ORR and DCR by Brand

This visualization tells a compact but emotionally charged story about treatment efficacy. In oncology, the objective response rate (ORR) is a key metric for understanding the effectiveness of a cancer treatment. ORR is a composite metric that considers the proportion of patients who achieve a complete response (CR) with all signs of cancer disappearing and the proportion of patients who achieve a partial response (PR) with significant tumor reduction. Disease control rate (DCR) adds the additional portion of patients whose disease did not progress but didn't significantly respond to treatment either (stable disease, SD).

In this project, the task was to simplify the comparison of ORR and DCR with Brand A against the same metric for Brand B - specifically to emphasize the dramatic improvement of Brand A over Brand B in supporting patients' remission journeys. The visualization also needed to convey the composition of ORR and ultimately walk viewers through what efficacy actually means for patients.

Project considerations & specs

• Primary audience: Internal stakeholders and the general oncology audience.

• Core goal: Clearly communicate how ORR and DCR are constructed and emphasize the magnitude of Brand A’s improvement over Brand B.

• Key requirements:

  • Preserve part-to-whole relationships (CR + PR = ORR; ORR + SD = DCR)

  • Enable clean, fair comparison between brands

  • Include essential context (n-values) without clutter

  • Remain compact enough for multi-platform use

Design & process

This project needed to function as a primary endpoint visual - something that could anchor an efficacy story, not just support it. Traditional bars felt flat and overly familiar, especially given how dramatic the difference between Brand A and Brand B actually was.

The solution was a set of concentric, stacked radial bars. Each brand occupies its own ring, with Brand A intentionally placed on the outside to lead the comparison. The full radial length represents the overall endpoint (ORR or DCR), while the stacked sections within show how much of that endpoint comes from CR, PR, and (when applicable) SD.

Radial bars solved several problems at once: they’re visually striking without being decorative, compact enough to travel well across formats, and they create a natural center space for direct numeric comparison. Final ORR values are written into the center of the rings and animated to count upward from zero as the CR and PR sections build outward - reinforcing the idea that ORR is comprised of these sub-values.

To support deeper understanding, secondary labels animate outward as the sub-sections complete, showing both percentages and n-values for each response type. A subtle breadcrumb-style toggle hints that a similar visualization is available for DCR without overwhelming the screen with multiple charts at once.

Outcome & insight

This visualization succeeds because it balances accuracy, drama, and restraint. Brand A’s performance is undeniably stronger - and the radial structure allows that difference to feel impactful without distorting scale or exaggerating values. Thin bars and concentric alignment make the comparison immediate, while animation gently guides the viewer through the logic behind the numbers.

More importantly, the chart makes clinical meaning legible. The increase in Complete Response isn’t just a larger segment; it’s more patients experiencing full remission and improved quality of life. That narrative lands faster and more intuitively than any table or paragraph could.

Limitations & future refinements

• Animation timing may be slightly too fast and could benefit from more breathing room.

• Because the animation resolves into a static state, replay functionality would help reinforce the story for repeat viewers.

• Tone-on-tone shading (especially for CR sections) may reduce accessibility and could be improved with stronger contrast or patterning.

• Beyond animation pacing, the overall structure felt strong enough that few changes were necessary.

What I learned

This project was a lesson in trusting design instinct. The first concept I sketched ended up being the one that carried all the way through production - a reminder that not every problem needs to be over-engineered. The final result became part of our team’s core showreel and a reference point for future work. More than anything, it reinforced my belief that the most effective analytics visuals don’t just explain data, they shape understanding and emotion at the same time.