Evidence-based medicine as Bayesian decision-making

Judgement and Decision Making in Clinical and Return-to-Sports Decision Making: A Narrative Review

Making return-to-sport decisions can be complex and multi-faceted, as it requires an evaluation of an individual's physical, psychological, and social well-being. Specifically, the timing of progression, regression, or return to sport can be difficult to determine due to the multitude of information that needs to be considered by clinicians. With the advent of new sports technology, the increasing volume of data poses a challenge to clinicians in effectively processing and utilising it to enhance the quality of their decisions. To gain a deeper understanding of the mechanisms underlying human decision making and associated biases, this narrative review provides a brief overview of different decision-making models that are relevant to sports rehabilitation settings. Accordingly, decisions can be made intuitively, analytically, and/or with heuristics. This narrative review demonstrates how the decision-making models can be applied in the context of return-to-sport decisions and shed light on strategies that may help clinicians improve decision quality.

A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease

The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study's aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical application of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method.

Label-free multimodal electro-thermo-mechanical (ETM) phenotyping as a novel biomarker to differentiate between normal, benign, and cancerous breast biopsy tissues

BACKGROUND

Technologies for quick and label-free diagnosis of malignancies from breast tissues have the potential to be a significant adjunct to routine diagnostics. The biophysical phenotypes of breast tissues, such as its electrical, thermal, and mechanical properties (ETM), have the potential to serve as novel markers to differentiate between normal, benign, and malignant tissue.

RESULTS

We report a system-of-biochips (SoB) integrated into a semi-automated mechatronic system that can characterize breast biopsy tissues using electro-thermo-mechanical sensing. The SoB, fabricated on silicon using microfabrication techniques, can measure the electrical impedance (Z), thermal conductivity (K), mechanical stiffness (k), and viscoelastic stress relaxation (%R) of the samples. The key sensing elements of the biochips include interdigitated electrodes, resistance temperature detectors, microheaters, and a micromachined diaphragm with piezoresistive bridges. Multi-modal ETM measurements performed on formalin-fixed tumour and adjacent normal breast biopsy samples from N = 14 subjects were able to differentiate between invasive ductal carcinoma (malignant), fibroadenoma (benign), and adjacent normal (healthy) tissues with a root mean square error of 0.2419 using a Gaussian process classifier. Carcinoma tissues were observed to have the highest mean impedance (110018.8 ± 20293.8 Ω) and stiffness (0.076 ± 0.009 kNm-1) and the lowest thermal conductivity (0.189 ± 0.019 Wm-1 K-1) amongst the three groups, while the fibroadenoma samples had the highest percentage relaxation in normalized load (47.8 ± 5.12%).

CONCLUSIONS

The work presents a novel strategy to characterize the multi-modal biophysical phenotype of breast biopsy tissues to aid in cancer diagnosis from small-sized tumour samples. The methodology envisions to supplement the existing technology gap in the analysis of breast tissue samples in the pathology laboratories to aid the diagnostic workflow.

Improving diagnostic decision support through deliberate reflection: a proposal

Digital decision support (DDS) is expected to play an important role in improving a physician's diagnostic performance and reducing the burden of diagnostic error. Studies with currently available DDS systems indicate that they lead to modest gains in diagnostic accuracy, and these systems are expected to evolve to become more effective and user-friendly in the future. In this position paper, we propose that a way towards this future is to rethink DDS systems based on deliberate reflection, a strategy by which physicians systematically review the clinical findings observed in a patient in the light of an initial diagnosis. Deliberate reflection has been demonstrated to improve diagnostic accuracy in several contexts. In this paper, we first describe the deliberate reflection strategy, including the crucial element that would make it useful in the interaction with a DDS system. We examine the nature of conventional DDS systems and their shortcomings. Finally, we propose what DDS based on deliberate reflection might look like, and consider why it would overcome downsides of conventional DDS.

A Framework for Clinicians to Improve the Decision-Making Process in Return to Sport

Return-to-sport (RTS) decisions are critical to clinical sports medicine and are often characterised by uncertainties, such as re-injury risk, time pressure induced by competition schedule and social stress from coaches, families and supporters. RTS decisions have implications not only for the health and performance of an athlete, but also the sports organisation. RTS decision-making is a complex process, which relies on evaluating multiple biopsychosocial factors, and is influenced by contextual factors. In this narrative review, we outline how RTS decision-making of clinicians could be evaluated from a decision analysis perspective. To begin with, the RTS decision could be explained as a sequence of steps, with a decision basis as the core component. We first elucidate the methodological considerations in gathering information from RTS tests. Second, we identify how decision-making frameworks have evolved and adapt decision-making theories to the RTS context. Third, we discuss the preferences and perspectives of the athlete, performance coach and manager. We conclude by proposing a framework for clinicians to improve the quality of RTS decisions and make recommendations for daily practice and research.

Clinical Reasoning Behind Antibiotic Use in PICUs: A Qualitative Study

OBJECTIVES

To describe the reasoning processes used by pediatric intensivists to make antibiotic-related decisions.

DESIGN

Grounded theory qualitative study.

SETTING

Three Canadian university-affiliated tertiary medical, surgical, and cardiac PICUs.

PATIENTS

Twenty-one PICU physicians.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We conducted field observation during morning rounds followed by semistructured interviews with participants to examine the clinical reasoning behind antibiotic-related decisions (starting/stopping antibiotics, or treatment duration) made for patients with a suspected/proven bacterial infection. We used a grounded theory approach for data collection and analysis. Thematic saturation was reached after 21 interviews. Of the 21 participants, 10 (48%) were female, 15 (71%) were PICU attending staff, and 10 (48%) had greater than 10 years in clinical practice. Initial clinical reasoning involves using an analytical approach to determine the likelihood of bacterial infection. In case of uncertainty, an assessment of patient safety is performed, which partly overlaps with the use of intuitive clinical reasoning. Finally, if uncertainty remains, physicians tend to consult infectious diseases experts. Factors that override this clinical reasoning process include disease severity, pressure from consultants, and the tendency to continue antibiotic treatment initiated by colleagues.

CONCLUSIONS

Antibiotic-related decisions for critically ill children are complex, and pediatric intensivists use several clinical reasoning strategies to decrease the uncertainty around the bacterial etiology of infections. However, disease severity and patient safety concerns may overrule decisions based on clinical evidence and lead to antibiotic use. Several cognitive biases were identified in the clinical reasoning processes.

When to give aspirin to prevent preeclampsia: application of Bayesian decision theory

BACKGROUND

There is good evidence that first-trimester assessment of the risk for preterm preeclampsia and treatment of the high-risk group with aspirin reduces the incidence of preterm preeclampsia. Furthermore, there is evidence that aspirin is associated with an increased risk of maternal and neonatal hemorrhagic complications. Against this background, there are ongoing debates whether aspirin should be recommended for all women or to a subpopulation of women predicted to be at increased risk of developing preeclampsia. Moreover, if a strategy of the prediction and prevention of preterm preeclampsia is to be used, what method should be used for the prediction, and what risk cutoff should be used to decide on who to treat?

