Evidence-Based Medicine Online First, published on November 26, 2013 as 10.1136/eb-2013-101610 Perspective
Basic learning concepts in EBM: the bidimentional hierarchy of evidence Pierre La Rochelle 10.1136/eb-2013-101610
Department of Medecine familiale et medecine d’urgence, Université Laval, CSSS de Kamouraska 360 des Érables, Sainte-Anne-de-la-Pocatière, Québec, Canada
Correspondence to: Dr Pierre La Rochelle, Department of Medecine familiale et medecine d’urgence, Université Laval, CSSS de Kamouraska 360 des Érables, Sainte-Anne-de-la-Pocatière, Québec, Canada G0R 1Z0; [email protected]
To introduce clinicians to EBM it can be useful to draw a simplified graph (figure 1) to get a basic understanding of some of the most important concepts. This graph focuses on the relationship between the study design and its corresponding risk of bias and the magnitude of an effect to observe. On the left, we have an upsidedown triangle. The width level of the triangle depicts the prevalence of all the possible effects to be observed. In fact, small magnitude effects are very frequent whereas dramatic magnitude effects are very rare. Just to the right, a similar upright triangle portrays different types of studies ranked in accordance with their risk of bias. The width of the triangle represents the amplitude of the bias. Case reports are often inaccurate because they carry a very high risk of bias. That is why they are on the bottom of the triangle. In contrast to them, systematic reviews of randomised control trials (RCTs) or large RCTs both containing a low-to-moderate risk of bias are on the top. If we are looking for a small effect, we need to select the study design that will provide the highest resolution. For example, to demonstrate the effectiveness of rosuvastatin in the treatment of stable, mild dyslipidemia, we use a large, multi-centered RCT with a rigorous methodology.1 The difference measured, which is significant, has a small magnitude: a 0.55% absolute overall mortality reduction (95% CI 0.67 to 0.97) or a 19% relative risk reduction. This small effect, the number needed to treat of 182 over a period of
1.9 years, may be clinically relevant to patients or not. Moreover, at an intermediate level, we have cohort studies bearing their bias of residual confounding. We can use the example of the sleeping position to prevent the sudden infant death syndrome.2 In fact, a systematic review of observational studies has revealed a magnitude of an important mortality reduction depicting OR of 4.1 (95% CI 3.1 to 5.5) of the back position over the front position. Combined with a historical, observational study showing a dose–response gradient, these facts were sufficient to conclude that the intervention was efficacious. Finally, if we are looking for a very large effect, such as, insulin therapy to prevent mortality in diabetic ketoacidosis, we can use a case series which can demonstrate its dramatic impact on mortality.3 However, in that particular situation, we always need to stay on the lookout for large biases like a prognostic factor such as age. Thus, the optimal study design depends primarily on the magnitude of the impact of the intervention that we intend to observe. Then, the validity of the study must be assessed using different criteria depending on which study design had been selected. When a case series is chosen, the clinician must first pay attention to the magnitude of the effect that was previously estimated. This magnitude should have a risk ratio corrected for available confounding variables that are higher than 2–5 range or a lower than 0.5–0.2 range.4 5 However, when a more sophisticated study design such as a systematic review
Figure 1 Relationship between the magnitude of an effect to observe and different study designs with their corresponding risk of bias.
Evid Based Med Month 2013 | volume 0 | number 0 |
Copyright Article author (or their employer) 2013. Produced by BMJ Publishing Group Ltd under licence.
1
Perspective of RCTs is chosen, we must rather focus on the rigour of the methodology, the statistical precision of the estimates and the significance of the measured outcomes to patients. All in all, these different study designs, when used under specific circumstances, can lead to a strong recommendation based on high-enough quality evidence. Finally, figure 1 illustrates the links between some of the most basic concepts of EBM and should help clinicians to pursue their learning in clinical epidemiology. Acknowledgement The author acknowledge the contributions of Professor Brian Haynes and Sir Iain Chalmers for their precious advice and Mr Rusty Gagnon for his knowledge on English writing.
2
Evid Based Med Month 2013 | volume 0 | number 0 |
Competing interests None. References 1. Ridker PM, Danielson E, Fonseca FA, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med 2008;359:2195–207. 2. Gilbert R, Salanti G, Harden M, et al. Infant sleeping position and the sudden infant death syndrome: systematic review of observational studies and historical review of recommendations from 1940 to 2002. Int J Epidemiol 2005;34:874–87. 3. Banting F. Pancreatic extracts in the treatment of diabetes mellitus. CMAJ 1922;12:141–6. 4. Glasziou P, Chalmers I, Rawlins M, et al. When are randomised trials unnecessary? Picking signal from noise. BMJ 2007;334:349–51. 5. Guyatt GH, Oxman AD, Sultan S, et al. GRADE guidelines: 9. Rating up the quality of evidence. J Clin Epidemiol 2011;64:1311–16.
