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.

Plot method for objects returned by the absoluteRisk function. Current plot types are cumulative incidence and survival functions.

absoluteRisk.CompRisk(
  object,
  time,
  newdata,
  method = c("numerical", "montecarlo"),
  nsamp = 100,
  onlyMain = TRUE,
  type = c("CI", "survival"),
  addZero = TRUE
)

absoluteRisk(
  object,
  time,
  newdata,
  method = c("numerical", "montecarlo"),
  nsamp = 100,
  s = c("lambda.1se", "lambda.min"),
  n.trees,
  onlyMain = TRUE,
  type = c("CI", "survival"),
  addZero = TRUE,
  ntimes = 100,
  ...
)

# S3 method for absRiskCB
print(x, ...)

# S3 method for absRiskCB
plot(
  x,
  ...,
  xlab = "time",
  ylab = ifelse(attr(x, "type") == "CI", "cumulative incidence",
    "survival probability"),
  type = "l",
  gg = TRUE,
  id.names,
  legend.title
)

Arguments

object

Output of function fitSmoothHazard.

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. Alternatively, if newdata = "typical", the absolute risk will be computed at a "typical" covariate profile (see Details).

method

Method used for integration. Defaults to "numerical", which uses the trapezoidal rule to integrate over all time points together. The only other option is "montecarlo", which implements Monte-Carlo integration.

nsamp

Maximal number of subdivisions (if method = "numerical") or number of sampled points (if method = "montecarlo").

onlyMain

Logical. For competing risks, should we return absolute risks only for the main event of interest? Defaults to TRUE.

type

Line type. Only used if gg = FALSE. This argument gets passed to graphics::matplot(). Default: 'l'

addZero

Logical. Should we add time = 0 at the beginning of the output? Defaults to TRUE.

s

Value of the penalty parameter lambda at which predictions are required (for class cv.glmnet).

n.trees

Number of trees used in the prediction (for class gbm).

ntimes

Number of time points (only used if time is missing).

...

further arguments passed to matplot. Only used if gg=FALSE.

x

Fitted object of class absRiskCB. This is the result from the casebase::absoluteRisk() function.

xlab

xaxis label, Default: 'time'

ylab

yaxis label. By default, this will use the "type" attribute of the absRiskCB object

gg

Logical for whether the ggplot2 package should be used for plotting. Default: TRUE

id.names

Optional character vector used as legend key when gg=TRUE. If missing, defaults to V1, V2, ...

legend.title

Optional character vector of the legend title. Only used if gg = FALSE. Default is 'ID'

Value

If time was provided, returns the estimated absolute risk for the user-supplied covariate profiles. This will be stored in a matrix or a higher dimensional array, depending on the input (see details). If both time and newdata are missing, returns the original data with a new column containing the risk estimate at failure times.

A plot of the cumulative incidence or survival curve

Details

If newdata = "typical", we create a typical covariate profile for the absolute risk computation. This means that we take the median for numerical and date variables, and we take the most common level for factor variables.

In general, 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.

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).

See also

Examples

# Simulate censored survival data for two outcome types 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))]
#> z t_event t_comp #> 1: 1 510.83410 2.3923947 #> 2: 1 33.98842 23.7578470 #> 3: 1 997.76445 31.5864062 #> 4: 1 209.28888 5.7092667 #> 5: 0 75.35774 81.5124801 #> --- #> 996: 1 111.80274 0.1186062 #> 997: 0 238.05336 60.3685477 #> 998: 0 142.60033 3.6318489 #> 999: 0 103.37601 24.5722384 #> 1000: 0 255.84352 113.5144522
DT[,`:=`("event" = 1 * (t_event < t_comp) + 2 * (t_event >= t_comp), "time" = pmin(t_event, t_comp))]
#> z t_event t_comp event time #> 1: 1 510.83410 2.3923947 2 2.3923947 #> 2: 1 33.98842 23.7578470 2 23.7578470 #> 3: 1 997.76445 31.5864062 2 31.5864062 #> 4: 1 209.28888 5.7092667 2 5.7092667 #> 5: 0 75.35774 81.5124801 1 75.3577374 #> --- #> 996: 1 111.80274 0.1186062 2 0.1186062 #> 997: 0 238.05336 60.3685477 2 60.3685477 #> 998: 0 142.60033 3.6318489 2 3.6318489 #> 999: 0 103.37601 24.5722384 2 24.5722384 #> 1000: 0 255.84352 113.5144522 2 113.5144522
DT[time >= tlim, `:=`("event" = 0, "time" = tlim)]
#> z t_event t_comp event time #> 1: 1 510.83410 2.3923947 2 2.3923947 #> 2: 1 33.98842 23.7578470 0 20.0000000 #> 3: 1 997.76445 31.5864062 0 20.0000000 #> 4: 1 209.28888 5.7092667 2 5.7092667 #> 5: 0 75.35774 81.5124801 0 20.0000000 #> --- #> 996: 1 111.80274 0.1186062 2 0.1186062 #> 997: 0 238.05336 60.3685477 0 20.0000000 #> 998: 0 142.60033 3.6318489 2 3.6318489 #> 999: 0 103.37601 24.5722384 0 20.0000000 #> 1000: 0 255.84352 113.5144522 0 20.0000000
out_linear <- fitSmoothHazard(event ~ time + z, DT, ratio = 10)
#> 'time' will be used as the time variable
linear_risk <- absoluteRisk(out_linear, time = 10, newdata = data.table("z" = c(0,1))) # Plot CI curves---- library(ggplot2) data("brcancer") mod_cb_tvc <- fitSmoothHazard(cens ~ estrec*log(time) + horTh + age + menostat + tsize + tgrade + pnodes + progrec, data = brcancer, time = "time", ratio = 1) smooth_risk_brcancer <- absoluteRisk(object = mod_cb_tvc, newdata = brcancer[c(1,50),]) class(smooth_risk_brcancer)
#> [1] "absRiskCB" "matrix" "array"
plot(smooth_risk_brcancer)