Utilizes pre-fitted time-to-event and time-to-dropout models to generate event and dropout times for ongoing subjects and new subjects. It also provides a prediction interval for the expected time to reach the target number of events.
predictEvent(
df = NULL,
target_d = NA,
newSubjects = NULL,
event_fit = NULL,
m = 5,
dropout_fit = NULL,
m_dropout = 5,
fixedFollowup = FALSE,
followupTime = 365,
pilevel = 0.9,
nyears = 4,
target_t = NA,
nreps = 500,
showEnrollment = TRUE,
showEvent = TRUE,
showDropout = FALSE,
showOngoing = FALSE,
showsummary = TRUE,
showplot = TRUE,
by_treatment = FALSE,
covariates_event = NULL,
event_fit_with_covariates = NULL,
covariates_dropout = NULL,
dropout_fit_with_covariates = NULL,
fix_parameter = FALSE
)A list of prediction results which includes important information such as the median, lower and upper percentiles for the estimated day and date to reach the target number of events, as well as simulated event data for both ongoing and new subjects. The data for the prediction plot is also included within this list. If target_t is specified, it additionally provides the median, lower, and upper percentiles of the event count at target_t, as well as the predictive probability of achieving the target number of events by target_t.
The subject-level enrollment and event data, including
trialsdt, usubjid, randdt, cutoffdt,
time, event, and dropout. The data should also
include treatment coded as 1, 2, and so on, and
treatment_description for by-treatment prediction. By default,
it is set to NULL for event prediction at the design stage.
The target number of events to reach in the study.
The enrollment data for new subjects including
draw and arrivalTime. The data should also include
treatment for prediction by treatment. By default,
it is set to NULL,
indicating the completion of subject enrollment.
The pre-fitted event model used to generate predictions.
The number of event time intervals to extrapolate the hazard function beyond the last observed event time.
The pre-fitted dropout model used to generate
predictions. By default, it is set to NULL,
indicating no dropout.
The number of dropout time intervals to extrapolate the hazard function beyond the last observed dropout time.
A Boolean variable indicating whether a fixed
follow-up design is used. By default, it is set to FALSE
for a variable follow-up design.
The follow-up time for a fixed follow-up design, in days. By default, it is set to 365.
The prediction interval level. By default, it is set to 0.90.
The number of years after the data cut for prediction. By default, it is set to 4.
The target number of days after the data cutoff used to predict both the number of events and the probability of achieving the target event count.
The number of replications for simulation. By default,
it is set to 500. If newSubjects is not NULL,
the number of draws in newSubjects should be nreps.
A Boolean variable to control whether or not to
show the number of enrolled subjects. By default, it is set to
TRUE.
A Boolean variable to control whether or not to
show the number of events. By default, it is set to
TRUE.
A Boolean variable to control whether or not to
show the number of dropouts. By default, it is set to
FALSE.
A Boolean variable to control whether or not to
show the number of ongoing subjects. By default, it is set to
FALSE.
A Boolean variable to control whether or not to
show the prediction summary. By default, it is set to TRUE.
A Boolean variable to control whether or not to
show the prediction plot. By default, it is set to TRUE.
A Boolean variable to control whether or not to
predict event by treatment group. By default,
it is set to FALSE.
The names of baseline covariates from the input data frame to include in the event model, e.g., c("age", "sex"). Factor variables need to be declared in the input data frame.
The pre-fitted event model with covariates used to generate event predictions for ongoing subjects.
The names of baseline covariates from the input data frame to include in the dropout model, e.g., c("age", "sex"). Factor variables need to be declared in the input data frame.
The pre-fitted dropout model with covariates used to generate dropout predictions for ongoing subjects.
Whether to fix parameters at the maximum likelihood estimates when generating new data for prediction. Defaults to FALSE, in which case, parameters will be drawn from their approximate posterior distribution.
Kaifeng Lu, kaifenglu@gmail.com
To ensure successful event prediction at the design stage, it is
important to provide the newSubjects data set.
To specify the event (dropout) model used during the design-stage event
prediction, the event_fit (dropout_fit) should be a list
with one element per treatment. For each treatment, the element
should include model to specify the event model
(exponential, weibull, log-logistic, log-normal, or piecewise
exponential), and theta and vtheta to indicate
the parameter values and the covariance matrix. For the piecewise
exponential event (dropout) model, the list should also include
piecewiseSurvivalTime (piecewiseDropoutTime) to indicate
the location of knots. It should be noted that the model averaging
and spline options are not appropriate for use during the design stage.
Following the commencement of the trial, we obtain the event
model fit and the dropout model fit based on the observed data,
denoted as event_fit and dropout_fit, respectively.
These fitted models are subsequently utilized to generate event
and dropout times for both ongoing and new subjects in the trial.
Emilia Bagiella and Daniel F. Heitjan. Predicting analysis times in randomized clinical trials. Stat in Med. 2001; 20:2055-2063.
Gui-shuang Ying and Daniel F. Heitjan. Weibull prediction of event times in clinical trials. Pharm Stat. 2008; 7:107-120.
# Event prediction after enrollment completion
set.seed(2000)
event_fits <- fitEvent(
df = interimData2,
event_model = "piecewise exponential",
piecewiseSurvivalTime = c(0, 140, 352))
dropout_fits <- fitDropout(
df = interimData2,
dropout_model = "exponential")
event_pred <- predictEvent(
df = interimData2, target_d = 200,
event_fit = event_fits$fit,
dropout_fit = dropout_fits$fit,
pilevel = 0.90, nreps = 100)
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