Evidence Based ◽  
Educational Training ◽  
Practice Teaching ◽  
Approach To Teaching ◽  
Indispensable Tool ◽  
Based Medicine

Summary Evidence-based medicine (EBM) is an indispensable tool in clinical practice. Teaching and training of EBM to trainee clinicians is patchy and fragmented at its best. Clinically integrated teaching of EBM is more likely to bring about changes in skills, attitudes and behaviour. Provision of evidence-based health care is the most ethical way to practice, as it integrates up-to-date, patient-oriented research into the clinical decision making process, thus improving patients' outcomes. In this article, we aim to dispel the myth that EBM is an academic and statistical exercise removed from practice by providing practical tips for teaching the minimum skills required to ask questions and critically identify and appraise the evidence and presenting an approach to teaching EBM within the existing clinical and educational training infrastructure.

scholarly journals Tips for teaching evidence-based medicine in a clinical setting: lessons from adult learning theory. Part two

2008 ◽  
Vol 101 (11) ◽  
pp. 536-543 ◽  
Author(s):  
Sadia Malick ◽  
Kausik Das ◽  
Khalid S Khan
Keyword(s):  
Adult Learning ◽  
Evidence Based Medicine ◽  
Clinical Decision Making ◽  
Adult Learning Theory ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Use ◽  
Evidence Based Healthcare ◽  
Based Medicine ◽  
Oriented Research

Summary Evidence-based medicine (EBM) is the clinical use of current best available evidence from relevant, valid research. Provision of evidence-based healthcare is the most ethical way to practise as it integrates up-to-date patient-oriented research into the clinical decision-making to improve patients' outcomes. This article provides tips for teachers to teach clinical trainees the final two steps of EBM: integrating evidence with clinical judgement and bringing about change.

Evidence-based medicine and machine learning: a partnership with a common purpose

2020 ◽  
pp. bmjebm-2020-111379
Author(s):  
Ian Scott ◽  
David Cook ◽  
Enrico Coiera
Keyword(s):  
Machine Learning ◽  
Evidence Based Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Therapeutic Interventions ◽  
Evidence Based ◽  
Rigorous Approach ◽  
Common Purpose ◽  
Future Outcomes ◽  
Based Medicine

From its origins in epidemiology, evidence-based medicine has promulgated a rigorous approach to assessing the validity, impact and applicability of hypothesis-driven empirical research used to evaluate the utility of diagnostic tests, prognostic tools and therapeutic interventions. Machine learning, a subset of artificial intelligence, uses computer programs to discover patterns and associations within huge datasets which are then incorporated into algorithms used to assist diagnoses and predict future outcomes, including response to therapies. How do these two fields relate to one another? What are their similarities and differences, their strengths and weaknesses? Can each learn from, and complement, the other in rendering clinical decision-making more informed and effective?

Practicing Evidence-Based Medicine

2015 ◽  
Author(s):  
Michael Barnett ◽  
Niteesh Choudhry
Keyword(s):  
Evidence Based Medicine ◽  
Test Performance ◽  
Statistical Significance ◽  
Clinical Decision ◽  
Medical Decision ◽  
Number Needed To Treat ◽  
Evidence Based ◽  
Symptomatic Carotid ◽  
The North ◽  
Based Medicine

Today, a plethora of resources for evidence-based medicine (EBM) are available via alert services, compendia, and more. In theory, a clinician researching a topic or looking for information regarding a clinical decision should easily find the literature or synopses needed. However, the real challenge lies in recognizing which resources (out of hundreds or possibly thousands) present the best and most reliable evidence. As well, evidence from research is only part of the decision calculus, and the clinician, not the evidence, makes the final decisions. Medical decision analysis attempts to formalize the process and reduce it to algebra, but it is difficult or impossible to represent all the components of a decision mathematically and validly let alone do so in “real time” for individual patients. This review discusses these challenges and more, including how to ask answerable questions, understand the hierarchy for evidence-based information resources, critically appraise evidence, and apply research results to patient care. Figures show the total number of new articles in Medline from 1965 to 2012, a “4S” hierarchy of preappraised medicine, percentage of physician and medical student respondents with a correct or incorrect answer to a question about calculating the positive predictive value of a hypothetical screening test, a nomogram for Bayes’s rule, an example of nomogram use for pulmonary embolism, and a model for evidence-informed clinical decisions. Tables list selected barriers to the implementation of EBM; Patient, Intervention, Comparison, and Outcome (PICO) framework for formulating clinical questions; guides for assessing medical texts for evidence-based features; clinically useful measures of disease frequency and statistical significance and precision; definitions of clinically useful measures of diagnostic test performance and interpretation; definitions of clinically useful measures of treatment effects from clinical trials; summary of results and derived calculations from the North American Symptomatic Carotid Endarterectomy Trial (NASCET); and selected number needed to treat values for common therapies. This review contains 6 highly rendered figures, 9 tables, and 28 references.