OBJECTIVE

This study aimed to compare the policies of universal treatment, stratified treatment, and no treatment with aspirin.

STUDY DESIGN

Decisions about aspirin prophylaxis were considered from the perspective of the Bayesian decision theory. Using this approach, the treatment policies were evaluated for risks of preterm preeclampsia, effects of aspirin, and trade-offs between the harms and benefits of the treatment. Evidence on the risk of preterm preeclampsia was taken from the Screening programme for pre-eclampsia study, which was a first-trimester screening study for the prediction of preeclampsia. Evidence of the effect of aspirin was taken from the Aspirin for Evidence-Based Preeclampsia Prevention trial, which was a trial of aspirin vs placebo in the prevention of preterm preeclampsia. The trade-off between the benefits and harms of aspirin was specified by addressing the question, "What is the maximum number of women that should be treated to prevent 1 case of preterm preeclampsia?" The number can be considered as an exchange rate between the harms and benefits of using aspirin to prevent preterm PE. Given the uncertainty about the harms associated with aspirin, the treatment policies were compared across a wide range of exchange rates.

RESULTS

For exchange rates between 10 and 1000 women treated with aspirin to prevent 1 case of preterm preeclampsia, the net benefit achieved from the risk assessment and targeted treatment of women at high risk of preterm preeclampsia was higher than that from women with no treatment or women with universal treatment with aspirin.

CONCLUSION

Universal treatment with aspirin should be avoided. Risk-based screening should be used, and the cutoff for taking aspirin should be determined from the consideration of the trade-off between the benefits and harms and detection, false-positive, and screen-positive rates.

Multi-Criteria Decision Analysis for Benefit-Risk Analysis by National Regulatory Authorities

The approval process for pharmaceuticals has always included a consideration of the trade-offs between benefits and risks. Until recently, these trade-offs have been made in panel discussions without using a decision model to explicitly consider what these trade-offs might be. Recently, the EMA and the FDA have embraced Multi-Criteria Decision Analysis (MCDA) as a methodology for making approval decisions. MCDA offers an approach for improving the quality of these decisions and, in particular, by using quantitative and qualitative data in a structured decision model to make trade-offs in a logical, transparent and auditable way. This paper will review the recent use of MCDA by the FDA and EMA and recommend its wider adoption by other National Regulatory Authorities (NRAs) and the pharmaceutical industry.

Willingness to Treat with Therapies of Unknown Effectiveness in Severe COVID-19: A Survey of Intensivist Physicians

RATIONALE

Little is known about how physicians develop their beliefs about new treatments or update their beliefs in the face of new clinical evidence. These issues are particularly salient in the context of the COVID-19 pandemic, which created rapid demand for novel therapies in the absence of robust evidence.

OBJECTIVE

To identify psychological traits associated with physicians' willingness to treat with unproven therapies and willingness to update their treatment preferences in the setting of new evidence in the context of COVID-19.

METHODS

We administered a longitudinal e-mail survey to United States physicians board-certified in intensive care medicine in April and May, 2020 (phase one); and October and November, 2020 (phase two). We assessed five psychological traits potentially related to evidence-uptake: need for cognition, evidence skepticism, need for closure, risk tolerance, and research engagement. We then examined the relationship between these traits and physician preferences for pharmacological treatment for a hypothetical patient with severe COVID-19 pneumonia.

RESULTS

There were 592 responses to the phase one survey, conducted prior to publication of trial data. At this time physicians were most willing to treat with macrolide antibiotics (50.5%), followed by antimalaria agents (36.1%), corticosteroids (24.5%), antiretroviral agents (22.6%), and angiotensin inhibitors (4.4%). Greater evidence skepticism (relative risk, RR=1.40, 95% CI: 1.30 - 1.52, p<0.001), greater need for closure (RR=1.19, 95% CI: 1.06 - 1.34, p=0.003), and greater risk tolerance (RR=1.17, 95% CI: 1.08 - 1.26, p<0.001) were associated with an increased willingness to treat; while greater need for cognition (RR: 0.85, 95% CI: 0.75 - 0.96, p=0.010) and greater research engagement (RR=0.91, 95% CI: 0.88 - 0.95, p<.0001) were associated with decreased willingness to treat. In phase two, most physicians updated their beliefs after publication of trial data about antimalarial agents and corticosteroids. Physicians with greater evidence skepticism more likely to persist in their beliefs.

CONCLUSIONS

Psychological traits associated with clinical decisions in the setting of uncertain evidence may provide insight into strategies to better align clinical practice with published evidence.

Bayes Theorem and Protopathic Bias: Methodological Concerns When Addressing the Impact of Fetal Heart Rate Patterns on the Cesarean Section Rate

Over the last 30 years, the caesarean section rate has reached global epidemic proportions. This trend is driven by multiple factors, an important one of which is the use and inconsistent interpretation of the electronic fetal monitoring (EFM) system. Despite its introduction in the 1960s, the EFM has not definitively improved neonatal outcomes, yet it has since significantly contributed to a seven-fold increase in the caesarean section rate. As we attempt to reduce the caesarean rates in the developed world, we should consider focusing on areas that have garnered little attention in the literature, such as physician sensitization to the poor predictive power of the EFM and the research method biases that are involved in studying the abnormal heart rate patterns-umbilical cord pH relationship. Herein, we apply Bayes theorem to different clinical scenarios to illustrate the poor predictive power of the EFM, as well as shed light on the principle of protopathic bias, which affects the classification of research outcomes among studies addressing the effects of the EFM on caesarean rates. We propose and discuss potential solutions to the aforementioned considerations, which include the re-examination of guidelines with which we interpret fetal heart rate patterns and the development of noninvasive technologies that evaluate fetal pH in real time.

Bayesian Methods in the Field of Rehabilitation

Bayesian techniques, as an alternative method of statistical analysis in rehabilitation studies, have some advantages such as handling small sample sizes, allowing incorporation of previous experience of the researchers or clinicians, being suitable for different kinds of studies, and managing highly complex models. These characteristics are important in rehabilitation research. In the present article, the Bayesian approach is displayed through three examples in previously analyzed data with traditional or frequentist methods. The studies used as examples have small sample sizes and show that the Bayesian procedures enhance the statistical information of the results. The Bayesian credibility interval includes the true value of the corresponding parameter diminishing uncertainty about the treatment effect. In addition, the Bayes factor value quantifies the evidence provided by the data in favor of the alternative hypothesis as opposed to the null hypothesis. Bayesian inference could be an interesting and adaptable alternative statistical method for physical medicine and rehabilitation applications.

