Survival plots – Medical charts 1

Not too long ago, I transitioned from molecular biology to clinical oncology research. In the hospital, I adapted to a different pace, visible hierarchies, and learned patience as patient care naturally takes precedence over research! I also got used to new data science environments with specific requirements for documentation, privacy, and ethics. And of course, I learned about a number of new visualizations and plots that are common in clinical data reporting! I will introduce a few of these in this article-series.

Anatomy of a survival plot

A common chart type in clinical oncology is the ‘survival’-plot, also known as Kaplan-Meier plot. However, neither name appears in your standard chart guide or references ( or Survival plots generally looks like a line plot, with the time shown on the x-axis. The time range depends on the clinical trial and its defined endpoints – and may be anywhere from minutes or days to years. Listed time points are the follow-up appointments and as such neither in regular intervals nor evenly spread.

The y-axis

The tricky bit is of course the y-axis. The data points do not encode directly measured values but instead a ‘survival probability’ at the given time point. Naturally the ‘survival probability’ decrease over the study time for any cohort. As time passes, more and more patients will invariably experience an event that was previously set as the study end-point. Study end-points typically are survival, hence the name of the plot, but they may also be recurrence of a cancer or also a positive event like leaving the intensive unit.

The survival probability is then re-calculated only for those participants still enrolled in the study at the given time point. Towards the very end of the curve, when only one or two patients are still observed, the curves most drastically change, this however reflects only relative large effect one event may have on a smaller set of study participants.  

The categories

Survival plot most often compare survival probabilities for two conditions. These could be how patients survive under a new care regime compared to the standard of care or two alternative medications. But survival plots can also be used to illustrate different response groups such as male and female study participants, or patients stratified by age.

Often the confidence interval is plotted along with the survival probability for each category and can help to gauge the uncertainty. This is especially important towards the end of the curve when fewer participants mean also increasingly larger uncertainty of the data.

Additional decorations

Survival plots frequently label the time with 50% survival probability for each cohort and often include a table below the plot explicitly listing the patients at risk for specific time points. 

Patients also drop out of a clinical study before the observed event. The participant is then excluded from the data analysis, which is termed censoring. They may die, get well and leave a trial, or stop participating in the study. This reduces the pool of persons at risk, even if no patient reached the endpoint. In the survival plot these persons are often marked in the respective category line with a tick-mark.


The very same data and survival probabilities can of course also be plotted differently. For instance, instead of focusing on the entire range of the data, a zoomed view of the early time points may help to understand critical differences in treatment. At these time points the CI is still very low and differences likely to be meaningful.

There are also versions that flip the y-axis. Now instead of showing the survival probability, the plot focuses on how along the observed time course more and more events occur and sums them up (cumulative events) or indicates the likelihood of the event (cumulative hazard).

References & Try it out

Upcoming articles:

  • Medical charts 2: Trial/study diagrams
  • Medical charts 3: Forrest plots
  • Medical charts 4: Common pitfalls

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