![]() Note that the percentage of participants surviving does not always represent the percentage who are alive (which assumes that the outcome of interest is death). Time is shown on the X-axis and survival (proportion of people at risk) is shown on the Y-axis. Survival curves are often plotted as step functions, as shown in the figure below. Using nonparametric methods, we estimate and plot the survival distribution or the survival curve. We focus here on two nonparametric methods, which make no assumptions about how the probability that a person develops the event changes over time. More details on parametric methods for survival analysis can be found in Hosmer and Lemeshow and Lee and Wang 1,3. Other distributions make different assumptions about the probability of an individual developing an event (i.e., it may increase, decrease or change over time). 2 Perhaps the most popular is the exponential distribution, which assumes that a participant's likelihood of suffering the event of interest is independent of how long that person has been event-free. Some popular distributions include the exponential, Weibull, Gompertz and log-normal distributions. There are a number of popular parametric methods that are used to model survival data, and they differ in terms of the assumptions that are made about the distribution of survival times in the population. There are several different ways to estimate a survival function or a survival curve. ![]()
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