The Bayesian Approach to Decision Making and Analysis in Nutrition Research and Practice

This is part of a series of monographs on research design and analysis. The purpose of this article is to describe the purposes of and approach to conducting Bayesian decision making and analysis. Bayesian decision making involves basing decisions on the probability of a successful outcome, where this probability is informed by both prior information and new evidence the decision maker obtains. The statistical analysis that underlies the calculation of these probabilities is Bayesian analysis. In recent years, the Bayesian approach has been applied more commonly in both nutrition research and clinical decision making, and registered dietitian nutritionists would benefit from gaining a deeper understanding of this approach. This article provides a background of Bayesian decision making and analysis, and then presents applications of the approach in two different areas-medical diagnoses and nutrition policy research. It concludes with a description of how Bayesian decision making may be used in everyday life to allow each of us to appropriately weigh established beliefs and prior knowledge with new data and information in order to make well-informed and wise decisions.

Paramedic point of care ultrasound at Australian mass gatherings

Point of care ultrasound (POCUS) is not traditionally performed by paramedics, and where it is used, is generally limited to resuscitative-type ultrasound examinations. We describe a select series of patient care cases collected between August 2017 and February 2018 which are the first known examples of expanded POCUS performed by a paramedic in this context. These point of care scans were performed for both high and lower acuity patient presentations and are felt to have contributed to improved decision-making in the treatment and onward referral of patients in the Australian festival and event medicine.

Predictive probability of success using surrogate endpoints

The predictive probability of success of a future clinical trial is a key quantitative tool for decision-making in drug development. It is derived from prior knowledge and available evidence, and the latter typically comes from the accumulated data on the clinical endpoint of interest in previous clinical trials. However, a surrogate endpoint could be used as primary endpoint in early development and, usually, no or limited data are collected on the clinical endpoint of interest. We propose a general, reliable, and broadly applicable methodology to predict the success of a future trial from surrogate endpoints, in a way that makes the best use of all the available evidence. The predictions are based on an informative prior, called surrogate prior, derived from the results of past trials on one or several surrogate endpoints. If available, in a Bayesian framework, this prior could be combined with data from past trials on the clinical endpoint of interest. Two methods are proposed to address a potential discordance between the surrogate prior and the data on the clinical endpoint. We investigate the patterns of behavior of the predictions in a comprehensive simulation study, and we present an application to the development of a drug in Multiple Sclerosis. The proposed methodology is expected to support decision-making in many different situations, since the use of predictive markers is important to accelerate drug developments and to select promising drug candidates, better and earlier.

CDSS-RM: a clinical decision support system reference model

Clinical Decision Support Systems (CDSS) provide aid in clinical decision making and therefore need to take into consideration human, data interactions, and cognitive functions of clinical decision makers. The objective of this paper is to introduce a high level reference model that is intended to be used as a foundation to design successful and contextually relevant CDSS systems. The paper begins by introducing the information flow, use, and sharing characteristics in a hospital setting, and then it outlines the referential context for the model, which are clinical decisions in a hospital setting. Important characteristics of the Clinical decision making process include: (i) Temporally ordered steps, each leading to new data, which in turn becomes useful for a new decision, (ii) Feedback loops where acquisition of new data improves certainty and generates new questions to examine, (iii) Combining different kinds of clinical data for decision making, (iv) Reusing the same data in two or more different decisions, and (v) Clinical decisions requiring human cognitive skills and knowledge, to process the available information. These characteristics form the foundation to delineate important considerations of Clinical Decision Support Systems design. The model includes six interacting and interconnected elements, which formulate the high-level reference model (CDSS-RM). These elements are introduced in the form of questions, as considerations, and are examined with the use of illustrated scenario-based and data-driven examples. The six elements /considerations of the reference model are: (i) Do CDSS mimic the cognitive process of clinical decision makers? (ii) Do CDSS provide recommendations with longitudinal insight? (iii) Is the model performance contextually realistic? (iv) Is the ‘Historical Decision’ bias taken into consideration in CDSS design? (v) Do CDSS integrate established clinical standards and protocols? (vi) Do CDSS utilize unstructured data? The CDSS-RM reference model can contribute to optimized design of modeling methodologies, in order to improve response of health systems to clinical decision-making challenges.

A Bayesian decision analysis approach to assess voice disorder risks by using acoustic features

Vocal fold nodules are recognized as an occupational disease for all collective of workers performing activities for which maintained and continued use of voice is required. Computer-aided systems based on features extracted from voice recordings have been considered as potential noninvasive and low cost tools to diagnose some voice-related diseases. A Bayesian decision analysis approach has been proposed to classify university lectures in three levels of risk: low, medium, and high, based on the information provided by acoustic features extracted from healthy controls and people suffering from vocal fold nodules. The proposed risk groups are associated with different treatments. The approach is based on the calculation of posterior probabilities of developing vocal fold nodules and considers utility functions that include the financial cost and the probability of recovery for the corresponding treatment. Maximization of the expected utilities is considered. By using this approach, the risk of having vocal fold nodules is identified for each university lecturer, so he/she can be properly assigned to the right treatment. The approach has been applied to university lecturers according to the Disease Prevention Program of the University of Extremadura. However, it can also be applied to other voice professionals (singers, speakers, coaches, actors…).

Periodic benefit-risk assessment using Bayesian stochastic multi-criteria acceptability analysis

Benefit-risk (BR) assessment is essential to ensure the best decisions are made for a medical product in the clinical development process, regulatory marketing authorization, post-market surveillance, and coverage and reimbursement decisions. One challenge of BR assessment in practice is that the benefit and risk profile may keep evolving while new evidence is accumulating. Regulators and the International Conference on Harmonization (ICH) recommend performing periodic benefit-risk evaluation report (PBRER) through the product's lifecycle. In this paper, we propose a general statistical framework for periodic benefit-risk assessment, in which Bayesian meta-analysis and stochastic multi-criteria acceptability analysis (SMAA) will be combined to synthesize the accumulating evidence. The proposed approach allows us to compare the acceptability of different drugs dynamically and effectively and accounts for the uncertainty of clinical measurements and imprecise or incomplete preference information of decision makers. We apply our approaches to two real examples in a post-hoc way for illustration purpose. The proposed method may easily be modified for other pre and post market settings, and thus be an important complement to the current structured benefit-risk assessment (sBRA) framework to improve the transparent and consistency of the decision-making process.

A Growing Consensus for Change in Interpretation of Clinical Research Evidence

CONTEXT

  The paradigm of evidence-based practice (EBP) is well established among the health care professions, but perspectives on the best methods for acquiring, analyzing, appraising, and using research evidence are evolving.

