mcf: Mean Cumulative Function (MCF)
In reda: Recurrent Event Data Analysis

mcf	R Documentation

Mean Cumulative Function (MCF)

Description

An S4 class generic function that returns the mean cumulative function (MCF) estimates from a fitted model or returns the nonparametric MCF estimates (by Nelson-Aalen estimator or Cook-Lawless cumulative sample mean estimator) from the sample data.

Usage

mcf(object, ...)

## S4 method for signature 'formula'
mcf(
  object,
  data,
  subset,
  na.action,
  variance = c("LawlessNadeau", "Poisson", "bootstrap", "CSV", "none"),
  logConfInt = FALSE,
  adjustRiskset = TRUE,
  level = 0.95,
  control = list(),
  ...
)

## S4 method for signature 'rateReg'
mcf(
  object,
  newdata,
  groupName,
  groupLevels,
  level = 0.95,
  na.action,
  control = list(),
  ...
)

Arguments

`object`	An object used to dispatch a method.
`...`	Other arguments for future usage.
`data`	A data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from `environment(formula)`, usually the environment from which the function is called.
`subset`	An optional vector specifying a subset of observations to be used in the fitting process.
`na.action`	A function that indicates what should the procedure do if the data contains `NA`s. The default is set by the na.action setting of `options`. The "factory-fresh" default is `na.omit`. Other possible values inlcude `na.fail`, `na.exclude`, and `na.pass`. `help(na.fail)` for details.
`variance`	A character specifying the method for variance estimates. The available options are `"LawlessNadeau"` (the default) for Lawless and Nadeau (1995) method, `"Poisson"` for Poisson process method, `"bootstrap"` for bootstrap method, `"CSV"` for variance estimates of the corresponding cumulative sample mean function (CSM) by the cumulative sample variance method (Cook and Lawless, 2007), and `"none"` for no variance estimates. Partial matching on the names is allowed.
`logConfInt`	A logical value. If `FALSE` (the default), the confidence interval are constructed based on the normality of the MCF estimates. Otherwise, the confidence intervals of given level are constucted based on the normality of logarithm of the MCF estimates.
`adjustRiskset`	A logical value indicating whether to adjust the size of risk-set. If `TRUE` by default, the size of risk-set will be adjusted based on at-risk indicators and Nelson-Aalen estimator will be returned. Otherwise, the cumulative sample mean (CSM) function given by Cook and Lawless (2007) will be returned without adjustment on size of risk-set.
`level`	An optional numeric value indicating the confidence level required. The default value is 0.95.
`control`	An optional named list specifying other options. For `rateReg` object, it can be used to specify the time grid where the MCF is estimated. The available named elements are given as follows: `grid`: The time grid where MCF is estimated. A dense grid is suggested for further using the plot method. `length.out`: The length of grid points. The dafault value is 1,000. `from`: The starting point of grid. The default value is the left boundary knots (for `rateReg` object). `to`: The endpoint of grid. The default value is the right boundary knots (for `rateReg` object). The option `length.out`, `from`, `to` will be ignored if `grid` is specified directly. Otherwise, the grid will be generated by function `seq.int` with specified `from`, `to` and `length.out`. For formula method, the available named elements are given as follows: `B`: The number of bootstrap replicates for using bootstrap method for variance estimates of sample MCF estimates. The default value is 200. `se.method`: The method used for SE estimates for bootstrap. The available methods include `"sample.se"` (the default) and `"normality"`. The former takes the sample SE of point estimates from bootstrap samples; The latter estimates SE based on interquantile and normality assumption. `ci.method`: The method used for confidence interval (CI) for bootstrap. The available options include `"normality"` (the default) and `"percentile"`. The former estimates the CI based on SE estimates and normality assumption; The latter takes percentiles of the bootstrap estimates. `keep.data`: A logical value specifying whether to keep the processed data in the output object. If `TRUE` (the default), the processed data will be kept in the output and available for later usage. Otherwise, an empty data frame object will be returned in the `data` slot. `FALSE` may be set when the memory consumption is of concern and we only need MCF estimates. For example, the function `mcfDiff` and `mcfDiff.test` will not be applicable for the `mcf.formula` object with an empty `data` slot. `verbose`: A logical value. The default value is `TRUE`. If `FALSE`, possible data checking messages (not including warnings or errors) will be suppressed.
`newdata`	An optional data frame. If specified, the data frame should have the same column names as the covariate names appearing in the formula of original fitting.
`groupName`	An optional length-one charactor vector to specify the name for grouping each unique row in `newdata`, such as "gender" for "male" and "female". The default value is "Group".
`groupLevels`	An optional charactor vector to specify the levels for each unique row in `newdata`, such as "treatment" and "control". The default values are `"Level"` followed by a numeric sequence with length of number of levels.

Details

For formula object with Recur object as response, the covariate specified at the right hand side of the formula should be either 1 or any "linear" conbination of categorical variable in the data. The former computes the overall sample MCF. The latter computes the sample MCF for each level of the combination of the categorical variable(s) specified, respectively.

