`heemod`

This document is a presentation of the basic steps to define and run a model in `heemod`

. Note that decision trees are actually a subset even of *Markov model*, and thus can be specified easily with this package.

When building a Markov model the following steps must be performed:

- Specify transition probabilities between states.
- Specify values attached to states (costs, utilities, etc.).
- Combine this information and run the model.

The probability to transition from one state to another during a time period is called a *transition probability*. Said time period is called a *cycle*.

Transition probabilities between states can be described through a 2-way table where the lines correspond to the states at the beginning of a cycle and the columns to the states at the end of a cycle. Consider a model with 2 states `A`

and `B`

:

A | B | |
---|---|---|

A |
1 | 2 |

B |
3 | 4 |

When starting a cycle in state **A** (row **A**), the probability to still be in state **A** at the end of the cycle is found in colunm **A** (cell **1**) and the probability to change to state **B** is found in column **B** (cell **2**).

Similarly, when starting a cycle from state **B** (row **B**), the probability to be in state **A** or **B** at the end of the cycle are found in cells **3** or **4** respectively.

In the context of Markov models, this 2-way table is called a *transition matrix*. A transition matrix can be defined easily in `heemod`

with the `define_transition()`

function. If we consider the previous example, where cell values have been replaced by actual probabilities:

A | B | |
---|---|---|

A |
0.9 | 0.1 |

B |
0.2 | 0.8 |

That transition matrix can be defined with the following command:

`## No named state -> generating names.`

```
## A transition matrix, 2 states.
##
## A B
## A 0.9 0.1
## B 0.2 0.8
```

Values are attached to states. Cost and utility are classical examples of such values. To continue with the previous example, the following values can be attachd to state **A** and **B**:

- State
**A**has a cost of*1234*per cycle and an utility of*0.85*. - State
**B**has a cost of*4321*per cycle and an utility of*0.50*.

A state and its values can be defined with `define_state()`

:

```
## A state with 2 values.
##
## cost = 1234
## utility = 0.85
```

```
## A state with 2 values.
##
## cost = 4321
## utility = 0.5
```

Now that the transition matrix and the state values are defined for a given strategy, we can combine them with `define_strategy()`

:

`## No named state -> generating names.`

```
## A Markov model strategy:
##
## 2 states,
## 2 state values
```

A model is a made of one or more strategy, run with `run_model()`

for a given number of cycles. Here we run only one strategy, so no comparison of the cost-effectiveness of the different strategies will be made. The variables corresponding to valuation of cost and effect must be given at that point.

`## No named model -> generating names.`

```
## 1 strategy run for 10 cycles.
##
## Initial state counts:
##
## A = 1000L
## B = 0L
##
## Counting method: 'life-table'.
##
## Values:
##
## cost utility
## I 19796856 7654.552
```

By default the model is run for *1000* persons starting in the first state (here state **A**).

We can plot the state membership counts over time. Other plot types are available.

Plots can be modified using `ggplot2`

syntax.

```
library(ggplot2)
plot(res_mod) +
xlab("Time") +
ylab("N") +
theme_minimal() +
scale_color_brewer(
name = "State",
palette = "Set1"
)
```

And black & white plots for publication are available with the `bw`

plot option

The state membership counts and the values can be accessed with `get_counts()`

and `get_values()`

respectively.

```
## .strategy_names markov_cycle state_names count
## 1 I 1 A 950.0000
## 2 I 2 A 865.0000
## 3 I 3 A 805.5000
## 4 I 4 A 763.8500
## 5 I 5 A 734.6950
## 6 I 6 A 714.2865
```

```
## markov_cycle .strategy_names value_names value
## 1 1 I cost 1388350
## 2 2 I cost 1650745
## 3 3 I cost 1834422
## 4 4 I cost 1962995
## 5 5 I cost 2052997
## 6 6 I cost 2115998
```

Convenience functions are available to easily compute transition probabilities from indidence rates, OR, RR, or probabilities estimated on a different timeframe.

Example : convert an incidence rate of *162* cases per *1,000* person-years to a 5-year probability.

`## [1] 0.5551419`

See `?probability`

to see a list of the convenience functions available.

Mortality rates by age and sex (often used as transition probabilities) can be downloaded from the WHO online database with the function `get_who_mr()`

.

External data contained in user-defined data frames can be referenced in a model with the function `look_up()`

.

In order to compare different strategies it is possible to run a model with multiple strategies in parallel, examples are provided in `vignette("c-homogeneous", "heemod")`

or `vignette("d-non-homogeneous", "heemod")`

.

Time varying transition probabilities and state values are available through the `markov_cycle`

and `state_cycle`

variables. Time-varying probabilities and values are explained in `vignette("b-time-dependency", "heemod")`

.

Transition costs between states can be defined by adding an additional state called a transition state. This is a special case of a tunnel state. A situation where the transtion from `A`

to `B`

(noted `A->B`

) costs $100 and reduces utility by 0.1 points compared to the usual values of `B`

can be modelled by adding a state `B_trans`

.

The cost and utility of `B_trans`

are the same as those of `B`

+$100 and -0.1 QALY respectively. `A->B_trans`

is equal to the former value of `A->B`

. `A->B`

is set to 0 (all transitions from `A`

to `B`

must pass through `B_trans`

from now on). The probability to stay in `B_trans`

is 0, `B_trans->B`

is equal to `B->B`

and similarly all `B_trans->*`

are equal to `B->*`

.

Some operation such as PSA can be significantly sped up using parallel computing. This can be done in the following way:

- Define a cluster with the
`use_cluster()`

functions (i.e.`use_cluster(4)`

to use 4 cores). - Run the analysis as usual.
- To stop using parallel computing use the
`close_cluster()`

function.

Results may vary depending on the machine, but we found speed gains to be quite limited beyond 4 cores.

Probabilistic uncertainty analysis `vignette("e-probabilistic", "heemod")`

and deterministic sensitivity analysis `vignette("f-sensitivity", "heemod")`

can be performed. Population-level estimates and heterogeneity can be computed : `vignette("g-heterogeneity", "heemod")`

.