BACKGROUND

  The EBP paradigm has shifted away from a hierarchy of research-evidence quality to recognize that multiple research methods can yield evidence to guide clinicians and patients through a decision-making process. Whereas the "frequentist" approach to data interpretation through hypothesis testing has been the dominant analytical method used by and taught to athletic training students and scholars, this approach is not optimal for integrating evidence into routine clinical practice. Moreover, the dichotomy of rejecting, or failing to reject, a null hypothesis is inconsistent with the Bayesian-like clinical decision-making process that skilled health care providers intuitively use. We propose that data derived from multiple research methods can be best interpreted by reporting a credible lower limit that represents the smallest treatment effect at a specified level of certainty, which should be judged in relation to the smallest effect considered to be clinically meaningful. Such an approach can provide a quantifiable estimate of certainty that an individual patient needs follow-up attention to prevent an adverse outcome or that a meaningful level of therapeutic benefit will be derived from a given intervention.

CONCLUSIONS

  The practice of athletic training will be influenced by the evolution of the EBP paradigm. Contemporary practice will require clinicians to expand their critical-appraisal skills to effectively integrate the results derived from clinical research into the care of individual patients. Proper interpretation of a credible lower limit value for a magnitude ratio has the potential to increase the likelihood of favorable patient outcomes, thereby advancing the practice of evidence-based athletic training.

Comparison of sperm motility subpopulation structure among wild anadromous and farmed male Atlantic salmon (Salmo salar) parr using a CASA system

Atlantic salmon (Salmo salar) is an endangered freshwater species that needs help to recover its wild stocks. However, the priority in aquaculture is to obtain successful fertilisation and genetic variability to secure the revival of the species. The aims of the present work were to study sperm subpopulation structure and motility patterns in wild anadromous males and farmed male Atlantic salmon parr. Salmon sperm samples were collected from wild anadromous salmon (WS) and two generations of farmed parr males. Sperm samples were collected from sexually mature males and sperm motility was analysed at different times after activation (5 and 35 s). Differences among the three groups were analysed using statistical techniques based on Cluster analysis the Bayesian method. Atlantic salmon were found to have three sperm subpopulations, and the spermatozoa in ejaculates of mature farmed parr males had a higher velocity and larger size than those of WS males. This could be an adaptation to high sperm competition because salmonid species are naturally adapted to this process. Motility analysis enables us to identify sperm subpopulations, and it may be useful to correlate these sperm subpopulations with fertilisation ability to test whether faster-swimming spermatozoa have a higher probability of success.

A statistical framework for applying RNA profiling to chemical hazard detection

Use of 'omics technologies in environmental science is expanding. However, application is mostly restricted to characterizing molecular steps leading from toxicant interaction with molecular receptors to apical endpoints in laboratory species. Use in environmental decision-making is limited, due to difficulty in elucidating mechanisms in sufficient detail to make quantitative outcome predictions in any single species or in extending predictions to aquatic communities. Here we introduce a mechanism-agnostic statistical approach, supplementing mechanistic investigation by allowing probabilistic outcome prediction even when understanding of molecular pathways is limited, and facilitating extrapolation from results in laboratory test species to predictions about aquatic communities. We use concepts familiar to environmental managers, supplemented with techniques employed for clinical interpretation of 'omics-based biomedical tests. We describe the framework in step-wise fashion, beginning with single test replicates of a single RNA variant, then extending to multi-gene RNA profiling, collections of test replicates, and integration of complementary data. In order to simplify the presentation, we focus on using RNA profiling for distinguishing presence versus absence of chemical hazards, but the principles discussed can be extended to other types of 'omics measurements, multi-class problems, and regression. We include a supplemental file demonstrating many of the concepts using the open source R statistical package.

Evaluation of safety and effectiveness of factor VIII treatment in haemophilia A patients with low titre inhibitors or a personal history of inhibitor

There is no prospective evidence on inhibitor recurrence among haemophilia A patients with low titre inhibitors or history of inhibitors, and whether or how therapeutic choices affect the risk of recurrence. The aims of this study were to synthesise safety data in patients with moderate-severe haemophilia A and with low titre inhibitors or inhibitor history enrolled in the rAHF PFM (ADVATE) – Post-Authorization Safety Studies (ADVATE-PASS) international programme. The study was conducted in clinics participating to the ADVATE PASS programme. The patient population consisted of patients entering the studies with low titre (≤5 BU) inhibitors or a positive personal history of inhibitors. Patients on Immune Tolerance Induction at study entry were excluded. Primary outcome was new or recurrent inhibitor titre > 5 BU. Secondary outcomes were any increase of inhibitor titre not reaching 5 BU; any unexplained change in treatment regimen. Primary analysis was done by two-stage random effects meta-analysis. Secondary analysis was done by a hierarchical Bayesian random effects logistic model. A total of 219 patients from seven studies were included. Of these 214 (97.7 %) patients had been previously treated for more than 50 exposure days. Two hundred ten patients had positive history for inhibitors, nine a baseline measurable titre. No patient presented a primary outcome event (95 % confidence interval [CI] 0–1.6 %). Six patients with previous history developed a low titre recurrence (overall rate 2.2, 95 %CI 0–4.8 %). When any increase of inhibitor titre or any treatment change was accounted for, overall 3.7 % (95 % CI 0 %-8.0 %) of patients experienced the outcome. In conclusion, the observed rate of events does not support the definition of this population as at high risk for inhibitor development.

Efficient and effective precepting of pharmacy students in acute and ambulatory care rotations: A Delphi expert panel study

PURPOSE

Using the Delphi process, a panel of experienced preceptors achieved consensus on best practices to increase preceptor efficiency and effectiveness.

METHODS

The Delphi panelists completed 3 survey rounds and a face-to-face meeting. Survey questions covered several topics, including preparation of students for rotations, preceptor efficiency and effectiveness, potential resident contributions to precepting, methods of developing critical-thinking skills and providing assessment and feedback, precepting time metrics, and barriers to preceptor effectiveness. Panel consensus was defined as agreement of ≥80%.

RESULTS

Fifteen of 36 invited preceptors (42%) completed all 3 survey rounds. The expert panel reached consensus on 6 essentials for effective rotations, 8 precepting contributions that could be made by appropriately trained residents, precepting barriers, 4 strategies for teaching critical thinking, and 5 valuable characteristics of the One Minute Preceptor model. Panelists reported on time spent with students presenting new patient cases (median, 10 minutes per case), time devoted to assessment of students' clinical performance (median, 22 minutes per student weekly), and time dedicated to student professional development (median, 20 minutes per student weekly).

CONCLUSION

Important strategies for preceptors identified by the panel included (1) a thorough orientation to logistics, expectations, and scheduling of activities, (2) using appropriately trained residents in student training, (3) providing opportunities for critical thinking and therapeutic decision-making, (4) giving frequent, quality feedback on clinical activities, and (5) giving feedback to learners on a regular basis.

