Do colored cells in risk matrices affect decision-making and risk perception? Insights from randomized controlled studies

Risk matrices communicate the likelihood and potential impact of risks and are often used to inform decision-making around risk mitigations. The merits and demerits of risk matrices in general have been discussed extensively, yet little attention has been paid to the potential influence of color in risk matrices on their users. We draw from fuzzy-trace theory and hypothesize that when color is present, individuals are likely to place greater value on reducing risks that cross color boundaries (i.e., the boundary-crossing effect), leading to sub-optimal decision making. In two randomized controlled studies, employing forced-choice and willingness-to-pay measures to investigate the boundary-crossing effect in two different color formats for risk matrices, we find preliminary evidence to support our hypotheses that color can influence decision making. The evidence also suggests that the boundary-crossing effect is only present in, or is stronger for, higher numeracy individuals. We therefore recommend that designers should consider avoiding color in risk matrices, particularly in situations where these are likely to be used by highly numerate individuals, if the communication goal is to inform in an unbiased way.     

Bayesian single-to-double arm transition design using both short-term and long-term endpoints

The choice between single-arm designs versus randomized double-arm designs has been contentiously debated in the literature of phase II oncology trials. Recently, as a compromise, the single-to-double arm transition design was proposed, combining the two designs into one trial over two stages. Successful implementation of the two-stage transition design requires a suspension period at the end of the first stage to collect the response data of the already enrolled patients. When the evaluation of the primary efficacy endpoint is overly long, the between-stage suspension period may unfavorably prolong the trial duration and cause a delay in treating future eligible patients. To accelerate the trial, we propose a Bayesian single-to-double arm design with short-term endpoints (BSDS), where an intermediate short-term endpoint is used for making early termination decisions at the end of the single-arm stage, followed by an evaluation of the long-term endpoint at the end of the subsequent double-arm stage. Bayesian posterior probabilities are used as the primary decision-making tool at the end of the trial. Design calibration steps are proposed for this Bayesian monitoring process to control the frequentist operating characteristics and minimize the expected sample size. Extensive simulation studies have demonstrated that our design has comparable power and average sample size but a much shorter trial duration than conventional single-to-double arm design. Applications of the design are illustrated using two phase II oncology trials with binary endpoints.     

Should responder analyses be conducted on continuous outcomes?

Continuous outcomes are often dichotomized to classify trial subjects as responders or nonresponders, with the difference in rates of response between treatment and control defined as the “responder effect.” In this article, we caution that dichotomization of continuous interval outcomes may not be best practice. Defining clinical benefit or harm for continuous interval outcomes as the difference between the means of treatment and control, that is, the “continuous treatment effect,” we examine the case where treatment and control outcomes are normally distributed and differ only in location. For this case, continuous treatment effects may be considered clinically relevant if they exceed a prespecified minimum clinically important difference. In contrast, using minimum clinically important differences as dichotomization thresholds will not ensure clinically relevant responder effects. For example, in some situations, increasing the threshold may actually relax the criterion for effectiveness by increasing the calculated responder effect. Using responder effects to quantitatively assess benefit or risk of investigational drugs for continuous interval outcomes presents interpretational challenges. In particular, when the dichotomization threshold is halfway between the treatment and control outcome means, the responder effect is at a maximum with a magnitude monotonically related to the number of standard deviations between the mean outcomes of treatment and control. Large responder effect benefits may therefore reflect clinically unimportant continuous treatment effects amplified by small standard deviations, and small responder effect risks may reflect either clinically important continuous treatment effects minimized by large standard deviations, or selection of a dichotomization threshold not providing maximum responder effect.     

元散曲中的青山意象

在元散曲中，青山意象多次出现，不仅作为客观的景物存在，还具有很多内在的与主观的含义。在隐逸之作中，它既是文 人的心灵归处和精神家园，又是文人的伴侣与知己；在抒怀之作中，它既反衬了历史的虚无又是世事变幻的参照；在相思之作 中，它是女子的屏障，隔断了她们眺望情人的视线；在写景之作中，它提供了色彩的叠加与对比。     

Classification Algorithm for Skin Color (CASCo): A new tool to measure skin color in social science research

Objective

A growing body of literature reveals that skin color has significant effects on people's income, health, education, and employment. However, the ways in which skin color has been measured in empirical research have been criticized for being inaccurate, if not subjective and biased.

Objective

Introduce an objective, automatic, accessible and customizable Classification Algorithm for Skin Color (CASCo).

Methods

We review the methods traditionally used to measure skin color (verbal scales, visual aids or color palettes, photo elicitation, spectrometers and image-based algorithms), noting their shortcomings. We highlight the need for a different tool to measure skin color

Results

We present CASCo, a (social researcher-friendly) Python library that uses face detection, skin segmentation and k-means clustering algorithms to determine the skin tone category of portraits.

Conclusion

After assessing the merits and shortcomings of all the methods available, we argue CASCo is well equipped to overcome most challenges and objections posed against its alternatives. While acknowledging its limitations, we contend that CASCo should complement researchers. toolkit in this area.