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In evidence synthesis, inconsistency is typically assessed visually and with the I2 and the Q statistics. However, these measures have important limitations (i) if there are few primary studies of small sample sizes or (ii) if there are multiple studies with precise estimates. In addition, with the increasing use of decision thresholds (DT), for example in Grading of Recommendations Assessment, Development and Evaluation evidence to decision (EtD) frameworks, inconsistency judgments can be anchored around DTs. In this article, we developed quantitative measures to assess inconsistency based on DTs. We developed two measures to quantify inconsistency based on DTs – the decision inconsistency (DI) and the across-studies inconsistency (ASI) indices. The DI and the ASI are based on the distribution of the posterior samples studies’ effect sizes (ES) across interpretation categories defined by DTs. We developed these indices for the Bayesian context, followed by a frequentist extension. The DI informs on the overall inconsistency of ESs across interpretation categories, while the ASI quantifies how different studies are compared to each other (in relation to interpretation categories) based on absolute effects. A DI ≥ 50% and an ASI ≥ 25% are suggestive of important inconsistency. We provide an R package (metainc) and a web tool (https://metainc.med.up.pt/) to support the computation of the DI and ASI, including in the context of sensitivity analyses assessing the impact of potential uncertainty in inconsistency. The DI and the ASI can contribute to quantitatively assess inconsistency, particularly as DTs are gaining recognition in evidence synthesis and health decision-making. •Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessments of inconsistency are facilitated by decision thresholds (DT).•Two inconsistency indices have been developed to measure inconsistency based on DTs.•The new indices allow to assess the impact of uncertainty on inconsistency.

Background Decision thresholds play important role in medical decision-making. Individual decision-making differences may be attributable to differences in subjective judgments or cognitive processes that are captured through the decision thresholds. This systematic scoping review sought to characterize the literature on non-expected utility decision thresholds in medical decision-making by identifying commonly used theoretical paradigms and contextual and subjective factors that inform decision thresholds. Methods A structured search designed around three concepts—individual decision-maker, decision threshold, and medical decision—was conducted in MEDLINE (Ovid) and Scopus databases from inception to July 2023. ProQuest (Dissertations and Theses) database was searched to August 2023. The protocol, developed a priori, was registered on Open Science Framework and PRISMA-ScR guidelines were followed for reporting on this study. Titles and abstracts of 1,618 articles and the full texts for the 228 included articles were reviewed by two independent reviewers. 95 articles were included in the analysis. A single reviewer used a pilot-tested data collection tool to extract study and author characteristics, article type, objectives, theoretical paradigm, contextual or subjective factors, decision-maker, and type of medical decision. Results Of the 95 included articles, 68 identified a theoretical paradigm in their approach to decision thresholds. The most common paradigms included regret theory, hybrid theory, and dual processing theory. Contextual and subjective factors that influence decision thresholds were identified in 44 articles. Conclusions Our scoping review is the first to systematically characterizes the available literature on decision thresholds within medical decision-making. This study offers an important characterization of the literature through the identification of the theoretical paradigms for non-expected utility decision thresholds. Moreover, this study provides insight into the various contextual and subjective factors that have been documented within the literature to influence decision thresholds, as well as these factors juxtapose theoretical paradigms.

Thresholds and their relevance to conservation have become a major topic of discussion in the ecological literature. Unfortunately, in many cases the lack of a clear conceptual framework for thinking about thresholds may have led to confusion in attempts to apply the concept of thresholds to conservation decisions. Here, we advocate a framework for thinking about thresholds in terms of a structured decision making process. The purpose of this framework is to promote a logical and transparent process for making informed decisions for conservation. Specification of such a framework leads naturally to consideration of definitions and roles of different kinds of thresholds in the process. We distinguish among three categories of thresholds. Ecological thresholds are values of system state variables at which small changes bring about substantial changes in system dynamics. Utility thresholds are components of management objectives (determined by human values) and are values of state or performance variables at which small changes yield substantial changes in the value of the management outcome. Decision thresholds are values of system state variables at which small changes prompt changes in management actions in order to reach specified management objectives. The approach that we present focuses directly on the objectives of management, with an aim to providing decisions that are optimal with respect to those objectives. This approach clearly distinguishes the components of the decision process that are inherently subjective (management objectives, potential management actions) from those that are more objective (system models, estimates of system state). Optimization based on these components then leads to decision matrices specifying optimal actions to be taken at various values of system state variables. Values of state variables separating different actions in such matrices are viewed as decision thresholds. Utility thresholds are included in the objectives component, and ecological thresholds may be embedded in models projecting consequences of management actions. Decision thresholds are determined by the above-listed components of a structured decision process. These components may themselves vary over time, inducing variation in the decision thresholds inherited from them. These dynamic decision thresholds can then be determined using adaptive management. We provide numerical examples (that are based on patch occupancy models) of structured decision processes that include all three kinds of thresholds.