Practicing Evidence-Based Medicine (SAGHE)

2015 ◽  
Author(s):  
Michael Barnett ◽  
Niteesh Choudhry
Keyword(s):  
Evidence Based Medicine ◽  
Test Performance ◽  
Statistical Significance ◽  
Clinical Decision ◽  
Medical Decision ◽  
Number Needed To Treat ◽  
Evidence Based ◽  
Symptomatic Carotid ◽  
The North ◽  
Based Medicine

Today, a plethora of resources for evidence-based medicine (EBM) are available via alert services, compendia, and more. In theory, a clinician researching a topic or looking for information regarding a clinical decision should easily find the literature or synopses needed. However, the real challenge lies in recognizing which resources (out of hundreds or possibly thousands) present the best and most reliable evidence. As well, evidence from research is only part of the decision calculus, and the clinician, not the evidence, makes the final decisions. Medical decision analysis attempts to formalize the process and reduce it to algebra, but it is difficult or impossible to represent all the components of a decision mathematically and validly let alone do so in “real time” for individual patients. This review discusses these challenges and more, including how to ask answerable questions, understand the hierarchy for evidence-based information resources, critically appraise evidence, and apply research results to patient care. Figures show the total number of new articles in Medline from 1965 to 2012, a “4S” hierarchy of preappraised medicine, percentage of physician and medical student respondents with a correct or incorrect answer to a question about calculating the positive predictive value of a hypothetical screening test, a nomogram for Bayes’s rule, an example of nomogram use for pulmonary embolism, and a model for evidence-informed clinical decisions. Tables list selected barriers to the implementation of EBM; Patient, Intervention, Comparison, and Outcome (PICO) framework for formulating clinical questions; guides for assessing medical texts for evidence-based features; clinically useful measures of disease frequency and statistical significance and precision; definitions of clinically useful measures of diagnostic test performance and interpretation; definitions of clinically useful measures of treatment effects from clinical trials; summary of results and derived calculations from the North American Symptomatic Carotid Endarterectomy Trial (NASCET); and selected number needed to treat values for common therapies. This review contains 6 highly rendered figures, 9 tables, and 28 references.

Practicing Evidence-Based Medicine

2015 ◽  
Author(s):  
Michael Barnett ◽  
Niteesh Choudhry
Keyword(s):  
Evidence Based Medicine ◽  
Test Performance ◽  
Statistical Significance ◽  
Clinical Decision ◽  
Medical Decision ◽  
Number Needed To Treat ◽  
Evidence Based ◽  
Symptomatic Carotid ◽  
The North ◽  
Based Medicine

Today, a plethora of resources for evidence-based medicine (EBM) are available via alert services, compendia, and more. In theory, a clinician researching a topic or looking for information regarding a clinical decision should easily find the literature or synopses needed. However, the real challenge lies in recognizing which resources (out of hundreds or possibly thousands) present the best and most reliable evidence. As well, evidence from research is only part of the decision calculus, and the clinician, not the evidence, makes the final decisions. Medical decision analysis attempts to formalize the process and reduce it to algebra, but it is difficult or impossible to represent all the components of a decision mathematically and validly let alone do so in “real time” for individual patients. This review discusses these challenges and more, including how to ask answerable questions, understand the hierarchy for evidence-based information resources, critically appraise evidence, and apply research results to patient care. Figures show the total number of new articles in Medline from 1965 to 2012, a “4S” hierarchy of preappraised medicine, percentage of physician and medical student respondents with a correct or incorrect answer to a question about calculating the positive predictive value of a hypothetical screening test, a nomogram for Bayes’s rule, an example of nomogram use for pulmonary embolism, and a model for evidence-informed clinical decisions. Tables list selected barriers to the implementation of EBM; Patient, Intervention, Comparison, and Outcome (PICO) framework for formulating clinical questions; guides for assessing medical texts for evidence-based features; clinically useful measures of disease frequency and statistical significance and precision; definitions of clinically useful measures of diagnostic test performance and interpretation; definitions of clinically useful measures of treatment effects from clinical trials; summary of results and derived calculations from the North American Symptomatic Carotid Endarterectomy Trial (NASCET); and selected number needed to treat values for common therapies. This review contains 6 highly rendered figures, 9 tables, and 28 references.

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