Return fitted survival, cumulative hazard or hazard at a series of times
from a fitted `flexsurvreg`

or `flexsurvspline`

model.

1 2 3 4 |

`object` |
Output from |

`newdata` |
Data frame containing covariate values to produce fitted values for. Or a list that can be coerced to such a data frame. There must be a column for every covariate in the model formula, and one row for every combination of covariates the fitted values are wanted for. These are in the same format as the original data, with factors as a single variable, not 0/1 contrasts. If this is omitted, if there are any continuous covariates, then a single summary is provided with all covariates set to their mean values in the data - for categorical covariates, the means of the 0/1 indicator variables are taken. If there are only factor covariates in the model, then all distinct groups are used by default. |

`X` |
Alternative way of defining covariate values to produce fitted
values for. Since version 0.4, Columns of For “factor” (categorical) covariates, the values of the contrasts
representing factor levels (as returned by the |

`type` |
Ignored if |

`fn` |
Custom function of the parameters to summarise against time.
This has optional first two arguments |

`t` |
Times to calculate fitted values for. By default, these are the sorted unique observation (including censoring) times in the data - for left-truncated datasets these are the "stop" times. |

`start` |
Optional left-truncation time or times. The returned survival, hazard or cumulative hazard will be conditioned on survival up to this time. A vector of the same length as |

`ci` |
Set to |

`B` |
Number of simulations from the normal asymptotic distribution of
the estimates used to calculate confidence intervals. Decrease for greater
speed at the expense of accuracy, or set |

`cl` |
Width of symmetric confidence intervals, relative to 1. |

`tidy` |
If |

`...` |
Further arguments passed to or from other methods. Currently unused. |

Time-dependent covariates are not currently supported. The covariate values are assumed to be constant through time for each fitted curve.

If `tidy=FALSE`

, a list with one component for each unique
covariate value (if there are only categorical covariates) or one component
(if there are no covariates or any continuous covariates). Each of these
components is a matrix with one row for each time in `t`

, giving the
estimated survival (or cumulative hazard, or hazard) and 95% confidence
limits. These list components are named with the covariate names and
values which define them.

If `tidy=TRUE`

, a data frame is returned instead. This is formed by
stacking the above list components, with additional columns to identify the
covariate values that each block corresponds to.

If there are multiple summaries, an additional list component named
`X`

contains a matrix with the exact values of contrasts (dummy
covariates) defining each summary.

The `plot.flexsurvreg`

function can be used to quickly plot
these model-based summaries against empirical summaries such as
Kaplan-Meier curves, to diagnose model fit.

Confidence intervals are obtained by random sampling from the asymptotic normal distribution of the maximum likelihood estimates (see, e.g. Mandel (2013)).

C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk

Mandel, M. (2013). "Simulation based confidence intervals for functions with complicated derivatives." The American Statistician (in press).

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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