To describe the priors and decision thresholds in phase 2 and 3 randomized controlled trials (RCTs) evaluating drug efficacy using Bayesian methods. A systematic review of phase 2 and 3 RCTs evaluating drug efficacy through Bayesian inference was conducted across the MEDLINE, EMBASE, and Cochrane databases, with no date restrictions until September 2022. The type of prior used for the analysis of the primary endpoint and its characteristics (type and parameters of the distribution, justification, and sensitivity analysis), the use of a posterior probability decision threshold defined a priori, and its value, were extracted. From 1161 articles screened, 69 articles were ultimately included, encompassing a total of 91 comparisons, as some trials assessed multiple primary endpoints or treatments. The prior was assigned to treatment effect in 51% of the cases (n = 46) to each arm in 37% (n = 34) and was not explicitly defined in 12% (n = 11). Prior distribution was described (with its parameters) in 59% of cases (n = 54). A decision threshold was set a priori in 68% of the results (n = 62), and its value ranged from 70% to 99% (median 95%). The inconsistent description of priors, along with the wide variation and occasional absence of decision thresholds, underscore the need for clear guidelines on the use and reporting of Bayesian methods. Bayesian methods are being used more frequently in clinical trials to assess drug's efficacy. These methods offer flexibility by incorporating prior knowledge into the analysis. However, the use of Bayesian approaches is still not widespread, and there are challenges with how results are interpreted, partly due to a lack of clear standards. We conducted a systematic review to describe how Bayesian methods were used and reported in phase 2 and 3 clinical trials assessing drug efficacy. We looked at the types of prior information used in the analyses and how decisions about efficacy were made based on the results. Out of 1161 studies reviewed, 69 were included in the analysis, covering 91 drug comparisons. The priors in Bayesian drug trials were assigned to treatment effects or each arm, often justified but variably describe. Similarly, decision thresholds for determining drug efficacy were preset in most studies, but with heterogeneity in the thresholds used to conclude. Our findings highlight the need for clearer guidelines on using Bayesian methods in clinical trials to improve transparency and consistency in how results are reported. •A systematic review analyzed Bayesian methods in phase 2 and 3 drug efficacy trials.•The use and reporting of priors and decision thresholds showed significant variability.•Priors were justified in 74% of cases, but 26% lacked adequate description.•Posterior probability thresholds were set in 68% of comparisons, with varying values.

The evaluation of health benefits and harms of an intervention with GRADE Evidence to Decision (EtD) frameworks includes judgments if the effects are “trivial,” “small,” “moderate,” or “large.” Such judgments ideally require the a priori establishment of decision thresholds (DTs), whose empirical derivation for single outcomes has been previously described. In this article, we provide a methodological approach to estimate DTs for composite endpoints based on disutilities. We generated an approach that involves the computation of pooled disutilities, in which the disutility of each outcome comprised in the composite endpoint is weighted by the respective relative frequency. We also applied a modeling approach based on probability distributions to account for uncertainty in the estimates. We present a practical example of the determination of DTs associated with the development of at least one adverse event following treatment with intranasal medications for rhinitis that we used in the Allergic Rhinitis and its Impact on Asthma guidelines. We provide the methodological steps of a modeling-based approach to compute pooled disutilities and, as a result, DTs for composite endpoints. We have developed a webtool (https://compositedt.med.up.pt/) that allows for a simple implementation of the proposed approach. Applying our approach to a practical example, we concluded that rhinitis nasal medications, compared to placebo, were associated with a trivial harm from adverse events. We propose an approach for estimating DTs for composite endpoints, which may be particularly valuable whenever composite endpoints are used for clinical research, clinical practice, and decision-making. •An approach has been developed to compute DTs for composite endpoints.•The approach is based on weighted disutilities and allows to consider uncertainty.•We provide detailed stepwise guidance and a webtool to implement this approach.

Optimal decision-making requires balancing fast but error-prone and more accurate but slower decisions through adjustments of decision thresholds. Here, we demonstrate two distinct correlates of such speed-accuracy adjustments by recording subthalamic nucleus (STN) activity and electroencephalography in 11 Parkinson’s disease patients during a perceptual decision-making task; STN low-frequency oscillatory (LFO) activity (2–8 Hz), coupled to activity at prefrontal electrode Fz, and STN beta activity (13–30 Hz) coupled to electrodes C3/C4 close to motor cortex. These two correlates differed not only in their cortical topography and spectral characteristics but also in the relative timing of recruitment and in their precise relationship with decision thresholds. Increases of STN LFO power preceding the response predicted increased thresholds only after accuracy instructions, while cue-induced reductions of STN beta power decreased thresholds irrespective of instructions. These findings indicate that distinct neural mechanisms determine whether a decision will be made in haste or with caution. In everyday decisions, we have to balance how quickly we need to make a decision with how accurate we want our decision to be. For example, if you plan your next holiday you might want to make sure that you pick the best destination without caring too much about the time it takes to arrive at that decision. On the other hand, in your lunch break you might want to quickly choose between the different meals on the menu to make sure you are back at work on time, even though you might overlook a dish that you would have preferred. This effect – that decisions we make in haste are more likely to be suboptimal than slower, more deliberate decisions – is known as the speed-accuracy trade-off. One theory suggests that the activity of a brain area termed the subthalamic nucleus reflects whether people will prioritize speed or accuracy during decision-making. This area is seated deep inside the brain, meaning that it is normally difficult to record its activity. Herz et al. have now recorded the activity of the subthalamic nucleus in individuals with Parkinson’s disease who underwent brain surgery as part of their treatment. When these individuals switched between fast and cautious decision-making, the activity in the subthalamic nucleus changed, as did its relationship with the activity seen in other brain areas. Furthermore, these activity changes predicted how much information participants acquired before committing to a choice. Deep brain stimulation of the subthalamic nucleus is now a standard treatment for Parkinson’s disease. It will be important to assess whether this treatment affects the changes in subthalamic activity that are related to decision-making, and whether this affects whether an individual is more likely to make fast or accurate decisions.

