How are risk ratios reported in orthopaedic surgery journals? A descriptive study of formats used to report absolute risks

The numerical format in which risks are communicated can affect risk comprehension and perceptions of medical professionals. We investigated what numerical formats are used to report absolute risks in empirical articles, estimated the frequency of biasing formats and rated the quality of figures used to display the risks. Descriptive study of reporting practices. We randomly sampled articles published in seven leading orthopaedic surgery journals during a period of 13 years. From these, we selected articles that reported group comparisons on a binary outcome (eg, revision rates in two groups) and recorded the numerical format used to communicate the absolute risks in the results section. The quality of figures was assessed according to published guidelines for transparent visual aids design. Prevalence of information formats and quality of figures. The final sample consisted of 507 articles, of which 14% reported level 1 evidence, 13% level 2 and 73% level 3 or lower. The majority of articles compared groups of different sizes (90%), reported both raw numbers and percentages (64%) and did not report the group sizes alongside (50%). Fifteen per cent of articles used two formats identified as biasing: only raw numbers (8%, '90 patients vs 100 patients') or raw numbers reported alongside different group sizes (7%, '90 out of 340 patients vs 100 out of 490 patients'). The prevalence of these formats decreased in more recent publications. Figures (n=79) had on average two faults that could distort comprehension, and the majority were rated as biasing. Authors use a variety of formats to report absolute risks in scientific articles and are likely not aware of how some formats and graph design features can distort comprehension. Biases can be reduced if journals adopt guidelines for transparent risk communication but more research is needed into the effects of different formats

On the likelihood of surrogates conforming to the substituted judgment standard when making end-of-life decisions for their partner

A considerable proportion of end-of-life decisions are made by the patient’s next-of-kin, who can be asked to follow the substituted judgment standard and decide based on the patient’s wishes. The question of whether these surrogate decision makers are actually able to do so has become an important issue. In this study, we examined how the likelihood of surrogates conforming to the substituted judgment standard varies with individual differences in mortality acceptance and confidence in their decision making. We recruited 153 participants in romantic relationships between 18 and 80 years old from the general population. We asked them to make hypothetical end-of-life decisions for themselves and on behalf of their partner, as well as predict what their partner would do, and complete a series of questionnaires. Participants predicted that their partner would make similar decisions to their own but were more likely to accept a life-saving treatment that could result in reduced quality of life on their partner’s behalf than for themselves. Decisions made by older adults were more likely to conform to the substituted judgment standard, which is encouraging given that they are more likely to be confronted with these decisions in real life, although this was not due to differences in mortality acceptance. Older adults were also more likely to have had previous discussions with their partner and thereby know that person’s wishes and feel confident that they made the right decision, but these factors did not affect their likelihood of conforming to the substituted judgment standard. This shows that encouraging discussions about end of life among families would ease the decision process, but more work is needed to ensure that surrogates can adhere to the substituted judgment standard

Biasing and debiasing health decisions with bar graphs: Costs and benefits of graph literacy

Bar graphs can improve risk communication in medicine and health. Unfortunately, recent research has revealed that bar graphs are associated with a robust bias that can lead to systematic judgement and decision-making errors. When people view bar graphs representing means, they tend to believe that data points located within bars are more likely to be part of the underlying distributions than equidistant points outside bars. In three experiments, we investigated potential consequences, key cognitive mechanisms, and generalisability of the within-the-bar bias in the medical domain. We also investigated the effectiveness of different interventions to reduce the effect of this bias and protect people from errors. Results revealed that the within-the-bar bias systematically affected participants’ judgements and decisions concerning treatments for controlling blood glucose, as well as their interpretations of ecological graphs designed to guide health policy decisions. Interestingly, individuals with higher graph literacy showed the largest biases. However, the use of dot plots to replace bars improved the accuracy of interpretations. Perceptual mechanisms underlying the within-the-bar bias and prescriptive implications for graph design are discussed

The Influence of Causal Knowledge in Two-Alternative Forced-Choice Tasks

Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limited computational capacity, time, and knowledge when making decisions. These heuristics are effective to the extent that they can exploit the structure of information in the environment in which they operate. They require knowledge about the predictive value of probabilistic cues. However, it is often difficult to keep track of all the available cues in the environment and how they relate to any relevant criterion. We suggest that knowledge about the causal structure of the environment helps decision makers focus on a manageable subset of cues, thus effectively reducing the potential computational complexity inherent in even relatively simple decision-making tasks. Specifically, we claim that causal knowledge can act as a meta-cue for identifying highly valid cues and help to estimate cue-validities. Causal knowledge, however, can also bias people's decisions. We review experimental evidence that tested these hypotheses

From reading numbers to seeing ratios: a benefit of icons for risk comprehension

Promoting a better understanding of statistical data is becoming increasingly important for improving risk comprehension and decision-making. In this regard, previous studies on Bayesian problem solving have shown that iconic representations help infer frequencies in sets and subsets. Nevertheless, the mechanisms by which icons enhance performance remain unclear. Here, we tested the hypothesis that the benefit offered by icon arrays lies in a better alignment between presented and requested relationships, which should facilitate the comprehension of the requested ratio beyond the represented quantities. To this end, we analyzed individual risk estimates based on data presented either in standard verbal presentations (percentages and natural frequency formats) or as icon arrays. Compared to the other formats, icons led to estimates that were more accurate, and importantly, promoted the use of equivalent expressions for the requested probability. Furthermore, whereas the accuracy of the estimates based on verbal formats depended on their alignment with the text, all the estimates based on icons were equally accurate. Therefore, these results support the proposal that icons enhance the comprehension of the ratio and its mapping onto the requested probability and point to relational misalignment as potential interference for text-based Bayesian reasoning. The present findings also argue against an intrinsic difficulty with understanding single-event probabilities

The effects of numeracy and presentation format on judgments of contingency

Covariation information can be used to infer whether a causal link plausibly exists between two dichotomous variables, and such judgments of contingency are central to many critical and everyday decisions. However, individuals do not always interpret and integrate covariation information effectively, an issue that may be compounded by limited numeracy skills, and they often resort to the use of heuristics, which can result in inaccurate judgments. This experiment investigated whether presenting covariation information in a composite bar chart increased accuracy of contingency judgments, and whether it can mitigate errors driven by low numeracy skills. Participants completed an online questionnaire, which consisted of an 11-item numeracy scale and three covariation problems that varied in level of difficulty, involving a fictitious fertilizer and its impact on whether a plant bloomed or not. Half received summary covariation information in a composite bar chart, and half in a 2 × 2 matrix that summarized event frequencies. Viewing the composite bar charts increased accuracy of individuals both high and low in numeracy, regardless of problem difficulty, resulted in more consistent judgments that were closer to the normatively correct value, and increased the likelihood of detecting the correct direction of association. Findings are consistent with prior work, suggesting that composite bar charts are an effective way to improve covariation judgment and have potential for use in the domain of health risk communication