Evidence-based assessment of ADHD diagnosis in children and adolescents

This study illustrates the accuracy and efficiency of using an evidence-based assessment (EBA) strategy for diagnosis of attention-deficit/hyperactivity disorder (ADHD) by integrating the scale scores obtained on BASC-3 teacher and parent rating scales. The examined process used empirical diagnostic likelihood ratios (DLRs) derived from a sample of children with ADHD (N = 339) matched on demographic characteristics from the normative sample. The results show that behavioral scales of executive functioning and functional communication provided incremental utility in ADHD diagnosis. With a revised probability of .80 or higher as the diagnostic criterion, teachers, and parents positively diagnosed 70% and 94% of the ADHD cases respectively. The EBA approach was efficient, with four scales on average used to reach the proposed posterior probability for final diagnosis. Finally, teachers and parents demonstrated a high agreement with respect to the diagnosis results and scales used for the diagnosis.

The Case for a Bayesian Approach to Benefit-Risk Assessment:: Overview and Future Directions

The benefit-risk assessment of a new medicinal product or intervention is one of the most complex tasks that sponsors, regulators, payers, physicians, and patients face. Therefore, communicating the trade-off of benefits and risks in a clear and transparent manner, using all available evidence, is critical to ensure that the best decisions are made. Several quantitative methods have been proposed in recent years that try to provide insight into this challenging problem. Bayesian inference, with its coherent approach for integrating different sources of information and uncertainty, along with its links to optimal decision theory, provides a natural framework to perform quantitative assessments of the benefit-risk trade-off. This paper describes the current state of the art in Bayesian methodologies for quantitative benefit-risk assessment, and how these may be leveraged throughout the life cycle of a medicinal product to support and augment clinical judgment and qualitative benefit-risk assessments. Gaps and potential new directions that extend the current approaches are also identified.

A simple way to unify multicriteria decision analysis (MCDA) and stochastic multicriteria acceptability analysis (SMAA) using a Dirichlet distribution in benefit-risk assessment

Quantitative methodologies have been proposed to support decision making in drug development and monitoring. In particular, multicriteria decision analysis (MCDA) and stochastic multicriteria acceptability analysis (SMAA) are useful tools to assess the benefit-risk ratio of medicines according to the performances of the treatments on several criteria, accounting for the preferences of the decision makers regarding the relative importance of these criteria. However, even in its probabilistic form, MCDA requires the exact elicitations of the weights of the criteria by the decision makers, which may be difficult to achieve in practice. SMAA allows for more flexibility and can be used with unknown or partially known preferences, but it is less popular due to its increased complexity and the high degree of uncertainty in its results. In this paper, we propose a simple model as a generalization of MCDA and SMAA, by applying a Dirichlet distribution to the weights of the criteria and by making its parameters vary. This unique model permits to fit both MCDA and SMAA, and allows for a more extended exploration of the benefit-risk assessment of treatments. The precision of its results depends on the precision parameter of the Dirichlet distribution, which could be naturally interpreted as the strength of confidence of the decision makers in their elicitation of preferences.

Training Pathology Residents to Practice 21st Century Medicine: A Proposal

Scientific advances, open information access, and evolving health-care economics are disrupting extant models of health-care delivery. Physicians increasingly practice as team members, accountable to payers and patients, with improved efficiency, value, and quality. This change along with a greater focus on population health affects how systems of care are structured and delivered. Pathologists are not immune to these disruptors and, in fact, may be one of the most affected medical specialties. In the coming decades, it is likely that the number of practicing pathologists will decline, requiring each pathologist to serve more and often sicker patients. The demand for increasingly sophisticated yet broader diagnostic skills will continue to grow. This will require pathologists to acquire appropriate professional training and interpersonal skills. Today’s pathology training programs are ill designed to prepare such practitioners. The time to practice for most pathology trainees is typically 5 to 6 years. Yet, trainees often lack sufficient experience to practice independently and effectively. Many studies have recognized these challenges suggesting that more effective training for this new century can be implemented. Building on the strengths of existing programs, we propose a redesign of pathology residency training that will meet (and encourage) a continuing evolution of American Board of Pathology and Accreditation Council for Graduate Medical Education requirements, reduce the time to readiness for practice, and produce more effective, interactive, and adaptable pathologists. The essence of this new model is clear definition and acquisition of core knowledge and practice skills that span the anatomic and clinical pathology continuum during the first 2 years, assessed by competency-based metrics with emphasis on critical thinking and skill acquisition, followed by individualized modular training with intensively progressive responsibility during the final years of training. We anticipate that implementing some or all aspects of this model will enable residents to attain a higher level of competency within the current time-based constraints of residency training.

Diabetes care related process modelling using Process Mining techniques. Lessons learned in the application of Interactive Pattern Recognition: coping with the Spaghetti Effect

Diabetes is one of the metabolic disorders with more growth expectations in next decades. The literature points to a correct self-management, to an appropriate treatment and to an adequate healthy lifestyle as a way to dramatically improve the quality of life of patients with diabetes. The implementation of a holistic diabetes care system, using rising information technologies for deploying cares based on the thesis of the Evidence-Based Medicine can be a effective solution to provide an adequate and continuous care to patients. However, the design and deployment of computer readable careflows is not a easy task. In this paper, we propose the use of Interactive Pattern Recognition techniques for the iterative design of those protocols and we analyze the problems of using Process Mining to infer careflows and how to how to cope with the resulting Spaghetti Effect.

Relationship between Student Pharmacist Decision Making Preferences and Experiential Learning

Objective. To determine if student pharmacists' preferences towards experiential and rational thinking are associated with performance on advanced pharmacy practice experiences (APPEs) and whether thinking style preference changes following APPEs. Methods. The Rational Experiential Inventory (REI), a validated survey of thinking style, was administered to student pharmacists before starting APPEs and re-administered after completing APPEs. APPE grades were compared to initial REI scores. Results. Rational Experiential Inventory scores remained consistent before and after APPEs. Overall, APPE grades were independent of REI scores. In a regression model, the REI experiential score was a significant negative predictor of hospital APPE grades. Conclusion. These findings suggest that overall APPE performance is independent of decision-making preference, and decision-making style does not change following immersion into APPEs. Instead of targeting teaching strategies towards a specific decision-making style, preceptors may use pedagogical approaches that promote sound clinical decision-making skills through critical thinking and reflection.

Where's the utility in Bayesian data-monitoring of clinical trials?

Background Data monitoring is now an established part of good practice in clinical trials. Bayesian procedures for data-monitoring of treatment trials have been proposed and used, but sometimes without explicit consideration of utilities. A natural statistical framework for evidence-based medicine is a Bayesian approach to decision-making that incorporates an integrated summary of the available evidence and associated uncertainty with assessment of utilities.

Methods We explore this approach to data monitoring, explicitly addressing separately the individual, scientific and public health perspectives. The Data Monitoring Committee's decision can then be thought of as a weighted combination of these perspectives. These ideas are illustrated with a trial of treatments for oesophageal cancer.

Results For a Bayesian approach without explicit utilities we show that a utility structure is, in fact, implicit, and that it may be viewed as a weighted sum of the individual and scientific utilities.

Conclusions We argue that explicit consideration of utilities leads to decisionmaking that is more transparent, and lays foundations for data monitoring of more complex trials.