Basic learning concepts in EBM: the bidimentional hierarchy of evidence Pierre La Rochelle 10.1136/eb-2013-101610
Department of Medecine familiale et medecine d’urgence, Université Laval, CSSS de Kamouraska 360 des Érables, Sainte-Anne-de-la-Pocatière, Québec, Canada
Correspondence to: Dr Pierre La Rochelle, Department of Medecine familiale et medecine d’urgence, Université Laval, CSSS de Kamouraska 360 des Érables, Sainte-Anne-de-la-Pocatière, Québec, Canada G0R 1Z0; [email protected]
To introduce clinicians to EBM it can be useful to draw a simplified graph (figure 1) to get a basic understanding of some of the most important concepts. This graph focuses on the relationship between the study design and its corresponding risk of bias and the magnitude of an effect to observe. On the left, we have an upsidedown triangle. The width level of the triangle depicts the prevalence of all the possible effects to be observed. In fact, small magnitude effects are very frequent whereas dramatic magnitude effects are very rare. Just to the right, a similar upright triangle portrays different types of studies ranked in accordance with their risk of bias. The width of the triangle represents the amplitude of the bias. Case reports are often inaccurate because they carry a very high risk of bias. That is why they are on the bottom of the triangle. In contrast to them, systematic reviews of randomised control trials (RCTs) or large RCTs both containing a low-to-moderate risk of bias are on the top. If we are looking for a small effect, we need to select the study design that will provide the highest resolution. For example, to demonstrate the effectiveness of rosuvastatin in the treatment of stable, mild dyslipidemia, we use a large, multi-centered RCT with a rigorous methodology.1 The difference measured, which is significant, has a small magnitude: a 0.55% absolute overall mortality reduction (95% CI 0.67 to 0.97) or a 19% relative risk reduction. This small effect, the number needed to treat of 182 over a period of
1.9 years, may be clinically relevant to patients or not. Moreover, at an intermediate level, we have cohort studies bearing their bias of residual confounding. We can use the example of the sleeping position to prevent the sudden infant death syndrome.2 In fact, a systematic review of observational studies has revealed a magnitude of an important mortality reduction depicting OR of 4.1 (95% CI 3.1 to 5.5) of the back position over the front position. Combined with a historical, observational study showing a dose–response gradient, these facts were sufficient to conclude that the intervention was efficacious. Finally, if we are looking for a very large effect, such as, insulin therapy to prevent mortality in diabetic ketoacidosis, we can use a case series which can demonstrate its dramatic impact on mortality.3 However, in that particular situation, we always need to stay on the lookout for large biases like a prognostic factor such as age. Thus, the optimal study design depends primarily on the magnitude of the impact of the intervention that we intend to observe. Then, the validity of the study must be assessed using different criteria depending on which study design had been selected. When a case series is chosen, the clinician must first pay attention to the magnitude of the effect that was previously estimated. This magnitude should have a risk ratio corrected for available confounding variables that are higher than 2–5 range or a lower than 0.5–0.2 range.4 5 However, when a more sophisticated study design such as a systematic review
Figure 1 Relationship between the magnitude of an effect to observe and different study designs with their corresponding risk of bias.
Evid Based Med Month 2013 | volume 0 | number 0 |
Copyright Article author (or their employer) 2013. Produced by BMJ Publishing Group Ltd under licence.
1
Perspective of RCTs is chosen, we must rather focus on the rigour of the methodology, the statistical precision of the estimates and the significance of the measured outcomes to patients. All in all, these different study designs, when used under specific circumstances, can lead to a strong recommendation based on high-enough quality evidence. Finally, figure 1 illustrates the links between some of the most basic concepts of EBM and should help clinicians to pursue their learning in clinical epidemiology. Acknowledgement The author acknowledge the contributions of Professor Brian Haynes and Sir Iain Chalmers for their precious advice and Mr Rusty Gagnon for his knowledge on English writing.
2
Evid Based Med Month 2013 | volume 0 | number 0 |
Competing interests None. References 1. Ridker PM, Danielson E, Fonseca FA, et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N Engl J Med 2008;359:2195–207. 2. Gilbert R, Salanti G, Harden M, et al. Infant sleeping position and the sudden infant death syndrome: systematic review of observational studies and historical review of recommendations from 1940 to 2002. Int J Epidemiol 2005;34:874–87. 3. Banting F. Pancreatic extracts in the treatment of diabetes mellitus. CMAJ 1922;12:141–6. 4. Glasziou P, Chalmers I, Rawlins M, et al. When are randomised trials unnecessary? Picking signal from noise. BMJ 2007;334:349–51. 5. Guyatt GH, Oxman AD, Sultan S, et al. GRADE guidelines: 9. Rating up the quality of evidence. J Clin Epidemiol 2011;64:1311–16.
