# Introducing longevity

#### Léo Belzile

#### 2026-07-13

# What does longevity do?

The `longevity` package provides a variety of numerical routines for parametric and nonparametric models for positive data subject to non informative censoring and truncation mechanisms. The package includes functions to estimate various parametric model parameters via maximum likelihood, produce diagnostic plots accounting for survival patterns, compare nested models using analysis of deviance, etc.

The syntax of `longevity` follows that of the popular `survival` package, but forgoes the specification of `Surv` type objects: rather, users must specify some of the following

- the time vector `time` (a left interval if `time2` is provided)
- the right interval `time2` for interval censoring
- a vector or scalar `event` indicating whether data are right, left or interval censored. The option `interval2`, for interval censoring, is useful if both `time` and `time2` vectors are provided with (potentially zero or infinite bounds) for censored observations.
- the status indicator, `event`, with 0 for right censored, 1 for observed event, 2 for left censored and 3 for interval censored. If omitted, `event` is set to 1 for all subjects.
- `ltrunc` and `rtrunc` for left and right truncation values. If omitted, they are set to 0 and \(\infty\), respectively.

The reason for specifying the `ltrunc` and `rtrunc` vector outside of the usual arguments is to accomodate instances where there is both interval censoring and interval truncation; `survival` supports left-truncation right-censoring for time-varying covariate models, but this isn’t really transparent.

## Example

We consider Dutch data from CBS; these data were analysed in Einmahl et al. (2019). For simplicity, we keep only Dutch people born in the Netherlands, who were at least centenarians when they died and whose death date is known.

    thresh0 <- 36525
    data(dutch, package = "longevity")
    dutch1 <- subset(dutch, ndays > thresh0 & !is.na(ndays) & valid == "A")

We can fit various parametric models accounting for the fact that data are interval truncated. First, we create a list to avoid having to type the name of all arguments repeatedly. These, if not provided directly to function, are selected from the list through `arguments`.

    args <- with(dutch1, list(
      time = ndays,  # time vector
      ltrunc = ltrunc, # left truncation bound
      rtrunc = rtrunc, # right truncation
      thresh = thresh0, # threshold (model only exceedances)
      family = "gp")) # choice of parametric model

The generalized Pareto distribution can be used for extrapolation, provided that the threshold is high enough that shape estimates are more or less stable. To check this, we can produce threshold stability plots, which display point estimates with 95% profile-based pointwise confidence intervals.

    tstab_c <- tstab(
      arguments = args,
      family = "gp", # parametric model, here generalized Pareto
      thresh = 102:108 * 365.25, # overwrites thresh
      method = "wald", # type of interval, Wald or profile-likelihood
      plot = FALSE) # by default, calls 'plot' routine
    plot(tstab_c,
         which.plot = "shape",
         xlab = "threshold (age in days)")

Threshold stability plot with generalized Pareto shape estimates for Dutch data as a function of threshold (in years).

We can fit various parametric models and compare them using the `anova` call, provided they are nested and share the same data. Diagnostic plots, adapted for survival data, can be used to check goodness-of-fit. These may be computationally intensive to produce in large samples, since they require estimation of the nonparametric maximum likelihood estimator of the distribution function. As such, we pick a relatively high threshold, 108 years, to reduce the computational burden.

    (m1 <- fit_elife(arguments = args,
              thresh = 108 * 365.25,
              family = "gp",
              export = TRUE))

    ## Model: generalized Pareto distribution.
    ## Sampling: interval truncated
    ## Log-likelihood: -631.242
    ##
    ## Threshold: 39447
    ## Number of exceedances: 90
    ##
    ## Estimates
    ##   scale    shape
    ## 412.688    0.112
    ##
    ## Standard Errors
    ## scale  shape
    ## 1.414  0.113
    ##
    ## Optimization Information
    ##   Convergence: TRUE

    m0 <- fit_elife(arguments = args,
              thresh = 108 * 365.25,
              family = "exp")
    anova(m1, m0)

    ##     npar Deviance Df     Chisq Pr(>Chisq)
    ## gp     2 1262.483 NA        NA         NA
    ## exp    1 1263.112  1 0.6287453  0.4278159

    plot(m1, which.plot = "qq")

Quantile-quantile plot of generalized Pareto model to exceedances above 108 years for Dutch.

## References

Einmahl, Jesson J., John H. J. Einmahl, and Laurens de Haan. 2019. “Limits to Human Life Span Through Extreme Value Theory.” Journal of the American Statistical Association 114 (527): 1075–80. https://doi.org/10.1080/01621459.2018.1537912.