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Articles by Benjamin Djulbegovic
Linking Minimally Important Differences (MID) and Acceptable Regret to Elicit Values and Preferences in Health Decision Models
1 Introduction Decision-analytical models are increasingly used to support clinical recommendations, shared decision-making and health policy. Their credibility depends not only on the best available evidence regarding benefits and harms, but also on explicit judgements about how much those benefits and harms matter to patients and other decision-makers [1, 2]. These judgements are commonly described as values and preferences (V&P) [1, 2].
Theory of Clinical Therapeutic Progress: Reconciling Equipoise With Fat-Tailed (Skewed) Outcomes
Original ResearchArticles in Press112004Open access 1Department of Mathematics, Indiana University Northwest, Gary, Indiana, USA 2Medical University of South Carolina, Division of Medical Hematology and Oncology, Department of Medicine, Charleston, SC, USA Publication History: Received July 14, 2025; Revised September 20, 2025; Accepted September 24, 2025; Published online September 29, 2025 DOI: 10.1016/j.jclinepi.2025.112004 External LinkAlso available on ScienceDirect External Link...
Un nuevo algoritmo en el diagnóstico de la anemia
Ayuda Buscar en la ayuda Buscar en la ayuda
There Is No Upper Limit on the Maximum Effect That Can Be Detected in Randomized Trials
Original ResearchArticles in Press111828Open access 1Medical University of South Carolina, Division of Medical Hematology and Oncology, Department of Medicine, Charleston, SC, USA 2Department of Mathematics, Indiana University Northwest, Gary, Indiana, USA 3Dept. of Equity, Ethics and Policy School of Population and Global Health, McGill University,2001 McGill College Ave.
Improving Guideline Development Processes: Integrating Evidence Estimation and Decision‐Analytical Frameworks
Evidence-based medicine (EBM) has substantially contributed to the advancement of clinical practice [1]. Originally centred on critical appraisal and the development of systematic reviews (SRs), EBM, through the operationalization of its principles via the Grades of Recommendation Assessment, Development and Evaluation (GRADE) system for evaluating medical evidence, has increasingly contributed to advancements in methods for clinical practice guidelines (CPGs) [1, 2].
What is the probability that higher versus lower quality of evidence represents true effects estimates?
A fundamental principle of evidence-based medicine (EBM) is that high-certainty evidence (CoE) offers more trustworthy effect estimates than lower CoE.1 This assumption has led to a common practice of relying on high CoE to issue strong recommendations for clinical practice and not recommending treatments if they are based on lower CoE.1 However, the fact that research findings could be affected by a high risk of bias, or chance effect does not necessarily mean that the findings observed in...
High certainty evidence is stable and trustworthy, whereas evidence of moderate or lower certainty may be equally prone to being unstable
This paper is only available as a PDF. To read, Please Download here. Highlights What is new? • Intervention effect estimates based on evidence without serious limitations (i.e. high-quality evidence) will change in relation to direction or statistical significance in about 1 in 14 cases. • A single limitation (even in case of moderate-quality evidence) may result in changes to effect direction or statistical significance in about 1 in 4 cases.
Decision curve analysis based on summary data
1 INTRODUCTION Decision curve analysis (DCA), a widely used method for evaluating diagnostic tests and predictive models, has thus far required individual patient data (IPD).1-4 Although IPD remains optimal for the development of predictive models, they are relatively rarely done.5 Even when investigators develop models based on IPD, they are often not subjected to decision analytical assessments such as DCA.
Guideline panel social dynamics influence the development of clinical practice recommendations: A mixed-methods systematic review
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Generalised decision curve analysis for explicit comparison of treatment effects
1 INTRODUCTION Decision curve analysis (DCA) represents a significant advance in using decision theory to evaluate diagnostic tests and prediction models.1 It incorporates the global, implicitly (holistically) assessed benefits and harms of treatment effects to enable the analyst to gain insight into whether using a test or model to aid clinical-decision making would lead to better clinical outcomes in comparison with the default strategies of “treat all” or “treat none”.
Sorites paradox and persistence in overuse and underuse in healthcare delivery services
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Sorites paradox and persistence in overuse and underuse in healthcare delivery services
Healthcare around the world suffers from underuse (undertreatment and undertesting) and overuse (overtreatment and overtesting) that have persisted for decades.
Evaluation of a fast‐and‐frugal clinical decision algorithm (‘pathways’) on clinical outcomes in hospitalised patients with COVID‐19 treated with anticoagulants
2.6.1 Rationale Statistically, one can argue that because its strong theoretical underpinning, evaluation of FFT-algorithm can be assessed using unadjusted comparisons. However, in observational studies we can never be sure that the effects of intervention (adherence to FFT-pathway algorithm, in our case) are solely responsible for observed outcome and not prognostic imbalance or cointerventions. Hence, these factors ought to be considered in the analysis.
