Package 'survHEhmc'

Title: Survival Analysis in Health Economic Evaluation using Hamiltonian Monte Carlo
Description: A module to complement the backbone structure of the package 'survHE' and expand its functionality to run survival models under a Bayesian approach (based on Hamiltonian Monte Carlo). <doi:10.18637/jss.v095.i14>.
Authors: Gianluca Baio [aut, cre], Philip Cooney [ctb], Andrew Jones [ctb]
Maintainer: Gianluca Baio <[email protected]>
License: MIT + file LICENSE
Version: 0.0.1
Built: 2024-10-24 20:25:28 UTC
Source: https://github.com/giabaio/survHEhmc

Help Index

Fit Poly-Weibull model for survival analysis of mixture hazards

Description

Runs the survival analysis using a Poly-Weibull model

Usage

poly.weibull(formula = NULL, data, ...)

Arguments

formula

a list of formulae (one for each components of the mixture. Can specify one single formula (in which case, the model is a simple Weibull regression). For example, a valid call is using formula=list(Surv(time,event)~1,Surv(time,event)~arm)
data

A data frame containing the data to be used for the analysis. This must contain data for the 'event' variable. In case there is no censoring, then event is a column of 1s.
...

Additional options (for INLA or HMC).

HMC specific options chains = number of chains to run in the HMC (default = 2) iter = total number of iterations (default = 2000) warmup = number of warmup iterations (default = iter/2) thin = number of thinning (default = 1) control = a list specifying Stan-related options, eg control=list(adapt_delta=0.85) (default = NULL) seed = the random seed (to make things replicable) pars = a vector of parameters (string, default = NA) include = a logical indicator (if FALSE, then the pars are not saved; default = TRUE) priors = a list (of lists) specifying the values for the parameters of the prior distributions in the models save.stan = a logical indicator (default = FALSE). If TRUE, then saves the data list for Stan and the model file(s)

Details

On object in the class survHE containing the following elements

Value

models

A list containing the fitted models. These contain the output from the original inference engine (flexsurv, INLA or rstan). Can be processed using the methods specific to the original packages, or via survHE-specific methods (such as plot, print) or other specialised functions (eg to extrapolate the survival curves, etc).

model.fitting

A list containing the output of the model-fit statistics (AIC, BIC, DIC). The AIC and BIC are estimated for all methods, while the DIC is only estimated when using Bayesian inference.

method

A string indicating the method used to fit the model, ie 'mle', 'inla' or 'hmc'.

misc

A list containing the time needed to run the model(s) (in seconds), the formula used, the results of the Kaplan-Meier analysis (which is automatically performed using npsurv) and the original data frame.

Note

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Author(s)

Gianluca Baio

References

G Baio (2019). survHE: Survival analysis for health economic evaluation and cost-effectiveness modelling. Journal of Statistical Software (2020). vol 95, 14, 1-47. doi:10.18637/jss.v095.i14

See Also

fit.models, make.surv

Examples

## Not run: 


#See Baio (2019) for extended example

for a given formula and dataset

Description

for a given formula and dataset

Usage

runHMC(x, exArgs)

Arguments

x

a (vector of) string(s) containing the name(s) of the model(s) to be fitted
exArgs

a list of extra arguments passed from the main 'fit.models' function

Note

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Author(s)

Gianluca Baio

References

Baio (2020). survHE

See Also

fit.models