Inhaled Cannabis for Chronic Neuropathic Pain: A Meta-analysis of Individual Patient Data

Chronic neuropathic pain, the most frequent condition affecting the peripheral nervous system, remains underdiagnosed and difficult to treat. Inhaled cannabis may alleviate chronic neuropathic pain. Our objective was to synthesize the evidence on the use of inhaled cannabis for chronic neuropathic pain. We performed a systematic review and a meta-analysis of individual patient data. We registered our protocol with PROSPERO CRD42011001182. We searched in Cochrane Central, PubMed, EMBASE, and AMED. We considered all randomized controlled trials investigating chronic painful neuropathy and comparing inhaled cannabis with placebo. We pooled treatment effects following a hierarchical random-effects Bayesian responder model for the population-averaged subject-specific effect. Our evidence synthesis of individual patient data from 178 participants with 405 observed responses in 5 randomized controlled trials following patients for days to weeks provides evidence that inhaled cannabis results in short-term reductions in chronic neuropathic pain for 1 in every 5 to 6 patients treated (number needed to treat = 5.6 with a Bayesian 95% credible interval ranging between 3.4 and 14). Our inferences were insensitive to model assumptions, priors, and parameter choices. We caution that the small number of studies and participants, the short follow-up, shortcomings in allocation concealment, and considerable attrition limit the conclusions that can be drawn from the review. The Bayes factor is 332, corresponding to a posterior probability of effect of 99.7%.

PERSPECTIVE

This novel Bayesian meta-analysis of individual patient data from 5 randomized trials suggests that inhaled cannabis may provide short-term relief for 1 in 5 to 6 patients with neuropathic pain. Pragmatic trials are needed to evaluate the long-term benefits and risks of this treatment.

Integration of PKPD relationships into benefit-risk analysis

AIM

Despite the continuous endeavour to achieve high standards in medical care through effectiveness measures, a quantitative framework for the assessment of the benefit-risk balance of new medicines is lacking prior to regulatory approval. The aim of this short review is to summarise the approaches currently available for benefit-risk assessment. In addition, we propose the use of pharmacokinetic-pharmacodynamic (PKPD) modelling as the pharmacological basis for evidence synthesis and evaluation of novel therapeutic agents.

METHODS

A comprehensive literature search has been performed using MESH terms in PubMed, in which articles describing benefit-risk assessment and modelling and simulation were identified. In parallel, a critical review of multi-criteria decision analysis (MCDA) is presented as a tool for characterising a drug's safety and efficacy profile.

RESULTS

A definition of benefits and risks has been proposed by the European Medicines Agency (EMA), in which qualitative and quantitative elements are included. However, in spite of the value of MCDA as a quantitative method, decisions about benefit-risk balance continue to rely on subjective expert opinion. By contrast, a model-informed approach offers the opportunity for a more comprehensive evaluation of benefit-risk balance before extensive evidence is generated in clinical practice.

CONCLUSIONS

Benefit-risk balance should be an integral part of the risk management plan and as such considered before marketing authorisation. Modelling and simulation can be incorporated into MCDA to support the evidence synthesis as well evidence generation taking into account the underlying correlations between favourable and unfavourable effects. In addition, it represents a valuable tool for the optimization of protocol design in effectiveness trials.

Comparative clinical effectiveness of management strategies for sciatica: systematic review and network meta-analyses

BACKGROUND

There are numerous treatment approaches for sciatica. Previous systematic reviews have not compared all these strategies together.

PURPOSE

To compare the clinical effectiveness of different treatment strategies for sciatica simultaneously.

STUDY DESIGN

Systematic review and network meta-analysis.

METHODS

We searched 28 electronic databases and online trial registries, along with bibliographies of previous reviews for comparative studies evaluating any intervention to treat sciatica in adults, with outcome data on global effect or pain intensity. Network meta-analysis methods were used to simultaneously compare all treatment strategies and allow indirect comparisons of treatments between studies. The study was funded by the UK National Institute for Health Research Health Technology Assessment program; there are no potential conflict of interests.

RESULTS

We identified 122 relevant studies; 90 were randomized controlled trials (RCTs) or quasi-RCTs. Interventions were grouped into 21 treatment strategies. Internal and external validity of included studies was very low. For overall recovery as the outcome, compared with inactive control or conventional care, there was a statistically significant improvement following disc surgery, epidural injections, nonopioid analgesia, manipulation, and acupuncture. Traction, percutaneous discectomy, and exercise therapy were significantly inferior to epidural injections or surgery. For pain as the outcome, epidural injections and biological agents were significantly better than inactive control, but similar findings for disc surgery were not statistically significant. Biological agents were significantly better for pain reduction than bed rest, nonopioids, and opioids. Opioids, education/advice alone, bed rest, and percutaneous discectomy were inferior to most other treatment strategies; although these findings represented large effects, they were statistically equivocal.

CONCLUSIONS

For the first time, many different treatment strategies for sciatica have been compared in the same systematic review and meta-analysis. This approach has provided new data to assist shared decision-making. The findings support the effectiveness of nonopioid medication, epidural injections, and disc surgery. They also suggest that spinal manipulation, acupuncture, and experimental treatments, such as anti-inflammatory biological agents, may be considered. The findings do not provide support for the effectiveness of opioid analgesia, bed rest, exercise therapy, education/advice (when used alone), percutaneous discectomy, or traction. The issue of how best to estimate the effectiveness of treatment approaches according to their order within a sequential treatment pathway remains an important challenge.

Bayesian methodology for the design and interpretation of clinical trials in critical care medicine: a primer for clinicians

OBJECTIVES

To review Bayesian methodology and its utility to clinical decision making and research in the critical care field.

DATA SOURCE AND STUDY SELECTION

Clinical, epidemiological, and biostatistical studies on Bayesian methods in PubMed and Embase from their inception to December 2013.

DATA SYNTHESIS

Bayesian methods have been extensively used by a wide range of scientific fields, including astronomy, engineering, chemistry, genetics, physics, geology, paleontology, climatology, cryptography, linguistics, ecology, and computational sciences. The application of medical knowledge in clinical research is analogous to the application of medical knowledge in clinical practice. Bedside physicians have to make most diagnostic and treatment decisions on critically ill patients every day without clear-cut evidence-based medicine (more subjective than objective evidence). Similarly, clinical researchers have to make most decisions about trial design with limited available data. Bayesian methodology allows both subjective and objective aspects of knowledge to be formally measured and transparently incorporated into the design, execution, and interpretation of clinical trials. In addition, various degrees of knowledge and several hypotheses can be tested at the same time in a single clinical trial without the risk of multiplicity. Notably, the Bayesian technology is naturally suited for the interpretation of clinical trial findings for the individualized care of critically ill patients and for the optimization of public health policies.

CONCLUSIONS

We propose that the application of the versatile Bayesian methodology in conjunction with the conventional statistical methods is not only ripe for actual use in critical care clinical research but it is also a necessary step to maximize the performance of clinical trials and its translation to the practice of critical care medicine.