Evidence, values & masks for control of COVID-19: two years later
The relationship between pragmatism, timing, and study size on impact of treatment trials: a qualitative, hypothesis generating study of systemic corticosteroids for COVID-19
IDENTIFICATION OF THRESHOLD FOR LARGE (DRAMATIC) EFFECTS THAT WOULD OBVIATE RANDOMIZED TRIALS IS NOT POSSIBLE
Abstract Objective To analyze distribution of “dramatic”, large treatment effects. Study Design & Setting Pareto distribution modeling of previously reported cohorts of 3,486 randomized trials (RCTs) that enrolled 1,532,459 patients and 730 non-randomized studies (NRS) enrolling 1,650,658 patients. Results We calculated the Pareto α parameter, which determines the tail of the distribution for various starting points of distribution [odds ratiomin (ORmin)].
High quality (certainty) evidence changes less often than low‐quality evidence, but the magnitude of effect size does not systematically differ between studies with low versus high‐quality evidence
Abstract Rationale, Aims, and Objectives It is generally believed that evidence from low quality of evidence generate inaccurate estimates about treatment effects more often than evidence from high (certainty) quality evidence (CoE). As a result, we would expect that (a) estimates of effects of health interventions initially based on high CoE change less frequently than the effects estimated by lower CoE (b) the estimates of magnitude of effect size differ between high and low CoE.
Using Twitter for the identification of COVID-19 vaccine-associated haematological adverse events
Although Operation Warp Speed (the US federal funding effort for vaccine development by pharmaceutical corporations) helped to facilitate the worldwide development of three US Food and Drug Administration (FDA) emergency-use approved COVID-19 vaccines in the USA in record time in 2020, it also resulted in rapid and large-scale vaccine dissemination before real-word safety studies outside of the original clinical trials could be done.
Can we trust strong recommendations based on low quality evidence?
Intended for healthcare professionals Toggle navigation The BMJ logo Toggle top menu News & Views Can we trust strong... Can we trust strong recommendations based on low quality evidence?
Certainty of evidence and intervention's benefits & harms are key determinants of guidelines’ recommendations
Highlights What is already known on this topic • Many factors recommended by evidence-based guidelines methods developed in the field of evidence-based health care (EBHC factors) and those suggested in the general literature on decision-making (non-EBHC factors) are postulated to affect guidelines panels’ individual and group decision-making.
Evidence, values & masks for control of COVID-19
Benjamin Djulbegovic, MD, PhD Benjamin Djulbegovic Correspondence Correspondence: Benjamin Djulbegovic, MD, PhD 1500 E Duarte Road Duarte, CA 91010-3000 Phone: 626-366-3053 (cell) Affiliations City of Hope, 1500 East Duarte Rd, Duarte, CAComparative Effectiveness Research & Evidence-based Medicine, 1500 East Duarte Rd, Duarte, CA; Search for articles by this author Iztok Hozo, PhD Gordon Guyatt, MD, MSc This paper is only available as a PDF. To read, Please Download here.
When are randomized trials unnecessary? A signal detection theory approach to approving new treatments based on non‐randomized studies
ORIGINAL PAPER Benjamin Djulbegovic MD, PhD Corresponding Author bdjulbegovic@coh.org orcid.org/0000-0003-0671-1447 Department of Supportive Care Medicine, City of Hope National Medical Centre, Duarte, California, USA Department of Haematology, City of Hope National Medical Centre, Duarte, California, USA Comparative Effectiveness Research and Evidence‐Based Medicine, City of Hope National Medical Centre, Duarte, California, USA Correspondence Benjamin Djulbegovic, Department of Supportive...
Research synthesis of information theory measures of uncertainty: Meta‐analysis of entropy and mutual information of diagnostic tests
ORIGINAL PAPER Benjamin Djulbegovic MD, Ph.D Corresponding Author bdjulbegovic@coh.org orcid.org/0000-0003-0671-1447 Department of Supportive Care Medicine, City of Hope, Duarte, California, USA Department of Hematology, City of Hope, Duarte, California, USA Evidence‐based Analytics & Program for Comparative Effectiveness Research and Evidence‐based Medicine, City of Hope, Duarte, California, USA Correspondence Benjamin Djulbegovic, Department of Supportive Care Medicine, City of Hope, 1500...
Evaluation of the U.S. governors' decision when to issue stay‐at‐home orders
ORIGINAL PAPER Benjamin Djulbegovic MD, PhD Corresponding Author E-mail address: bdjulbegovic@coh.org https://orcid.org/0000-0003-0671-1447 Department of Supportive Care Medicine, City of Hope National Medical Center, Duarte, California, USA Department of Hematology, City of Hope National Medical Center, Duarte, California, USA Evidence‐Based Analytics and Comparative Effectiveness Research, City of Hope National Medical Center, Duarte, California, USA Correspondence Benjamin Djulbegovic, MD,...
