Survival data stand out as a special statistical field. This paper tries to describe what survival data is and what makes it so special. Survival data concerns times to some events. A key point is the successive observation of time, which on the one hand leads to sometimes not being observed so that all that is known is that they exceed some given times (censoring), and on the other hand implies that predictions regarding the future course should be conditional on the present status (truncation). In the simplest case, this condition is that the individual is alive. The successive conditioning makes the hazard function, which describes the probability of an event happening during a short interval given that the individual is alive today, the most relevant concept. Here we discuss parametric as well as non-parametric methods. Examples are presented in a way that can be followed without the help of computers.

**
Keywords:
**
Censored data; Hazard function; Kaplan-Meier estimate; Logrank test; Survivor function.

Citedby |

Converging or Crossing Curves: Untie the Gordian Knot or Cut it? Appropriate Statistics for Non-Proportional Hazards in Decitabine DACO-016 Study (AML). Adv Ther, 2015 Sep 16; 32(9). PMID: 26369324 Free PMC article. |

Clinicopathological characteristics, adjuvant chemotherapy decision and disease outcome in patients with breast cancer with a 21-gene recurrence score of 26-30. Oncol Lett, 2020 Jul 30; 20(2). PMID: 32724396 Free PMC article. |