Description Usage Arguments Details Value Examples

Using the output of the function `fitSmoothHazard`

, we can compute absolute risks by
integrating the fitted hazard function over a time period and then converting this to an
estimated survival for each individual.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
absoluteRisk(object, ...)
## Default S3 method:
absoluteRisk(object, ...)
## S3 method for class 'glm'
absoluteRisk(object, time, newdata, method = c("montecarlo",
"numerical"), nsamp = 1000, ...)
## S3 method for class 'CompRisk'
absoluteRisk(object, time, newdata,
method = c("montecarlo", "numerical"), nsamp = 1000, ...)
``` |

`object` |
Output of function |

`...` |
Extra parameters. Currently these are simply ignored. |

`time` |
A vector of time points at which we should compute the absolute risks. |

`newdata` |
Optionally, a data frame in which to look for variables with which to predict. If omitted, the mean absolute risk is returned. |

`method` |
Method used for integration. Defaults to |

`nsamp` |
Maximal number of subdivisions (if |

If the user supplies the original dataset through the parameter `newdata`

, the mean absolute
risk can be computed as the average of the output vector.

In general, if `time`

is a vector of length greater than one, the output will include a
column corresponding to the provided time points. Some modifications of the `time`

vector
are done: `time=0`

is added, the time points are ordered, and duplicates are removed. All
these modifications simplify the computations and give an output that can easily be used to plot
risk curves.

On the other hand, if `time`

corresponds to a single time point, the output does not include
a column corresponding to time.

If there is no competing risk, the output is a matrix where each column corresponds to the several covariate profiles, and where each row corresponds to a time point. If there are competing risks, the output will be a 3-dimensional array, with the third dimension corresponding to the different events.

The numerical method should be good enough in most situation, but Monte Carlo integration can give more accurate results when the estimated hazard function is not smooth (e.g. when modeling with time-varying covariates). However, if there are competing risks, we strongly encourage the user to select Monte-Carlo integration, which is much faster than the numerical method. (This is due to the current implementation of the numerical method, and it may be improved in future versions.)

Returns the estimated absolute risk for the user-supplied covariate profiles. This will be stored in a 2- or 3-dimensional array, depending on the input. See details.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
# Simulate censored survival data for two outcome types from exponential distributions
library(data.table)
set.seed(12345)
nobs <- 1000
tlim <- 20
# simulation parameters
b1 <- 200
b2 <- 50
# event type 0-censored, 1-event of interest, 2-competing event
# t observed time/endpoint
# z is a binary covariate
DT <- data.table(z=rbinom(nobs, 1, 0.5))
DT[,`:=` ("t_event" = rweibull(nobs, 1, b1),
"t_comp" = rweibull(nobs, 1, b2))]
DT[,`:=`("event" = 1 * (t_event < t_comp) + 2 * (t_event >= t_comp),
"time" = pmin(t_event, t_comp))]
DT[time >= tlim, `:=`("event" = 0, "time" = tlim)]
out_linear <- fitSmoothHazard(event ~ time + z, DT)
out_log <- fitSmoothHazard(event ~ log(time) + z, DT)
linear_risk <- absoluteRisk(out_linear, time = 10, newdata = data.table("z"=c(0,1)))
log_risk <- absoluteRisk(out_log, time = 10, newdata = data.table("z"=c(0,1)))
``` |

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