Create a data table in long format from all combinations of specified tables from an object of class hesim_data and optionally time intervals. See "Details" for an explanation of how the expansion is done.
An object of class hesim_data.
A character vector of the names of the data tables in hesim_data to expand by.
Either a numeric vector of distinct times denoting the start of time intervals or a time_intervals object.
An object of class expanded_hesim_data, which is a data.table with an "id_vars"
attribute containing the names of the ID variables in the data table and, if times is
not NULL, a time_intervals object derived from times.
This function is similar to expand.grid(), but works for data frames or data tables.
Specifically, it creates a data.table from all combinations of the supplied tables in object
and optionally the start of times intervals in times.
The supplied tables are determined using the by argument. The resulting dataset is sorted by
prioritizing ID variables as follows: (i) strategy_id, (ii) patient_id,
(iii) the health-related ID variable (either state_id or transition_id, and
(iv) the time intervals from times.
strategies <- data.frame(strategy_id = c(1, 2))
patients <- data.frame(patient_id = seq(1, 3), age = c(65, 50, 75),
gender = c("Female", "Female", "Male"))
states <- data.frame(state_id = seq(1, 3),
state_var = c(2, 1, 9))
hesim_dat <- hesim_data(strategies = strategies,
patients = patients,
states = states)
expand(hesim_dat, by = c("strategies", "patients"))
#> strategy_id patient_id age gender
#> <num> <int> <num> <char>
#> 1: 1 1 65 Female
#> 2: 1 2 50 Female
#> 3: 1 3 75 Male
#> 4: 2 1 65 Female
#> 5: 2 2 50 Female
#> 6: 2 3 75 Male
expand(hesim_dat, by = c("strategies", "patients"),
times = c(0, 2, 10))
#> strategy_id patient_id time_id age gender time_start time_stop
#> <num> <int> <int> <num> <char> <num> <num>
#> 1: 1 1 1 65 Female 0 2
#> 2: 1 1 2 65 Female 2 10
#> 3: 1 1 3 65 Female 10 Inf
#> 4: 1 2 1 50 Female 0 2
#> 5: 1 2 2 50 Female 2 10
#> 6: 1 2 3 50 Female 10 Inf
#> 7: 1 3 1 75 Male 0 2
#> 8: 1 3 2 75 Male 2 10
#> 9: 1 3 3 75 Male 10 Inf
#> 10: 2 1 1 65 Female 0 2
#> 11: 2 1 2 65 Female 2 10
#> 12: 2 1 3 65 Female 10 Inf
#> 13: 2 2 1 50 Female 0 2
#> 14: 2 2 2 50 Female 2 10
#> 15: 2 2 3 50 Female 10 Inf
#> 16: 2 3 1 75 Male 0 2
#> 17: 2 3 2 75 Male 2 10
#> 18: 2 3 3 75 Male 10 Inf