lagged | R Documentation |
These functions create new columns in an input data table for covariate histories. Users must specify which covariates are to be used in the history functions.
lagged(
pool,
histvars,
histvals,
time_name,
t,
id_name,
baselags,
below_zero_indicator
)
cumavg(pool, histvars, time_name, t, id_name, below_zero_indicator)
lagavg(
pool,
histvars,
histvals,
time_name,
t,
id_name,
baselags,
below_zero_indicator
)
pool |
Data table containing all information prior to time |
histvars |
Vector of character strings specifying the names of the variables for which history functions are to be applied. |
histvals |
For |
time_name |
Character string specifying the name of the time variable in |
t |
Integer specifying the current time index. |
id_name |
Character string specifying the name of the ID variable in |
baselags |
Logical scalar for specifying the convention used for lagi and lag_cumavgi terms in the model statements when pre-baseline times are not
included in |
below_zero_indicator |
Logical scalar indicating whether the observed data set contains rows for time |
lagged
creates new columns for lagged versions of existing
variables in the dataset. The user must specify which variables are to be lagged.
cumavg
creates new columns for the cumulative average up until
time t
of existing variables in the dataset.
lagavg
creates new columns for the "lagged cumulative average"
(cumulative average up until time t, then lagged by one time unit) up until time t
of existing
variables in the dataset.
No value is returned. The data table pool
is modified in place.
## Estimating the effect of static treatment strategies on risk of a
## failure event
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
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,
int_descript = int_descript,
histories = histories, histvars = histvars,
basecovs = c('L3'), nsimul = nsimul,
seed = 1234)
gform_basic
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