What constitutes enough evidence to make a decision? While this is an important question across multiple domains, it takes on special importance in the US legal system, where jurors and judges are instructed to apply specific burdens of proof to render life-changing decisions. Civil trials use a preponderance of evidence (PoE) threshold to establish liability, while criminal trials require proof beyond a reasonable doubt (BaRD) to convict. It is still unclear, however, how laypeople interpret and apply these decision thresholds and how these standards compare to people’s intuitive belief (IB) of what constitutes enough evidence. Further, the extent to which their correct interpretation is context-dependent is currently unknown: are they unique to the legal context, or do they generalize to other contexts (e.g. medical, scientific, and perceptual) that also critically rely on decision thresholds? To compare burdens of proof across contexts requires a common parameter space. Here, we applied quantitative, psychometric analyses developed in psychophysics to compare decision thresholds across legal, nonlegal, and perceptual domains. We found a consistent pattern across domains in which BaRD was interpreted more stringently than PoE but, surprisingly, with PoE being more stringent than people’s IB. Decision thresholds were higher for legal contexts even when the costs of decision outcomes were equated. These results highlight how decisions are rendered inherently more stringently in the legal domain and suggest that laypeople’s IB are more lenient than either legal standard. These findings also illustrate the power of applying psychometrics to elucidate complex decision processes.

Background Decision-making impairments are central to substance use disorder (SUD), particularly in evaluating immediate versus delayed outcomes. However, conventional behavioral analyses provide limited insight into underlying cognitive mechanisms. This study applies the Drift Diffusion Model (DDM) to investigate intertemporal decision-making in female SUD across both gain and loss contexts, addressing a significant gap in understanding context-dependent decision processes. Methods The study compared 100 females with opioid use disorder to 86 female controls using intertemporal choice tasks in both gain and loss contexts. Participants made choices between smaller-immediate and larger-delayed monetary options across varying magnitudes, delay lengths, and reward differences. Behavioral preferences were analyzed using delay discounting models, while cognitive mechanisms were examined using hierarchical drift diffusion modeling to extract decision parameters (drift rates, thresholds, bias, non-decision time). Results Behaviorally, the SUD group showed stronger preferences for immediate rewards in gain scenarios and stronger avoidance of immediate losses in loss scenarios compared to controls. Delay discounting analysis revealed significantly lower discount rates in the SUD group in loss contexts ( p  <.001). DDM analysis demonstrated that the SUD group exhibited lower decision thresholds across contexts, reflecting impulsive decision characteristics. Additionally, they showed lower drift rates in gain scenarios, indicating reduced sensitivity to non-substance rewards, but higher drift rates in loss scenarios, suggesting heightened sensitivity to negative outcomes. These decision patterns varied systematically with monetary and temporal parameters. Conclusions This study reveals distinct context-dependent decision biases in female SUD, characterized by computational signatures that differ markedly between gain and loss domains. These findings enhance our understanding of SUD-related decision mechanisms beyond traditional behavioral measures and suggest potential computational targets for individualized assessment and intervention approaches, though these clinical implications remain exploratory and require extensive validation before practical implementation.

To evaluate how the rank probabilities obtained from network meta-analysis (NMA) change with the use of increasingly stringent criteria for the relative effect comparing two treatments which ranks one treatment better than the other. Systematic survey and reanalysis of published data. We included all systematic reviews (SRs) with NMA from the field of cardiovascular medicine that had trial-level data available, published in Medline up to February 2015. We reran all the NMAs and determined the probabilities of each treatment being the best. For the best treatment, we examined the effect on these probabilities of varying, what we call the decision threshold, the relative effect required to declare two treatments different. We included 14 SRs, having a median of 20 randomized trials and 9 treatments. The best treatments had probabilities of being best that ranged from 38% to 85.3%. The effect of changing the decision thresholds on the probability of a treatment being best varied substantially across reviews, with relatively little decrease (∼20 percentage points) in some settings but a decline to near 0% in others. Rank probabilities can be fragile to increases in the decision threshold used to claim that one treatment is more effective than another. Including these thresholds into the calculation of rankings may aid their interpretation and use in clinical practice.