predict.JointFPM: Post-estimation function for JointFPMs

In JointFPM: A Parametric Model for Estimating the Mean Number of Events

Post-estimation function for JointFPMs

Description

Predicts different estimates from a joint flexible parametric model. Currently only the estimation of the mean number of events at different time points is supported.

Usage

## S3 method for class 'JointFPM'
predict(
  object,
  type = "mean_no",
  newdata,
  t,
  exposed = NULL,
  ci_fit = TRUE,
  method = "romberg",
  ngq = 30,
  ...
)

Arguments

object	A joint flexible parametric model of class JointFPM.
type	A character vector defining the estimate of interest. Currently available options are: mean_no: Estimates the mean number of events at time(s) t. diff: Estimates the difference in mean number of events between exposed and unexposed at time(s) t. marg_mean_no: Estimates the marginal mean number of events. marg_diff: Estimates the marginal difference in the mean number of events.
newdata	A data.frame with one row including the variable values used for t he prediction. One value for each variable used in either the recurrent or competing event model is required when predicting mean_no or diff. For marg_mean_no or marg_diff, this includes the variable that you would like your marginal estimate to be conditioned on.
t	A vector defining the time points used for the prediction.
exposed	A function that takes newdata as an argument and creates a new dataset for the exposed group. This argument is required if type = 'diff'. Please see details for more information.
ci_fit	Logical indicator for whether confidence intervals should be estimated for the fitted estimates using the delta method.
method	The method used for the underlying numerical integration procedure. Defaults to "romberg", which uses the rmutil::int() function, but it is possible to use Gaussian quadrature by setting method = "gq" instead.
ngq	Number of quadrature nodes used when method = "gq". Defaults to 30, which lead to accurate results (compared to method = "romberg") in our experience.
...	Added for compatibility with other predict functions.

Details

The function required for the exposed argument must take the newdata dataset as argument and transform it to a new dataset that defines the exposed group. Assume we assume that we have a model with one variable trt which is a 0/1 coded treatment indicator. If we would like to obtain the difference in mean number of events comparing the untreated to treated group we could use the following function assuming that newdata = data.frame(trt = 0):

function(x){transform(x, trt = 1)}

Value

A data.frame with the following columns:

t:

The time for the prediction,

fit:

The point estimate of the prediction,

lci:

The lower confidence interval limit,

uci:

The upper confidence interval limit.

Examples

bldr_model <- JointFPM(Surv(time  = start,
                            time2 = stop,
                            event = event,
                            type  = 'counting') ~ 1,
                       re_model = ~ pyridoxine + thiotepa,
                       ce_model = ~ pyridoxine + thiotepa,
                       re_indicator = "re",
                       ce_indicator = "ce",
                       df_ce = 3,
                       df_re = 3,
                       cluster  = "id",
                       data     = bladder1_stacked)

predict(bldr_model,
        newdata = data.frame(pyridoxine = 1,
                             thiotepa   = 0),
        t       =  c(10, 20),
        ci_fit  = FALSE)

JointFPM documentation built on June 22, 2024, 9:38 a.m.