When Does the Incremental Risk Format Aid Informed Medical Decisions? The Role of Learning, Feedback, and Number of Treatment Options

Abstract

Background. Informed medical decisions require understanding the benefits and risks of treatments. This entails comparing treatment outcomes to a control group. The incremental risk format has been recommended as it directly visualizes the differences between treatment and control group in 1 graph, whereas they have to be calculated from 2 separate graphs in the total risk format. We investigated when the incremental risk format aids understanding. Methods. In 2 experiments, participants received information about medical treatments, either as incremental or total risk format. We assessed verbatim knowledge (precise quantitative knowledge), gist knowledge (knowledge of essential meaning), and evaluations of the formats. Study 1 ( N = 99) consisted of only 1 trial with medical information and also assessed recall. Study 2 ( N = 222) assessed learning across multiple trials and also varied the presence of feedback and the number of treatment options. Results. In study 1, the incremental risk format (v. total risk format) led to worse knowledge, recall, and evaluations. In study 2, participants learned to understand the incremental risk format over time, resulting in comparable verbatim knowledge and evaluations as in the total risk format, as well as in even better gist knowledge. Feedback and number of treatment options did not moderate the effect of risk format. Limitations. The studies were conducted with nonpatient samples, and study 2 employed hypothetical treatments. Conclusions. The incremental risk format was initially less understandable than the total risk format. After a short learning period, however, the incremental risk format resulted in better gist knowledge and was comparable otherwise, which suggests that participants had to get used to that format. This has important implications for the study of new formats.

Investigating the presentation of uncertainty in an icon array: A randomized trial

“…Some studies have found that visualizations are valued by carriers of BRCA1/2 pathogenic variants[ 36 , 37 ] and result in higher decision satisfaction[ 38 ], but have found no clear differences in comprehension, well-being or treatment intentions[ 38 , 39 ]. Studies communicating breast cancer risk with ‘incremental’ icon arrays containing three or more colors found that these have not fared as well as other formats[ 36 , 37 , 40 ], in line with research finding that this kind of icon array can be difficult to interpret without training[ 41 ]. BRCA risk communication studies using simpler icon arrays have been inconclusive[ 39 , 42 ] or have reported benefits[ 43 , 44 ].…”

Current Challenges When Using Numbers in Patient Decision Aids: Advanced Concepts

“…Where possible in decision aids, effect sizes should be presented as either independent event rates (using simple frequencies or percentage formats) and/or as an incremental (or absolute) risk increase or decrease from a baseline risk frequency. 3,6,7 A hypothetical example would be that 5 out of 100 (5%) people in a particular population group are likely to die from disease X over the next year without receiving treatment A. This could be described as the baseline frequency or control event rate.…”

“…It has been consistently recommended that relative risk presentations alone should be avoided as they tend to magnify risk perceptions and decrease understanding in both patients and clinicians. [7][8][9][10][11][12][13] Similarly, the number needed to treat and number needed to harm formats should also be avoided in patient decision aids, as they lead to overestimates of treatment effects and are poorly understood. 14 There is no advantage to providing baseline risk alongside relative risks, and this practice should continue to be avoided.…”

“…87 When evaluating novel or uncommon formats, it should be considered that participants may need to get used to these novel formats, so that researchers may want to include a learning or habituation phase to ensure a fair comparison with more classical formats. 7 Although some research indicates that people may have a slightly better understanding of risk when receiving a format that they prefer (e.g., numbers only v. numbers and graphical displays), 88 it is important to note that individuals' preferences for different visual displays are not always aligned with their performance. That is, the graph types that people prefer or evaluate more positively may not necessarily lead to better performance for a given task.…”

<p>Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data</p>

“…While it is commonly agreed that informed medical decisions require profound understanding of specific benefits and risks of treatments, conclusive information for a specific situation is often lacking. 1 As an example, this was the case in about half of the 3000 treatment options compiled in the BMJ Clinical Evidence compendium 2013 with another seven percent requiring (individualized) trade-offs between benefits and harms. 2 Thus, when prescribing drugs, clinicians constantly need to make treatment decisions for patients without knowing the best choice of often multiple available options.…”