Risk and Uncertainty Communication

Spiegelhalter, David

doi:10.1146/annurev-statistics-010814-020148

Cited by 172 publications

(159 citation statements)

References 104 publications

Supporting

Mentioning

154

Contrasting

Unclassified

Order By: Relevance

“…The prognostic utility of the model could be compared with current clinical algorithms for the treatment of rapid decline in a prospective study. As more statisticians translate findings from prediction models into point of care through R Shiny and other technologies, additional work will be needed to communicate risk measures from clinical dashboards to providers and patients …”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Szczesniak

Brokamp

et al. 2019

Statistics in Medicine

View full text Add to dashboard Cite

Cystic fibrosis (CF) is a progressive, genetic disease characterized by frequent, prolonged drops in lung function. Accurately predicting rapid underlying lung‐function decline is essential for clinical decision support and timely intervention. Determining whether an individual is experiencing a period of rapid decline is complicated due to its heterogeneous timing and extent, and error component of the measured lung function. We construct individualized predictive probabilities for “nowcasting” rapid decline. We assume each patient's true longitudinal lung function, S(t), follows a nonlinear, nonstationary stochastic process, and accommodate between‐patient heterogeneity through random effects. Corresponding lung‐function decline at time t is defined as the rate of change, S′(t). We predict S′(t) conditional on observed covariate and measurement history by modeling a measured lung function as a noisy version of S(t). The method is applied to data on 30 879 US CF Registry patients. Results are contrasted with a currently employed decision rule using single‐center data on 212 individuals. Rapid decline is identified earlier using predictive probabilities than the center's currently employed decision rule (mean difference: 0.65 years; 95% confidence interval (CI): 0.41, 0.89). We constructed a bootstrapping algorithm to obtain CIs for predictive probabilities. We illustrate real‐time implementation with R Shiny. Predictive accuracy is investigated using empirical simulations, which suggest this approach more accurately detects peak decline, compared with a uniform threshold of rapid decline. Median area under the ROC curve estimates (Q1‐Q3) were 0.817 (0.814‐0.822) and 0.745 (0.741‐0.747), respectively, implying reasonable accuracy for both. This article demonstrates how individualized rate of change estimates can be coupled with probabilistic predictive inference and implementation for a useful medical‐monitoring approach.

show abstract

Section: Discussionmentioning

confidence: 99%

“…As more statisticians translate findings from prediction models into point of care through R Shiny and other technologies, additional work will be needed to communicate risk measures from clinical dashboards to providers and patients. 38…”

Section: Discussionmentioning

confidence: 99%

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Szczesniak

Brokamp

et al. 2019

Statistics in Medicine

View full text Add to dashboard Cite

show abstract

“…An initial list of 37 references was drafted based on: working group members’ knowledge of the literature; references included in a major recent review paper on risk and uncertainty communication, (Spiegelhalter, ); references already available at EFSA from different sources. …”

Section: Sources Of Evidence For the Guidancementioning

confidence: 99%

“…Therefore, the use of these words should therefore be avoided, and infrequent but not negligible events should rather be described using terms having a positive directionality (e.g. ‘low probability’) (Teigen et al., ). Online versions of communication documents could provide links between probability terms and optional graphic displays related to the sources of uncertainty and to uncertainty scales (Spiegelhalter et al., ; Spiegelhalter, ).…”

Section: Sources Of Evidence For the Guidancementioning

confidence: 99%

Guidance on Communication of Uncertainty in Scientific Assessments

Hart¹,

Maxim²,

Siegrist³

et al. 2019

EFS2

View full text Add to dashboard Cite

This document provides guidance for communicators on how to communicate the various expressions of uncertainty described in EFSA's document: ‘Guidance on uncertainty analysis in scientific assessments’. It also contains specific guidance for assessors on how best to report the various expressions of uncertainty. The document provides a template for identifying expressions of uncertainty in scientific assessments and locating the specific guidance for each expression. The guidance is structured according to EFSA's three broadly defined categories of target audience: ‘entry’, ‘informed’ and ‘technical’ levels. Communicators should use the guidance for entry and informed audiences, while assessors should use the guidance for the technical level. The guidance was formulated using evidence from the scientific literature, grey literature and two EFSA research studies, or based on judgement and reasoning where evidence was incomplete or missing. The limitations of the evidence sources inform the recommendations for further research on uncertainty communication.

show abstract

“…With regard to the handling of epidemiological evidence, the framework might specify whether the focus is on overall-or condition-specific risks, how to conceptualize the relationship between population-and individual-level risks and which risk metrics to use [57]. This should occur at the start of the process and the final framework should record and explain any revisions.…”

Section: Improving the Objectivity And Transparency Of Judgements On mentioning

confidence: 99%

How should we set consumption thresholds for low risk drinking guidelines? Achieving objectivity and transparency using evidence, expert judgement and pragmatism

et al. 2018

View full text Add to dashboard Cite

Most high-income nations issue guidelines on low-risk drinking to inform individuals' decisions about alcohol consumption. However, leading scientists have criticized the processes for setting the consumption thresholds within these guidelines for a lack of objectivity and transparency. This paper examines how guideline developers should respond to such criticisms and focuses particularly on the balance between epidemiological evidence, expert judgement and pragmatic considerations. Although concerned primarily with alcohol, our discussion is also relevant to those developing guidelines for other health-related behaviours. We make eight recommendations across three areas. First, recommendations on the use of epidemiological evidence: (1) guideline developers should assess whether the available epidemiological evidence is communicated most appropriately as population-level messages (e.g. suggesting reduced drinking benefits populations rather than individuals); (2) research funders should prioritize commissioning studies on the acceptability of different alcohol-related risks (e.g. mortality, morbidity, harms to others) to the public and other stakeholders; and (3) guideline developers should request and consider statistical analyses of epidemiological uncertainty. Secondly, recommendations to improve objectivity and transparency when translating epidemiological evidence into guidelines: (4) guideline developers should specify and publish their analytical framework to promote clear, consistent and coherent judgements; and (5) guideline developers' decision-making should be supported by numerical and visual techniques which also increase the transparency of judgements to stakeholders. Thirdly, recommendations relating to the diverse use of guidelines: (6) guideline developers and their commissioners should give meaningful attention to how guidelines are used in settings such as advocacy, health promotion, clinical practice and wider health debates, as well as in risk communication; (7) guideline developers should make evidence-based judgements that balance epidemiological and pragmatic concerns to maximize the communicability, credibility and general effectiveness of guidelines; and (8) as with scientific judgements, pragmatic judgements should be reported transparently.

show abstract

Risk and Uncertainty Communication

Cited by 172 publications

References 104 publications

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Guidance on Communication of Uncertainty in Scientific Assessments

How should we set consumption thresholds for low risk drinking guidelines? Achieving objectivity and transparency using evidence, expert judgement and pragmatism

Contact Info

Product

Resources

About