A course in constructing effective displays of data for pharmaceutical research personnel

Abstract

Interpreting data and communicating effectively through graphs and tables are requisite skills for statisticians and non-statisticians in the pharmaceutical industry. However, the quality of visual displays of data in the medical and pharmaceutical literature and at scientific conferences is severely lacking. We describe an interactive, workshop-driven, 2-day short course that we constructed for pharmaceutical research personnel to learn these skills. The examples in the course and the workshop datasets source from our professional experiences, the scientific literature, and the mass media. During the course, the participants are exposed to and gain hands-on experience with the principles of visual and graphical perception, design, and construction of both graphic and tabular displays of quantitative and qualitative information. After completing the course, with a critical eye, the participants are able to construct, revise, critique, and interpret graphic and tabular displays according to an extensive set of guidelines.

The Predictive Individual Effect for Survival Data

“…The need for better communication among all parties has been and still is recognized as important. [46][47][48][49][50][51] The predictive individual effect helps answering the question about a patient's survival gain, which is clinically relevant, is easy to understand for all parties, and has a causal interpretation. It not only fits the estimand framework, 9, 52 it also aligns with the predictive paradigm introduced by de Finetti 53 and Geisser, 54 which differs conceptually from standard hypothesis testing or estimation approaches.…”

How can we make better graphs? An initiative to increase the graphical expertise and productivity of quantitative scientists

“…The limited use of effective graphs as a visual communication tool, even with the widely available tools at our disposal, is not a challenge unique to our company (Gordon and Finch, Duke et al, Krause and O'Connell, Bradstreet et al). To better understand what limitations or barriers exist that prevent the use of good graphics, we conducted a survey of quantitative scientists who regularly work with clinical data.…”

Effective Visual Communication for the Quantitative Scientist

“…Tukey, 9,10 Tufte 11 and Cleveland 12 laid the foundations for good quantitative graphics. Amit et al, 13 Bradstreet, 14 Cabanski et al, 8 Donahue, 15 Doumont, 16,17 Duke et al, 18 Few, 19 Gordon and Finch, 1 Krause and O'Connell, 20 Matange, 21 Nolan and Perrett, 22 Nussbaumer Knaflic, 23 Robbins, 24 Vandemeulebroecke et al, 25 Wainer, 26 Wong, 27 and Wong 28 have all fostered an intelligent and impactful use of visual communication and graphics. Collaborative initiatives such as CTSpedia 29 have emerged.…”