Detecting and diagnosing prior and likelihood sensitivity with power-scaling

Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an important part of the Bayesian workflow. We introduce a practical and computationally efficient sensitivity analysis approach using importance sampling to estimate properties of posteriors resulting from power-scaling the prior or likelihood. On this basis, we suggest a diagnostic that can indicate the presence of prior-data conflict or likelihood noninformativity and discuss limitations to the power-scaling approach. The approach can be easily included in Bayesian workflows with minimal effort by the model builder and we present an implementation in our new R package \texttt{priorsense}. We further demonstrate the workflow on case studies of real data using models varying in complexity from simple linear models to Gaussian process models.Comment: 26 pages, 14 figure

Sources of inaccuracy in the measurement of adult patients’ resting blood pressure in clinical settings: a systematic review

Background: To interpret blood pressure (BP) data appropriately, healthcare providers need to be knowledgeable of the factors that can potentially impact the accuracy of BP measurement and contribute to variability between measurements. Methods: A systematic review of studies quantifying BP measurement inaccuracy. Medline and CINAHL databases were searched for empirical articles and systematic reviews published up to June 2015. Empirical articles were included if they reported a study that was relevant to the measurement of adult patients' resting BP at the upper arm in a clinical setting (e.g. ward or office); identified a specific source of inaccuracy; and quantified its effect. Reference lists and reviews were searched for additional articles. Results: A total of 328 empirical studies were included. They investigated 29 potential sources of inaccuracy, categorized as relating to the patient, device, procedure or observer. Significant directional effects were found for 27; however, for some, the effects were inconsistent in direction. Compared with true resting BP, significant effects of individual sources ranged from -23.6 to R33 mmHg SBP and - 14 to R23 mmHg DBP. Conclusion: A single BP value outside the expected range should be interpreted with caution and not taken as a definitive indicator of clinical deterioration. Where a measurement is abnormally high or low, further measurements should be taken and averaged. Wherever possible, BP values should be recorded graphically within ranges. This may reduce the impact of sources of inaccuracy and reduce the scope for misinterpretations based on small, likely erroneous or misleading, changes

Quantitative systematic review: sources of inaccuracy in manually measured adult respiratory rate data

Aims: To identify the potential sources of inaccuracy in manually measured adult respiratory rate (RR) data and quantify their effects.Design: Quantitative systematic review with meta‐analyses where appropriate.Data Sources: Medline, CINAHL, and Cochrane Library (from database inception to 31 July 2019).Review Methods: Studies presenting data on individual sources of inaccuracy in the manual measurement of adult RR were analysed, assessed for quality, and grouped according to the source of inaccuracy investigated. Quantitative data were extracted and synthesized and meta‐analyses performed where appropriate.Results: Included studies (N\ua0=\ua049) identified five sources of inaccuracy. The\ua0awareness effect\ua0creates an artefactual reduction in actual RR, and\ua0observation methods\ua0involving shorter counts cause systematic underscoring. Individual RR measurements can differ substantially in either direction between observations due to\ua0inter‐\ua0or\ua0intra‐observer variability.\ua0Value bias, where particular RRs are over‐represented (suggesting estimation), is a widespread problem.\ua0Recording omission\ua0is also widespread, with higher average rates in inpatient versus triage/admission contexts.Conclusion: This review demonstrates that manually measured RR data are subject to several potential sources of inaccuracy.Impact: RR is an important indicator of clinical deterioration and commonly included in track‐and‐trigger systems. However, the usefulness of RR data depends on the accuracy of the observations and documentation, which are subject to five potential sources of inaccuracy identified in this review. A single measurement may be affected by several factors. Hence, clinicians should interpret recorded RR data cautiously unless systems are in place to ensure its accuracy. For nurses, this includes counting rather than estimating RRs, employing 60‐s counts whenever possible, ensuring patients are unaware that their RR is being measured, and documenting the resulting value. For any given site, interventions to improve measurement should take into account the local organizational and cultural context, available resources, and the specific measurement issues that need to be addressed

Moral Judgements on the Actions of Self-Driving Cars and Human Drivers in Dilemma Situations From Different Perspectives

Self-driving cars have the potential to greatly improve public safety. However, their introduction onto public roads must overcome both ethical and technical challenges. To further understand the ethical issues of introducing self-driving cars, we conducted two moral judgement studies investigating potential differences in the moral norms applied to human drivers and self-driving cars. In the experiments, participants made judgements on a series of dilemma situations involving human drivers or self-driving cars. We manipulated which perspective situations were presented from in order to ascertain the effect of perspective on moral judgements. Two main findings were apparent from the results of the experiments. First, human drivers and self-driving cars were largely judged similarly. However, there was a stronger tendency to prefer self-driving cars to act in ways to minimize harm, compared to human drivers. Second, there was an indication that perspective influences judgements in some situations. Specifically, when considering situations from the perspective of a pedestrian, people preferred actions that would endanger car occupants instead of themselves. However, they did not show such a self-preservation tendency when the alternative was to endanger other pedestrians to save themselves. This effect was more prevalent for judgements on human drivers than self-driving cars. Overall, the results extend and agree with previous research, again contradicting existing ethical guidelines for self-driving car decision making and highlighting the difficulties with adapting public opinion to decision making algorithms

Moral Judgements on the Actions of Self-driving Cars and Human Drivers in Dilemma Situations from Different Perspectives

Self-driving cars have the potential to greatly improve public safety. However, their introduction onto public roads must overcome both ethical and technical challenges. To further understand the ethical issues of introducing self-driving cars, we conducted two moral judgement studies investigating potential differences in the moral norms applied to human drivers and self- driving cars. In the experiments, participants made judgements on a series of dilemma situations involving human drivers or self-driving cars. We also manipulated which perspective situations were presented from in order to ascertain the effect of perspective on moral judgements. Largely, human drivers and self-driving cars were judged similarly. However, there was a stronger tendency to prefer self-driving cars to act in ways to minimise harm, compared to human drivers. Furthermore, there was an indication that perspective influences judgements, such that people prefer to save themselves at the expense of others. The results generally agree with previous research, again contradicting existing ethical guidelines for self-driving car decision making and highlighting the difficulties with adapting public opinion to decision making algorithms