Survival distributions in R

Survival distributions in R


My former colleague Devin Incerti has a nice summary of how to implement survival function estimation in R. Not only does he mathematically describe the probability density function (PDF), cumulative density function (CDF), and hazard rates for 8 commonly used parametric survival curves [see table below], he also describes how to implement these using the stats and flexsurv packages in R (see here) and has built a Shiny app (see here) to help explore these different curves.

Source: Devin Incerti blog

Which model should you use? Well, if you have limited data, the exponential is the simplest to estiamte as it requires only one parameter. Further, it assumes a constant hazard ratio, which may not be a bad assumption if you have limited amount of follow-up time. In other cases, assuming a constant hazard may be unrealistic. Some things to take into account for these distributions.

  • Exponential distribution only supports a constant hazard;
  • Weibull, Gompertz, and gamma distributions support monotonically increasing and decreasing hazards;
  • Log-logistic and lognormal distributions support arc-shaped and monotonically decreasing hazards; and
  • Generalized gamma distribution supports an arc-shaped, bathtub-shaped, monotonically increasing, and monotonically decreasing hazards.

After you pick a model, it can be fit to your data using maximum likelihood estimation. Each parameter can be modeled either to fit the data on average, or also as a function of individual covariates. While describing these models is helpful, there are other parametric distributions that cold be considered including splines and fractional polynomials



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