three_state_mm {survHE}

three_state_mm

Description

General purpose function to run a standard three-state Markov model (typically used in cancer modelling). The states are typically 'Pre-progression', 'Progressed' and 'Death'. No backward transition from 'Progressed' to 'Pre-progression' is allowed and 'Death' is obviously an absorbing state. All other transitions are possible. The crucial assumption is that individual-level data are available recording an indicator and the time of progression and death for each individual. The function returns the full transition matrix

Usage

three_state_mm(
  m_12,
  m_13,
  m_23,
  nsim = 1,
  start = c(1000, 0, 0),
  basecase = FALSE,
  ...
)

Arguments

m_12

A 'survHE' object (output to a call to fit.models) estimating the parameters of a model for the transition from 'Pre-progression' (state 1) to 'Progressed' (state 2). Given the individual level data with the complete event history (in the object 'data'), can be done with a call like 'x=make_data_multi_state(data)' and then fit.models(Surv(time,status)~...,data=x %>% filter(trans==1),...)

m_13

A 'survHE' object (output to a call to fit.models) estimating the parameters of a model for the transition from 'Pre-progression' (state 1) to 'Death' (state 3). Given the individual level data with the complete event history (in the object 'data'), can be done with a call like 'x=make_data_multi_state(data)' and then fit.models(Surv(time,status)~...,data=x %>% filter(trans==2),...)

m_23

A 'survHE' object (output to a call to fit.models) estimating the parameters of a model for the transition from 'Progressed' (state 2) to 'Death' (state 3). Given the individual level data with the complete event history (in the object 'data'), can be done with a call like 'x=make_data_multi_state(data)' and then fit.models(Surv(time,status)~...,data=x %>% filter(trans==3),...)

nsim

The number of simulations for the model parameters that are used to compute the survival curves. Defaults to nsim=1, which simply creates one survival curve for each treatment arm.

start

A vector of initial state occupancy. By default assumes 1000 individuals, all initially allocated to 'Pre-progression'

basecase

Should the base case be computed as well, based on the point estimate of the underlying model parameters? (Default=FALSE)

...

additional arguments.

Value

A list including the state occupancy simulations in an object 'm'. This is a tibble with the number of individuals in each of the 3 states at each of the times specified by the user. If nsim>1, then the tibble also contains a simulation index to keep track of that. The list also includes the computation time to obtain the state occupancy tibble (in the object 'running_time'). If basecase==TRUE, then the function also computes the "base case scenario" (based on 1 simulation from of the underlying survival curves, i.e. the point estimate of the model parameters) and stores it in the object 'base_case'

Note

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Author(s)

Gianluca Baio

References

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See Also

make.transition.probs make_data_multi_state

Examples

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