The MCF estimates are computed on each unique time point of the sample data. By default, the size of risk set is adjusted over time based on the at-risk indicators, which results in the Nelson-Aalen nonparametric estimator (Nelson 2003). If the size of risk set remains a constant (total number of processes) over time (specified by adjustRiskset = FALSE), the cumulative sample mean (CSM) function introduced in Chapter 1 of Cook and Lawless (2007) will be computed instead. The point estimate of sample MCF at each time point does not assume any particular underlying model. The variance estimates at each time point is computed following the Lawless and Nadeau method (LawLess and Nadeau 1995), the Poisson process method, or the bootstrap methods. The approximate confidence intervals are provided as well, which are constructed based on the asymptotic normality of the MCF itself (by default) or the logarithm of MCF.

For rateReg object, mcf estimates the baseline MCF and its confidence interval at each time grid if argument newdata is not specified. Otherwise, mcf estimates MCF and its confidence interval for the given newdata based on Delta-method.

Value

A mcf.formula or mcf.rateReg object.

A brief description of the slots of a mcf.formula object is given as follows:

formula: Model Formula.
data: Processed data based on the model formula or an empty data frame if keep.data is set to be FALSE.
MCF: A data frame containing estimates for sample MCF.
origin: Time origins.
multiGroup: A logical value indicating whether MCF is estimated for different groups respectively.
logConfInt: A logical value indicating whether the variance estimates are based on the normality of logarithm of the MCF estimates.
level: Confidence level specified.

Most slots of a mcf.rateReg object are inherited from the input rateReg object. A brief description of other slots is given as follows:

newdata: Given dataset used to estimate MCF.
MCF: A data frame containing MCF estimates.
level: Confidence level specified.
na.action: The way handling missing values.
control: The control list.
multiGroup: A logical value indicating whether MCF is estimated for different groups respectively.

Functions

mcf,formula-method: Sample MCF from data.
mcf,rateReg-method: Estimated MCF from a fitted model.

References

Cook, R. J., and Lawless, J. (2007). The statistical analysis of recurrent events, Springer Science & Business Media.

Lawless, J. F. and Nadeau, C. (1995). Some Simple Robust Methods for the Analysis of Recurrent Events. Technometrics, 37, 158–168.

Nelson, W. B. (2003). Recurrent Events Data Analysis for Product Repairs, Disease Recurrences, and Other Applications (Vol. 10). SIAM.

Examples

library(reda)

### sample MCF
## Example 1. valve-seat data
## the default variance estimates by Lawless and Nadeau (1995) method
valveMcf0 <- mcf(Recur(Days, ID, No.) ~ 1, data = valveSeats)
plot(valveMcf0, conf.int = TRUE, mark.time = TRUE, addOrigin = TRUE) +
    ggplot2::xlab("Days") + ggplot2::theme_bw()

## variance estimates following Poisson process model
valveMcf1 <- mcf(Recur(Days, ID, No.) ~ 1,
                 data = valveSeats, variance = "Poisson")
## variance estimates by bootstrap method (with 1,000 bootstrap samples)
set.seed(123)
valveMcf2 <- mcf(Recur(Days, ID, No.) ~ 1,
                 data = valveSeats, variance = "bootstrap",
                 control = list(B = 200))

## comparing the variance estimates from different methods
library(ggplot2)
ciDat <- rbind(cbind(valveMcf0@MCF, Method = "Lawless & Nadeau"),
               cbind(valveMcf1@MCF, Method = "Poisson"),
               cbind(valveMcf2@MCF, Method = "Bootstrap"))
ggplot(ciDat, aes(x = time, y = se)) +
    geom_step(aes(color = Method, linetype = Method)) +
    xlab("Days") + ylab("SE estimates") + theme_bw()

## comparing the confidence interval estimates from different methods
ggplot(ciDat, aes(x = time)) +
    geom_step(aes(y = MCF)) +
    geom_step(aes(y = lower, color = Method, linetype = Method)) +
    geom_step(aes(y = upper, color = Method, linetype = Method)) +
    xlab("Days") + ylab("Confidence intervals") + theme_bw()


## Example 2. the simulated data
simuMcf <- mcf(Recur(time, ID, event) ~ group + gender,
               data = simuDat, ID %in% 1 : 50)
plot(simuMcf, conf.int = TRUE, lty = 1 : 4,
     legendName = "Treatment & Gender")

### estimate MCF difference between two groups
## one sample MCF object of two groups
mcf0 <- mcf(Recur(time, ID, event) ~ group, data = simuDat)
## two-sample pseudo-score tests
mcfDiff.test(mcf0)
## difference estimates over time
mcf0_diff <- mcfDiff(mcf0, testVariance = "none")
plot(mcf0_diff)

## or explicitly ask for the difference of two sample MCF
mcf1 <- mcf(Recur(time, ID, event) ~ 1, data = simuDat,
            subset = group %in% "Contr")
mcf2 <- mcf(Recur(time, ID, event) ~ 1, data = simuDat,
            subset = group %in% "Treat")
## perform two-sample tests and estimate difference at the same time
mcf12_diff1 <- mcfDiff(mcf1, mcf2)
mcf12_diff2 <- mcf1 - mcf2   # or equivalently using the `-` method
stopifnot(all.equal(mcf12_diff1, mcf12_diff2))
mcf12_diff1
plot(mcf12_diff1)

### For estimated MCF from a fitted model,
### see examples given in function rateReg.

reda documentation built on July 9, 2022, 1:06 a.m.