markov_sde: Predictions for continuous time, nonhomogeneous Markov...

In rstpm2: Smooth Survival Models, Including Generalized Survival Models

Predictions for continuous time, nonhomogeneous Markov multi-state models using Aalen's additive hazards models.

Description

A numerically efficient algorithm to calculate predictions from a continuous time, nonhomogeneous Markov multi-state model. The main inputs are are a list of Aalen's additive hazards models, the initial values, the transition matrix and the covariate patterns. The predictions include state occupancy probabilities and length of stay. Standard errors are calculated using the delta method. Includes differences and standardisation.

Usage

markov_sde(models, trans, newdata, init = NULL, nLebesgue = 10000 + 1, los = FALSE,
           nOut = 300, weights = 1)
## S3 method for class 'markov_sde'
standardise(x, ...)
## S3 method for class 'markov_sde'
plot(x, y, stacked=TRUE, which=c("P","L"), index=NULL,
    xlab="Time", ylab=NULL, col=2:6, border=col,
    ggplot2=FALSE, lattice=FALSE, alpha=0.2,
    strata=NULL,
    ...)
## S3 method for class 'markov_sde'
as.data.frame(x, row.names=NULL, optional=NULL, ci=TRUE,
    P.conf.type="logit", L.conf.type="log",
    P.range=c(0,1), L.range=c(0,Inf),
    ...)

Arguments

models	list of models. Currently allows only for aalen regression models.
trans	Transition matrix describing the states and transitions in the multi-state model. If S is the number of states in the multi-state model, trans should be an S x S matrix, with (i,j)-element a positive integer if a transition from i to j is possible in the multi-state model, NA otherwise. In particular, all diagonal elements should be NA. The integers indicating the possible transitions in the multi-state model should be sequentially numbered, 1,...,K, with K the number of transitions. See msprep
newdata	data.frame of the covariates to use in the predictions
init	vector of the initial values with the same length as the number of states. Defaults to the first state having an initial value of 1 (i.e. "[<-"(rep(0,nrow(trans)),1,1)).
nLebesgue	Number of steps for the continuous integration
los	logical variable for whether to estimate the length of stay
nOut	number of rows to represent the continuous changes
weights	numeric vector to represent differences or standardisation

For plot.markov_sde:

y	(currently ignored)
stacked	logical for whether to stack the plots. Default: TRUE
index	indicator of which row of newdata to plot
which	character to indicate either transition probabilities ("P") or length of stay ("L")). Default: "P".
xlab	x-axis label
ylab	x-axis label
col	colours (ignored if ggplot2=TRUE)
border	border colours for the polygon (ignored if ggplot=TRUE)
ggplot2	use ggplot2
alpha	alpha value for confidence bands (ggplot)
lattice	use lattice
strata	formula for the stratification factors for the plot

For as.data.frame.markov_sde:

row.names	add in row names to the output data-frame
optional	(not currently used)
ci	logical for whether to include confidence intervals. Default: TRUE
P.conf.type	type of transformation for the confidence interval calculation for the state occupancy probabilities. Default: logit transformation. This is changed to "identity" if any of the weights are negative
L.conf.type	type of transformation for the confidence interval calculation for the length of stay calculation. Default: log transformation. "identity" if any of the weights are negative
P.range	valid values for the state occupancy probabilities. Default: (0,1).
L.range	valid values for the state occupancy probabilities. Default: (0,Inf).

For standardise.markov_sde:

x	object to extract standardised values
...	other arguments. For plot.markov_sde, these arguments are passed to plot.default. For standardise.markov_sde, these arguments are not used, as the standardisation must be done earlier in markov_sde.

Details

Uses an approach developed by Ryalen and colleagues. This is a re-implementation in C++.

The current implementation only allows for a vector of initial values rather than a matrix. The predictions will need to be re-run for different vectors of initial values.

Value

markov_sde returns an object of class "markov_sde".

Author(s)

Mark Clements

See Also

markov_msm

rstpm2 documentation built on March 31, 2023, 8:22 p.m.