condSURV (version 2.0.4)

survCOND: Conditional survival probabilities based on the Kaplan-Meier weights, Landmark approaches and Inverse probability of censoring weighted.

Description

Provides estimates for the conditional survival probabilities based on Kaplan-Meier weighted estimators, the Landmark approaches and Inverse probability of censoring weighted.

Usage

survCOND(
  formula,
  x,
  y,
  lower.tail = FALSE,
  method = "LDM",
  presmooth = FALSE,
  conf = TRUE,
  n.boot = 200,
  data,
  conf.level = 0.95,
  z.value,
  bw = "dpik",
  window = "gaussian",
  method.weights = "NW",
  cluster = FALSE,
  ncores = NULL,
  na.rm = TRUE
)

Value

An object of class "survCS" and one of the following four classes: "KMW", "LMD", "PLDM" and "IPCW". Objects are implemented as a list with elements:

est

data.frame with estimates of the conditional probabilities.

estimate

Estimates of the conditional survival probability.

LCI

The lower conditional survival probabilities of the interval.

UCI

The upper conditional survival probabilities of the interval.

conf.level

Level of confidence.

y

The total time for obtaining the estimates of the conditional survival probabilities.

x

The first time for obtaining the estimates of the conditional survival probabilities.

Nlevels

The number of levels of the covariate. Provides important information when the covariate at the right hand side of formula is of class factor.

conf

logical; if FALSE (default) the pointwise confidence bands are not given.

callp

The expression of the estimated probability.

levels

The levels of the qualitative covariate (if it is of class factor) on the right hand side of formula.

Arguments

formula

A formula object, which must have a survCS object as the response on the left of the ~ operator and, if desired, a term on the right. The term may be a qualitative or quantitative variable. For a single survival curve the right hand side should be ~ 1.

x

Time or vector of times for the condional event(s).

y

The total time for obtaining estimates for the conditional survival probabilities.

lower.tail

vector of logical values with the same size as 'x'. If 'x' has dimension one and if lower.tail = FALSE (default), probabilities are \(P(T > y|T1 > x)\) otherwise, \(P(T > y|T1 <= x)\). If the conditional event is 2-dimensional, then, for example, given \(x = c(x1, x2)\) and lower.tail = c(TRUE, FALSE) must be used to obtain probabilities \(P(T > y|T1 <= x1, T2 > x2)\). Multi-dimensional conditional events are introduced similarly.

method

The method used to compute the conditional survival function. Possible options are "LDM" and "KMW". Defaults to "LDM".

presmooth

A logical value. If TRUE, the presmoothed landmark estimator of the conditional survival function is computed. Only valid for method = "LDM".

conf

Provides pointwise confidence bands. Defaults to TRUE.

n.boot

The number of bootstrap samples. Defaults to 200 samples.

data

A data frame in which to interpret the variables named in the formula argument.

conf.level

Level of confidence. Defaults to 0.95 (corresponding to 95%).

z.value

The value of the covariate on the right hand side of formula at which the conditional survival probabilities are computed. For quantitative covariates, i.e. of class integer and numeric.

bw

A single numeric value to compute a kernel density bandwidth. Use "dpik" for the KernSmooth package based selector or "np" for the 'npudensbw' function of the np package.

window

A character string specifying the desired kernel. See details below for possible options. Defaults to "gaussian" where the gaussian density kernel will be used.

method.weights

A character string specifying the desired weights method. Possible options are "NW" for the Nadaraya-Watson weights and "LL" for local linear weights. Defaults to "NW".

cluster

A logical value. If TRUE (default), the bootstrap procedure for the confidence intervals is parallelized. Note that there are cases (e.g., a low number of bootstrap repetitions) that R will gain in performance through serial computation. R takes time to distribute tasks across the processors also it will need time for binding them all together later on. Therefore, if the time for distributing and gathering pieces together is greater than the time need for single-thread computing, it does not worth parallelize.

ncores

An integer value specifying the number of cores to be used in the parallelized procedure. If NULL (default), the number of cores to be used is equal to the number of cores of the machine - 1.

na.rm

A logical value indicating whether NA values should be stripped in the computation.

Author

Luis Meira-Machado and Marta Sestelo

Details

Possible options for argument window are "gaussian", "epanechnikov", "tricube", "boxcar", "triangular", "quartic" or "cosine".

References

L. Meira-Machado, M. Sestelo, and A. Goncalves (2016). Nonparametric estimation of the survival function for ordered multivariate failure time data: a comparative study. Biometrical Journal, 58(3), 623--634.

Examples

   fit <- survCOND(survCS(time1, event1, Stime, event) ~ 1, x = 365, y = 730,
   data = colonCS, method = "KMW", conf = FALSE)

   fit1 <- survCOND(survCS(time1, event1, Stime, event) ~ 1, x = 365,
   data = colonCS, method = "LDM", conf = FALSE)

   fit2 <- survCOND(survCS(time1, event1, Stime, event) ~ 1, x = 365,
   data = colonCS, method = "LDM", lower.tail = TRUE, conf = FALSE)

   fit3 <- survCOND(survCS(time1, event1, Stime, event) ~ 1, x = 365,
   y = c(730, 1095, 1460), data = colonCS, method = "LDM", presmooth = TRUE,
   lower.tail = TRUE, conf = TRUE, n.boot = 100, conf.level = 0.95,
   cluster = FALSE)

   fit4 <- survCOND(survCS(time1, event1, Stime, event) ~ rx, x = 365,
   data = colonCS, method = "LDM", conf = FALSE)

   fit5 <- survCOND(survCS(time1, event1, Stime, event) ~ factor(sex),
   x = 365, data = colonCS, method = "LDM", conf = FALSE)

  if (FALSE) {
   fit6 <- survCOND(survCS(time1, event1, Stime, event) ~ age,
   x = 365, y = 730, z.value = 48, data = colonCS, conf = TRUE)
   }