Decisions about knowledge in medical practice: the effect of temporal features of a task

A classic question of social science is how knowledge informs practice. Research on physicians' decisions about medical knowledge has focused on doctors' personal capabilities and features of the knowledge corpus, producing divergent findings. This study asks, instead, How is decision making about the use of knowledge influenced by features of work? From observations of one team's decisions in multiple clinical and administrative contexts, the author argues that making decisions is contingent upon temporal features of physicians' tasks. Physicians receive feedback at different speeds, and they must account for these speeds when judging what they can prioritize. This finding explains doctors' perceived uncertainty in other studies as a product of the long feedback loop in tasks, and their certainty or pragmatism as a product of shorter feedback loops. In these latter scenario's, physicians consider and deploy scientific knowledge after--and not before, as is usually assumed--determining a fruitful plan of action.

Combined benefit of prediction and treatment: a criterion for evaluating clinical prediction models

Clinical treatment decisions rely on prognostic evaluation of a patient’s future health outcomes. Thus, predictive models under different treatment options are key factors for making good decisions. While many criteria exist for judging the statistical quality of a prediction model, few are available to measure its clinical utility. As a consequence, we may find that the addition of a clinical covariate or biomarker improves the statistical quality of the model, but has little effect on its clinical usefulness. We focus on the setting where a treatment decision may reduce a patient’s risk of a poor outcome, but also comes at a cost; this may be monetary, inconvenience, or the potential side effects. This setting is exemplified by cancer chemoprevention, or the use of statins to reduce the risk of cardiovascular disease. We propose a novel approach to assessing a prediction model using a formal decision analytic framework. We combine the predictive model’s ability to discriminate good from poor outcome with the net benefit afforded by treatment. In this framework, reduced risk is balanced against the cost of treatment. The relative cost–benefit of treatment provides a useful index to assist patient decisions. This index also identifies the relevant clinical risk regions where predictive improvement is needed. Our approach is illustrated using data from a colorectal adenoma chemoprevention trial.

Assessing probability of malignancy in solid solitary pulmonary nodules with a new Bayesian calculator: improving diagnostic accuracy by means of expanded and updated features

OBJECTIVES

A crucial point in the work-up of a solitary pulmonary nodule (SPN) is to accurately characterise the lesion on the basis of imaging and clinical data available. We introduce a new Bayesian calculator as a tool to assess and grade SPN risk of malignancy.

METHODS

A set of 343 consecutive biopsy or interval proven SPNs was used to develop a calculator to predict SPN probability of malignancy. The model was validated on the study population in a "round-robin" fashion and compared with results obtained from current models described in literature.

RESULTS

In our case series, receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) of 0.893 for the proposed model and 0.795 for its best competitor, which was the Gurney calculator. Using observational thresholds of 5% and 10% our model returned fewer false-negative results, while showing constant superiority in avoiding false-positive results for each surgical threshold tested. The main downside of the proposed calculator was a slightly higher proportion of indeterminate SPNs.

CONCLUSIONS

We believe the proposed model to be an important update of current Bayesian analysis of SPNs, and to allow for better discrimination between malignancies and benign entities on the basis of clinical and imaging data.

KEY POINTS

• Bayesian analysis can help characterise solitary pulmonary nodules • Volume doubling time (VDT) is a good predictor of malignancy • A VDT of between 25 and 400 days is highly suggestive of malignancy • Nodule size, enhancement, morphology and VDT are the best predictors of malignancy.

Balancing benefit and risk of medicines: a systematic review and classification of available methodologies

BACKGROUND

The need for formal and structured approaches for benefit-risk assessment of medicines is increasing, as is the complexity of the scientific questions addressed before making decisions on the benefit-risk balance of medicines. We systematically collected, appraised and classified available benefit-risk methodologies to facilitate and inform their future use.

METHODS

A systematic review of publications identified benefit-risk assessment methodologies. Methodologies were appraised on their fundamental principles, features, graphical representations, assessability and accessibility. We created a taxonomy of methodologies to facilitate understanding and choice.

RESULTS

We identified 49 methodologies, critically appraised and classified them into four categories: frameworks, metrics, estimation techniques and utility survey techniques. Eight frameworks describe qualitative steps in benefit-risk assessment and eight quantify benefit-risk balance. Nine metric indices include threshold indices to measure either benefit or risk; health indices measure quality-of-life over time; and trade-off indices integrate benefits and risks. Six estimation techniques support benefit-risk modelling and evidence synthesis. Four utility survey techniques elicit robust value preferences from relevant stakeholders to the benefit-risk decisions.

CONCLUSIONS

Methodologies to help benefit-risk assessments of medicines are diverse and each is associated with different limitations and strengths. There is not a 'one-size-fits-all' method, and a combination of methods may be needed for each benefit-risk assessment. The taxonomy introduced herein may guide choice of adequate methodologies. Finally, we recommend 13 of 49 methodologies for further appraisal for use in the real-life benefit-risk assessment of medicines.

Biostatistical and medical statistics graduate education

The development of graduate education in biostatistics and medical statistics is discussed in the context of training within a medical center setting. The need for medical researchers to employ a wide variety of statistical designs in clinical, genetic, basic science and translational settings justifies the ongoing integration of biostatistical training into medical center educational settings and informs its content. The integration of large data issues are a challenge.

Incorporating uncertainty regarding applicability of evidence from meta-analyses into clinical decision making

OBJECTIVES

Judging applicability (relevance) of meta-analytical findings to particular clinical decision-making situations remains challenging. We aimed to describe an evidence synthesis method that accounts for possible uncertainty regarding applicability of the evidence.

STUDY DESIGN AND SETTING

We conceptualized uncertainty regarding applicability of the meta-analytical estimates to a decision-making situation as the result of uncertainty regarding applicability of the findings of the trials that were included in the meta-analysis. This trial-level applicability uncertainty can be directly assessed by the decision maker and allows for the definition of trial inclusion probabilities, which can be used to perform a probabilistic meta-analysis with unequal probability resampling of trials (adaptive meta-analysis). A case study with several fictitious decision-making scenarios was performed to demonstrate the method in practice.

RESULTS

We present options to elicit trial inclusion probabilities and perform the calculations. The result of an adaptive meta-analysis is a frequency distribution of the estimated parameters from traditional meta-analysis that provides individually tailored information according to the specific needs and uncertainty of the decision maker.

CONCLUSION

The proposed method offers a direct and formalized combination of research evidence with individual clinical expertise and may aid clinicians in specific decision-making situations.