Estimating the mean and variance from the median, range, and the size of a sample
Abstract Background Usually the researchers performing meta-analysis of continuous outcomes from clinical trials need their mean value and the variance (or standard deviation) in order to pool data. However, sometimes the published reports of clinical trials only report the median, range and the size of the trial. Methods In this article we use simple and elementary inequalities and approximations in order to estimate the mean and the variance for such trials.
Evidence-based medicine in times of crisis
Gordon Guyatt, MD, MSc Gordon Guyatt Correspondence Corresponding author: Gordon Guyatt, MD, Department of Heath Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada, L8S 4L8, Canada, , phone: 905-541-1215 Affiliations McMaster University, Hamilton, ON, L8S 4L8, Canada Search for articles by this author To read this article in full you will need to make a payment Subscribe to Journal of Clinical Epidemiology Article Info Publication History Accepted: July 8, 2020...
The threshold model revisited
Abstract Background The threshold model represents one of the most significant advances in the field of medical decision‐making, yet it often does not apply to the most common class of clinical problems, which include health outcomes as a part of definition of disease. In addition, the original threshold model did not take a decision‐maker's values and preferences explicitly into account.
US Food and Drug Administration Approvals of Drugs and Devices Based on Nonrandomized Clinical Trials: A Systematic Review and Meta-analysis
Key Points español 中文 (chinese) Question Are US Food and Drug Administration (FDA) decisions to approve new therapies based on nonrandomized clinical trials (non-RCTs) associated with the sizes of treatment effects?
The importance of randomised vs non-randomised trials
Benjamin Djulbegovic Benjamin Djulbegovic Affiliations Department of Supportive Care Medicine, Department of Hematology, and Program for Evidence-based Medicine and Comparative Effectiveness Research, City of Hope, Duarte, CA 91010, USA Search for articles by this author Paul Glasziou Iain Chalmers Hertzel Gerstein and colleagues propose that estimates of treatment effects with a relative risk (RR) of more than 4 might show that randomised controlled trials (RCTs) are not needed because...
Evidence vs Consensus in Clinical Practice Guidelines
Clinical practice guidelines have become increasingly prominent in clinical medicine over the last 4 decades, and represent one of the most important tools for potentially improving clinical decision-making and, in turn, potentially improving patients’ outcomes.1 Historically, many organizations categorized their guidelines as evidence-based or consensus-based and some organizations, including some of the largest professional organizations devoted to developing clinical practice guidelines...
Statins for Primary Prevention of Cardiovascular Disease
TO THE EDITOR: In their recent article, Yebyo and colleagues claimed to report “the first study to determine 10-year risk thresholds above which the benefits outweigh the harms” related to statin use in CVD (1). However, the authors did not calculate actual thresholds at which treatment with statins is equal to no treatment; they calculated the probabilities of CVD at which the benefits of statins are greater than its harms, or vice versa—a subtle but important difference.
Failure to place evidence at the centre of quality improvement remains a major barrier for advances in quality improvement
The full text of this article hosted at iucr.org is unavailable due to technical difficulties.
Management of cancer-associated anemia with erythropoiesis-stimulating agents: ASCO/ASH clinical practice guideline update
Julia Bohlius, Kari Bohlke, Roberto Castelli, Benjamin Djulbegovic, Maryam B. Lustberg, Massimo Martino, Giannis Mountzios, Namrata Peswani, Laura Porter, Tiffany N.
Management of Cancer-Associated Anemia With Erythropoiesis-Stimulating Agents: ASCO/ASH Clinical Practice Guideline Update
Use of erythropoiesis-stimulating agents (ESAs) to manage anemia raises hemoglobin (HgB) levels and reduces the need for RBC transfusions, but increases the risk of thromboembolic events.1,2 Studies have also reported decreased survival, increased mortality during active study phase, and/or an increased risk of cancer progression or recurrence with the use of ESAs in patients with cancer.3-6 The risks of ESAs prompted multiple regulatory actions by the US Food and Drug Administration (FDA)...
Journal of Evaluation in Clinical Practice
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Tolerating uncertainty about conceptual models of uncertainty in health care
The full text of this article hosted at iucr.org is unavailable due to technical difficulties.
Journal of Evaluation in Clinical Practice
Abstract Background The threshold model represents one of the most significant advances in the field of medical decision‐making, yet it often does not apply to the most common class of clinical problems, which include health outcomes as a part of definition of disease. In addition, the original threshold model did not take a decision‐maker's values and preferences explicitly into account.
