Generate data for a two-arms clinical trial with longitudinal continuous outcome and two intercurrent events (ICEs). ICE1 may be thought of as a discontinuation from study treatment due to study drug or condition related (SDCR) reasons. ICE2 may be thought of as discontinuation from study treatment due to uninformative study drop-out, i.e. due to not study drug or condition related (NSDRC) reasons and outcome data after ICE2 is always missing.
Usage
simulate_data(pars_c, pars_t, post_ice1_traj, strategies = getStrategies())
Arguments
- pars_c
A
simul_pars
object as generated byset_simul_pars()
. It specifies the simulation parameters of the control arm.- pars_t
A
simul_pars
object as generated byset_simul_pars()
. It specifies the simulation parameters of the treatment arm.- post_ice1_traj
A string which specifies how observed outcomes occurring after ICE1 are simulated. Must target a function included in
strategies
. Possible choices are: Missing At Random"MAR"
, Jump to Reference"JR"
, Copy Reference"CR"
, Copy Increments in Reference"CIR"
, Last Mean Carried Forward"LMCF"
. User-defined strategies could also be added. SeegetStrategies()
for details.- strategies
A named list of functions. Default equal to
getStrategies()
. SeegetStrategies()
for details.
Value
A data.frame
containing the simulated data. It includes the following variables:
id
: Factor variable that specifies the id of each subject.visit
: Factor variable that specifies the visit of each assessment. Visit0
denotes the baseline visit.group
: Factor variable that specifies which treatment group each subject belongs to.outcome_bl
: Numeric variable that specifies the baseline outcome.outcome_noICE
: Numeric variable that specifies the longitudinal outcome assuming no ICEs.ind_ice1
: Binary variable that takes value1
if the corresponding visit is affected by ICE1 and0
otherwise.dropout_ice1
: Binary variable that takes value1
if the corresponding visit is affected by the drop-out following ICE1 and0
otherwise.ind_ice2
: Binary variable that takes value1
if the corresponding visit is affected by ICE2.outcome
: Numeric variable that specifies the longitudinal outcome including ICE1, ICE2 and the intermittent missing values.
Details
The data generation works as follows:
Generate outcome data for all visits (including baseline) from a multivariate normal distribution with parameters
pars_c$mu
andpars_c$sigma
for the control arm and parameterspars_t$mu
andpars_t$sigma
for the treatment arm, respectively. Note that for a randomized trial, outcomes have the same distribution at baseline in both treatment groups, i.e. one should setpars_c$mu[1]=pars_t$mu[1]
andpars_c$sigma[1,1]=pars_t$sigma[1,1]
.Simulate whether ICE1 (study treatment discontinuation due to SDCR reasons) occurs after each visit according to parameters
pars_c$prob_ice1
andpars_c$or_outcome_ice1
for the control arm andpars_t$prob_ice1
andpars_t$or_outcome_ice1
for the treatment arm, respectively.Simulate drop-out following ICE1 according to
pars_c$prob_post_ice1_dropout
andpars_t$prob_post_ice1_dropout
.Simulate an additional uninformative study drop-out with probabilities
pars_c$prob_ice2
andpars_t$prob_ice2
at each visit. This generates a second intercurrent event ICE2, which may be thought as treatment discontinuation due to NSDRC reasons with subsequent drop-out. The simulated time of drop-out is the subject's first visit which is affected by drop-out and data from this visit and all subsequent visits are consequently set to missing. If for a subject, both ICE1 and ICE2 are simulated to occur, then it is assumed that only the earlier of them counts. In case both ICEs are simulated to occur at the same time, it is assumed that ICE1 counts. This means that a single subject can experience either ICE1 or ICE2, but not both of them.Adjust trajectories after ICE1 according to the given assumption expressed with the
post_ice1_traj
argument. Note that only post-ICE1 outcomes in the intervention arm can be adjusted. Post-ICE1 outcomes from the control arm are not adjusted.Simulate additional intermittent missing outcome data as per arguments
pars_c$prob_miss
andpars_t$prob_miss
.
The probability of the ICE after each visit is modeled according to the following
logistic regression model:
~ 1 + I(visit == 0) + ... + I(visit == n_visits-1) + I((x-alpha))
where:
n_visits
is the number of visits (including baseline).alpha
is the baseline outcome mean. The termI((x-alpha))
specifies the dependency of the probability of the ICE on the current outcome value. The corresponding regression coefficients of the logistic model are defined as follows: The intercept is set to 0, the coefficients corresponding to discontinuation after each visit for a subject with outcome equal to the mean at baseline are set according to parameterspars_c$prob_ice1
(pars_t$prob_ice1
), and the regression coefficient associated with the covariateI((x-alpha))
is set tolog(pars_c$or_outcome_ice1)
(log(pars_t$or_outcome_ice1)
).
Please note that the baseline outcome cannot be missing nor be affected by any ICEs.