Fits survival models to the provided data using the specified engine and returns various outputs including model parameters, goodness of fit, and estimates of median survival.
Arguments
- data
A data frame containing the survival data.
- time
The name of the column in
datacontaining the time-to-event information.- event
The name of the column in
dataindicating whether the event of interest occurred.- predict_by
(Optional) The name of the column in
datadefining the prediction variable.- covariates
(Optional) A character vector specifying the names of covariates to be included in the model.
- dists
(Optional) A character vector specifying the distribution(s) to be fitted.
When the engine parameter is set to "flexsurv", options are "exp", "exponential", "gamma", "genf", "genf.orig", "gengamma", "gengamma.orig", "gompertz", "llogis", "lnorm", "lognormal", "weibull", "weibullPH".
When the engine parameter is set to "flexsurvcure", options are "exp", "gamma", "gengamma", "gompertz", "llogis", "lnorm", "weibull".
When the engine parameter is set to "flexsurvspline", dists are ignored in favor of k and scale parameters.
When the engine parameter is set to "survival", options are "exponential", "extreme", "gaussian", "loggaussian" (same as lognormal), "logistic", "lognormal", "rayleigh", "weibull".
Default is
c("exp", "gamma", "gengamma", "gompertz", "llogis", "lnorm", "weibull")which applies to flexsurv-related engines.- engine
(Optional) The survival analysis engine to be used. Options are "flexsurv", "flexsurvcure", "flexsurvspline", and "survival". Default is "flexsurv".
"flexsurv" uses
flexsurv::flexsurvreg()."flexsurvspline" uses
flexsurv::flexsurvspline()."flexsurvcure" uses
flexsurvcure::flexsurvcure()."survival" uses
survival::survreg().
- k
(Optional) A numeric vector specifying the number of knots for spline-based models. Default is
c(1, 2, 3)to test different numbers.- scale
(Optional) A character vector specifying the scale parameter(s) for spline-based models. Options are "hazard", "odds", and "normal". Default is
"hazard".- add_time_0
Optional. Uses
survival::survfit0()to add a starting time of 0 to the KM survfit object. This may be useful for plotting the KM at a subsequent stage (in surv_plots). Default is TRUE.- ...
Additional arguments just to catch them and avoid errors.
Value
A list containing information about the fit_models() call, the distributions attempted, goodness of fit, fit averages, and cure fractions (if applicable).
Examples
models <- fit_models(
data = easysurv::easy_bc,
time = "recyrs",
event = "censrec",
predict_by = "group",
covariates = "group"
)
models
#>
#> ── Fit Models Summary ──────────────────────────────────────────────────────────
#> Engine: flexsurv.
#> Approach: predict_by_covariate.
#> • The predict_by argument was set to "group", which was also a covariate.
#> • Therefore, models were fit on the full dataset.
#> • This is sometimes referred to as "joint fits".
#>
#> Distributions attempted: "exp", "gamma", "gengamma", "gompertz", "llogis",
#> "lnorm", and "weibull".
#>
#> ── Median survival estimates ──
#>
#> dist aic_rank group=Good group=Medium group=Poor
#> 1 exp 7 11.479024 5.065736 2.466988
#> 2 gamma 4 8.511336 4.663958 2.538296
#> 3 gengamma 1 8.630495 4.507538 2.392662
#> 4 gompertz 6 8.530095 4.865718 2.606950
#> 5 llogis 3 8.474213 4.545136 2.336817
#> 6 lnorm 2 8.632037 4.558062 2.393145
#> 7 weibull 5 8.757940 4.741585 2.605819
#>
#> ℹ For comparison, the KM median survival times were NA, 5.255, and 2.184.
#> ℹ The distribution with the best (lowest) AIC was "gengamma".
#> ────────────────────────────────────────────────────────────────────────────────
#> → For more information, run `View()` on saved `fit_models()` output.