A unifying framework for improving health care
The quality health care around world is suboptimal. To improve the quality of contemporary health care delivery, advocates have proposed a number of scientific and technical initiatives. All these initiatives, however, have arisen and continue to operate in siloes, resulting in confusion and incommensurability among those concerned with health care improvement.
Expected utility versus expected regret theory versions of decision curve analysis do generate different results when treatment effects are taken into account - Hozo - - Journal of Evaluation in Clinical Practice - Wiley Online Library
Decision curve analysis (DCA) is a widely used method for evaluating diagnostic tests and predictive models. It was developed based on expected utility theory (EUT) and has been reformulated using expected regret theory (ERG). Under certain circumstances, these 2 formulations yield different results. Here we describe these situations and explain the variation.
Rational decision making in medicine: Implications for overuse and underuse
In spite of substantial spending and resource utilization, today's health care remains characterized by poor outcomes, largely due to overuse (overtesting/overtreatment) or underuse (undertesting/undertreatment) of health services. To a significant extent, this is a consequence of low‐quality decision making that appears to violate various rationality criteria. Such suboptimal decision making is considered a leading cause of death and is responsible for more than 80% of health expenses.
Transforming clinical practice guidelines and clinical pathways into fast-and-frugal decision trees to improve clinical care strategies
Contemporary delivery of health care is inappropriate in many ways, largely due to suboptimal Q5 decision-making. A typical approach to improve practitioners' decision-making is to develop evidence-based clinical practice guidelines (CPG) by guidelines panels, who are instructed to use their judgments to derive practice recommendations.
Larger effect sizes in non-randomized studies are associated with higher rates of EMA licensing approval
Publication HistoryPublished online: February 09, 2018Accepted: January 31, 2018; Received in revised form: December 17, 2017; Received: September 21, 2017;
Platelet Transfusion: A Clinical Practice Guideline From the AABB
From Brigham and Women's Hospital, Boston, Massachusetts; University of South Florida, Tampa, Florida; University of Washington, Seattle, Washington; University of British Columbia, Vancouver, British Columbia, Canada; Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; Children's Hospital Colorado, Aurora, Colorado; Christiana Care Health System, Wilmington, Delaware; University of Minnesota, Minneapolis, Minnesota; University of Vermont, Burlington, Vermont; Washington University...
Rational decision making in medicine: Implications for overuse and underuse
In spite of substantial spending and resource utilization, today's health care remains characterized by poor outcomes, largely due to overuse (overtesting/overtreatment) or underuse (undertesting/undertreatment) of health services. To a significant extent, this is a consequence of low-quality decision making that appears to violate various rationality criteria. Such suboptimal decision making is considered a leading cause of death and is responsible for more than 80% of health expenses.
On evidence-based medicine - Authors' reply
We thank Marjolein A van der Marck and colleagues, Feras Ali Mustafa, and Jan Matthys for their interest in our Review.1 We agree with van der Marck and colleagues that tackling multimorbidity is an enormous challenge for evidence-based practice that, so far, has not been met.
Determining optimal threshold for statins prescribing: individualization of statins treatment for primary prevention of cardiovascular disease
The American College of Cardiology and American Heart Association (ACC/AHA) statin guidelines recommend that people with risk of cardio-vascular disease (CVD) ?7.5% over 10 years should be treated with statins. This recommendation ignores individual patient CVD risks and preferences. We compared the ACC/AHA guidelines to the following management strategies a) individualized statins treatment based on Framingham Risk Score (FRS), b) treat none, c) treat all.
Towards theory integration: Threshold model as a link between signal detection theory, fast-and-frugal trees and evidence accumulation theory
We connected these large- and small-world theories by demonstrating that seemingly different decision-making concepts are actually equivalent.
Progress in evidence-based medicine: a quarter century on
Is screening for the psychological effects of war useful?
The predicament of patients with suspected Ebola
In their Comment in The Lancet Global Health, Eugene Richardson and colleagues1 criticised the tendency of many analyses of the Ebola epidemic (eg, a WHO report2) to ignore that it may be rational for a patient with a fever to avoid an Ebola treatment unit. They use the prisoner's dilemma to explain such non-cooperative behaviour.The prisoner's dilemma, however, is not the most appropriate analytical framework for this situation.
Expected utility versus expected regret theory versions of decision curve analysis do generate different results when treatment effects are taken into account - Hozo - 2016 - Journal of Evaluation in Clinical Practice - Wiley Online Library
Decision curve analysis (DCA) is a widely used method for evaluating diagnostic tests and predictive models. It was developed based on expected utility theory (EUT) and has been reformulated using expected regret theory (ERG). Under certain circumstances, these 2 formulations yield different results. Here we describe these situations and explain the variation.
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