Preface#
We are agents in an age-old battle: Dynamic vs. Static. Each side has its strategy. On one hand, static systems dominate clinical and public health literature—offering static Kaplan-Meier curves that serve as snapshots of survival probabilities over time. These are the orthodox tools, providing certainty and simplicity. However, their rigidity fails to capture the personalized, evolving nature of risk. We stand firmly with the Dynamic, where systems can adapt, interact, and provide precision that static representations can never offer.
In this Jupyter Book, we do more than critique orthodoxy. We provide an alternative—an interactive, dynamic web application that personalizes risk for individuals based on 58-100 demographic and clinical characteristics. Hosted on GitHub, using .js and .html, this app transforms static representations into dynamic systems. It allows users to explore their data in real time, offering fluid, individualized outputs that reflect the complexity of human experience rather than forcing data into predefined categories.
Strategy#
The adversaries have laid out their strategies: static systems maintain orthodoxy, offering broad, simplistic views of survival data, while dynamic systems innovate, allowing for personalized and interactive risk modeling. As agents of the Dynamic, we push for disruption—just as reinforcement learning improves performance through adversarial training, so too does dynamic modeling challenge the static status quo.1 Our web application is a direct attack on the limitations of static tools, providing adaptability and precision where static models offer stagnation.
Payoff#
The payoff for this revolution is enormous—not just for us, but for speculators and end-users alike: students, professors, clinicians, patients, public health experts, and the entire enterprise of patient education. A dynamic system personalizes risk, making patient care more accurate, education more impactful, and research more precise. It redefines the experience of those who rely on static models, offering them tools that can evolve as the data does. The traditional payoff models of publishing static data no longer suffice; dynamic models promise deeper insights, broader applications, and more powerful results.
Nash#
Finally, we come to reconciliation—the “Nash equilibrium.” This is where static and dynamic systems must balance, where peer-review processes at NIH study sections and in journals signal to the marketplace the impact of our work. When we receive grants, publish in high-impact journals, garner citations, and inspire editorials, these events serve as signals that the dynamic has taken root. The odds of this impact are high, and each of these signals adds weight to the argument that dynamic systems are the future of clinical research. Our mentors, agents of the static, represent the status quo, but the Nash equilibrium we seek is found when both sides are reconciled in the marketplace, through adaptation, innovation, and collaboration.
In reshaping the way we think about risk, survival, and personalization, we aim to disrupt, to innovate, and to revolutionize. Static systems have their place, but it is time for dynamic systems to lead, adapt, and personalize our understanding of the human condition.