Applying evidence-based medicine in telehealth: an interactive pattern recognition approximation

Born in the early nineteen nineties, evidence-based medicine (EBM) is a paradigm intended to promote the integration of biomedical evidence into the physicians daily practice. This paradigm requires the continuous study of diseases to provide the best scientific knowledge for supporting physicians in their diagnosis and treatments in a close way. Within this paradigm, usually, health experts create and publish clinical guidelines, which provide holistic guidance for the care for a certain disease. The creation of these clinical guidelines requires hard iterative processes in which each iteration supposes scientific progress in the knowledge of the disease. To perform this guidance through telehealth, the use of formal clinical guidelines will allow the building of care processes that can be interpreted and executed directly by computers. In addition, the formalization of clinical guidelines allows for the possibility to build automatic methods, using pattern recognition techniques, to estimate the proper models, as well as the mathematical models for optimizing the iterative cycle for the continuous improvement of the guidelines. However, to ensure the efficiency of the system, it is necessary to build a probabilistic model of the problem. In this paper, an interactive pattern recognition approach to support professionals in evidence-based medicine is formalized.

Bayesian road safety analysis: incorporation of past evidence and effect of hyper-prior choice

PROBLEM

This paper aims to address two related issues when applying hierarchical Bayesian models for road safety analysis, namely: (a) how to incorporate available information from previous studies or past experiences in the (hyper) prior distributions for model parameters and (b) what are the potential benefits of incorporating past evidence on the results of a road safety analysis when working with scarce accident data (i.e., when calibrating models with crash datasets characterized by a very low average number of accidents and a small number of sites).

METHOD

A simulation framework was developed to evaluate the performance of alternative hyper-priors including informative and non-informative Gamma, Pareto, as well as Uniform distributions. Based on this simulation framework, different data scenarios (i.e., number of observations and years of data) were defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas.

RESULTS

This study shows how the accuracy of model parameter estimates (inverse dispersion parameter) is considerably improved when incorporating past evidence, in particular when working with the small number of observations and crash data with low mean. The results also illustrates that when the sample size (more than 100 sites) and the number of years of crash data is relatively large, neither the incorporation of past experience nor the choice of the hyper-prior distribution may affect the final results of a traffic safety analysis.

CONCLUSIONS

As a potential solution to the problem of low sample mean and small sample size, this paper suggests some practical guidance on how to incorporate past evidence into informative hyper-priors. By combining evidence from past studies and data available, the model parameter estimates can significantly be improved. The effect of prior choice seems to be less important on the hotspot identification.

IMPACT ON INDUSTRY

The results show the benefits of incorporating prior information when working with limited crash data in road safety studies.

Multivariate meta-analysis of mixed outcomes: a Bayesian approach

Multivariate random effects meta-analysis (MRMA) is an appropriate way for synthesizing data from studies reporting multiple correlated outcomes. In a Bayesian framework, it has great potential for integrating evidence from a variety of sources. In this paper, we propose a Bayesian model for MRMA of mixed outcomes, which extends previously developed bivariate models to the trivariate case and also allows for combination of multiple outcomes that are both continuous and binary. We have constructed informative prior distributions for the correlations by using external evidence. Prior distributions for the within-study correlations were constructed by employing external individual patent data and using a double bootstrap method to obtain the correlations between mixed outcomes. The between-study model of MRMA was parameterized in the form of a product of a series of univariate conditional normal distributions. This allowed us to place explicit prior distributions on the between-study correlations, which were constructed using external summary data. Traditionally, independent ‘vague’ prior distributions are placed on all parameters of the model. In contrast to this approach, we constructed prior distributions for the between-study model parameters in a way that takes into account the inter-relationship between them. This is a flexible method that can be extended to incorporate mixed outcomes other than continuous and binary and beyond the trivariate case. We have applied this model to a motivating example in rheumatoid arthritis with the aim of incorporating all available evidence in the synthesis and potentially reducing uncertainty around the estimate of interest. © 2013 The Authors. Statistics inMedicine Published by John Wiley & Sons, Ltd.

Serologic and bacteriologic culture prevalence of Corynebacterium pseudotuberculosis infection in goats and sheep and use of Bayesian analysis to determine value of assay results for prediction of future infection

OBJECTIVE

To determine the serologic and bacteriologic culture prevalence of Corynebacterium pseudotuberculosis infection in sheep and goats and the value of such assays for prediction of future development of caseous lymphadenitis (CL).

DESIGN

Observational study.

ANIMALS

919 goats and sheep in 3 herds in southwest Texas.

PROCEDURES

During an initial evaluation, serologic and bacteriologic culture status for CL was determined for all animals. Subsequently, animals were evaluated every 6 months for a 13-month period to detect external CL lesions. Affected animals in 2 herds were treated with tulathromycin or a control treatment; affected animals in 1 herd were culled. The value of assays for prediction of future development of CL lesions was determined.

RESULTS

The serologic prevalence of CL in herds at the start of the study ranged from 7.52% to 69.54%. The bacteriologic culture prevalence of CL ranged from 0% to 6.12% at the start of the study and 0% to 9.56% at the end of the study. Synergistic hemolysin inhibition results were poor predictors of future development of CL lesions in animals during the study period; however, animals with positive bacteriologic culture results for CL were more likely to develop lesions in the future than were animals with negative bacteriologic culture results.

CONCLUSIONS AND CLINICAL RELEVANCE

Caseous lymphadenitis was detected in animals in this study despite prior management of affected animals in herds via culling. Use of a synergistic hemolysin inhibition test for management of CL may cause unnecessary culling of animals; treatment might allow retention of genetically valuable CL-affected animals in a herd without substantially increasing the prevalence of CL.

Evaluation of a predevelopment service delivery intervention: an application to improve clinical handovers

Background

We developed a method to estimate the expected cost-effectiveness of a service intervention at the design stage and ‘road-tested’ the method on an intervention to improve patient handover of care between hospital and community.

Method

The development of a nine-step evaluation framework:

1. Identification of multiple endpoints and arranging them into manageable groups;

2. Estimation of baseline overall and preventable risk;

3. Bayesian elicitation of expected effectiveness of the planned intervention;

4. Assigning utilities to groups of endpoints;

5. Costing the intervention;

6. Estimating health service costs associated with preventable adverse events;

7. Calculating health benefits;

8. Cost-effectiveness calculation;

9. Sensitivity and headroom analysis.

Results

    Literature review suggested that adverse events follow 19% of patient discharges, and that one-third are preventable by improved handover (ie, 6.3% of all discharges). The intervention to improve handover would reduce the incidence of adverse events by 21% (ie, from 6.3% to 4.7%) according to the elicitation exercise. Potentially preventable adverse events were classified by severity and duration. Utilities were assigned to each category of adverse event. The costs associated with each category of event were obtained from the literature. The unit cost of the intervention was €16.6, which would yield a Quality Adjusted Life Year (QALY) gain per discharge of 0.010. The resulting cost saving was €14.3 per discharge. The intervention is cost-effective at approximately €214 per QALY under the base case, and remains cost-effective while the effectiveness is greater than 1.6%.

Conclusions

We offer a usable framework to assist in ex ante health economic evaluations of health service interventions.