simple_restriction function

Simple Restriction

This function assists the implementation of a restriction on a covariate in the data table newdf by setting lines where the covariate is restricted to a user-specified value.

simple_restriction(newdf, pool, restriction, time_name, t, ...)

Arguments

• newdf: Data table containing the simulated data at time $t$.
• pool: Data table containing the simulated data at times before $t$.
• restriction: List of vectors. Each vector contains as its first entry the covariate affected by the restriction; its second entry the condition that must be TRUE for the covariate to be modeled; its third entry a function that executes other specific actions based on the condition (in this case, this function); and its fourth entry some value used by the function (in this case, the value the user desires to assign to the covariate when it is not modeled).
• time_name: Character string specifying the name of the time variable in pool and newdf.
• t: Integer specifying the current time index.
• ...: This argument is not used in this function.

Returns

No value is returned. The data table newdf is modified in place.

Examples

## Estimating the effect of static treatment strategies on risk of a
## failure event with a simple restriction

id <- 'id'
time_points <- 7
time_name <- 't0'
covnames <- c('L1', 'L2', 'A')
outcome_name <- 'Y'
outcome_type <- 'survival'
covtypes <- c('binary', 'bounded normal', 'binary')
histories <- c(lagged, lagavg)
histvars <- list(c('A', 'L1', 'L2'), c('L1', 'L2'))
covparams <- list(covmodels = c(L1 ~ lag1_A + lag_cumavg1_L1 + lag_cumavg1_L2 +
                                  L3 + t0,
                                L2 ~ lag1_A + L1 + lag_cumavg1_L1 +
                                  lag_cumavg1_L2 + L3 + t0,
                                A ~ lag1_A + L1 + L2 + lag_cumavg1_L1 +
                                  lag_cumavg1_L2 + L3 + t0))
ymodel <- Y ~ A + L1 + L2 + L3 + lag1_A + lag1_L1 + lag1_L2 + t0
intervention1.A <- list(static, rep(0, time_points))
intervention2.A <- list(static, rep(1, time_points))
int_descript <- c('Never treat', 'Always treat')
nsimul <- 10000

# At t0 == 5, assume we have deterministic knowledge that L1 equals 0
restrictions <- list(c('L1', 't0 != 5', simple_restriction, 0))

gform_basic <- gformula(obs_data = basicdata_nocomp, id = id,
                        time_points = time_points,
                        time_name = time_name, covnames = covnames,
                        outcome_name = outcome_name,
                        outcome_type = outcome_type, covtypes = covtypes,
                        covparams = covparams, ymodel = ymodel,
                        intervention1.A = intervention1.A,
                        intervention2.A = intervention2.A,
                        restrictions = restrictions,
                        int_descript = int_descript,
                        histories = histories, histvars = histvars,
                        basecovs = c('L3'), nsimul = nsimul,
                        seed = 1234)
gform_basic