Run Markov Model — run_model • heemod

Runs one or more strategy. When more than one strategy is provided, all strategies should have the same states and state value names.

run_model(
  ...,
  parameters = define_parameters(),
  init = c(1000L, rep(0L, get_state_number(get_states(list(...)[[1]])) - 1)),
  cycles = 1,
  method = c("life-table", "beginning", "end"),
  cost = NULL,
  effect = NULL,
  state_time_limit = NULL,
  central_strategy = NULL,
  inflow = rep(0L, get_state_number(get_states(list(...)[[1]])))
)

run_model_(
  uneval_strategy_list,
  parameters,
  init,
  cycles,
  method,
  cost,
  effect,
  state_time_limit,
  central_strategy,
  inflow
)

Arguments

...	One or more `uneval_model` object.
parameters	Optional. An object generated by `define_parameters()`.
init	numeric vector or result of `define_init()`, same length as number of states. Number of individuals in each state at the beginning.
cycles	positive integer. Number of Markov Cycles to compute.
method	Counting method. See details.
cost	Names or expression to compute cost on the cost-effectiveness plane.
effect	Names or expression to compute effect on the cost-effectiveness plane.
state_time_limit	Optional expansion limit for `state_time`, see details.
central_strategy	character. The name of the strategy at the center of the cost-effectiveness plane, for readability.
inflow	numeric vector or result of `define_inflow()`, similar to `init`. Number of new individuals in each state per cycle.
uneval_strategy_list	List of models, only used by `run_model_()` to avoid using `...`.

Value

A list of evaluated models with computed values.

Details

In order to compute comparisons strategies must be similar (same states and state value names). Thus strategies can only differ through transition matrix cell values and values attached to states (but not state value names).

The initial number of individuals in each state and the number of cycle will be the same for all strategies

state_time_limit can be specified in 3 different ways:

As a single value: the limit is applied to all states in all strategies. 2. As a named vector (where names are state names): the limits are applied to the given state names, for all strategies. 3. As a named list of named vectors: the limits are applied to the given state names for the given strategies.

Counting method represents where the transition should occur, based on https://journals.sagepub.com/doi/10.1177/0272989X09340585: "beginning" overestimates costs and "end" underestimates costs.

Examples

# running a single model

mod1 <-
  define_strategy(
    transition = define_transition(
      .5, .5,
      .1, .9
    ),
    define_state(
      cost = 543,
      ly = 1
    ),
    define_state(
      cost = 432,
      ly = 1
    )
  )
#> No named state -> generating names.
#> No named state -> generating names.


res <- run_model(
  mod1,
  init = c(100, 0),
  cycles = 2,
  cost = cost,
  effect = ly
)
#> No named model -> generating names.

# running several models
mod2 <-
  define_strategy(
    transition = define_transition(
      .5, .5,
      .1, .9
    ),
    define_state(
      cost = 789,
      ly = 1
    ),
    define_state(
      cost = 456,
      ly = 1
    )
    
  )
#> No named state -> generating names.
#> No named state -> generating names.


res2 <- run_model(
  mod1, mod2,
  init = c(100, 0),
  cycles = 10,
  cost = cost,
  effect = ly
)
#> No named model -> generating names.