Performs enrollment and event prediction by utilizing observed data and specified enrollment and event models.
getPrediction(
df = NULL,
to_predict = "enrollment and event",
target_n = NA,
target_d = NA,
enroll_model = "b-spline",
nknots = 0,
lags = 30,
accrualTime = 0,
enroll_prior = NULL,
event_model = "model averaging",
piecewiseSurvivalTime = 0,
k = 0,
scale = "hazard",
m = 5,
event_prior = NULL,
dropout_model = "exponential",
piecewiseDropoutTime = 0,
k_dropout = 0,
scale_dropout = "hazard",
m_dropout = 5,
dropout_prior = NULL,
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,
ngroups = 1,
alloc = NULL,
treatment_label = NULL,
covariates_event = NULL,
event_prior_with_covariates = NULL,
covariates_dropout = NULL,
dropout_prior_with_covariates = NULL,
fix_parameter = FALSE
)A list that includes the fits of observed data models, as well as simulated enrollment data for new subjects and simulated event data for ongoing and new subjects.
The subject-level enrollment and event data, including
trialsdt, usubjid, randdt, and cutoffdt for
enrollment prediction, and, additionally, time, event,
and dropout for event prediction. The data should also include
treatment coded as 1, 2, and so on, and
treatment_description for enrollment and
event prediction by treatment. By default, it is set to
NULL for enrollment and event prediction at the design stage.
Specifies what to predict: "enrollment only", "event only", or "enrollment and event". By default, it is set to "enrollment and event".
The target number of subjects to enroll in the study.
The target number of events to reach in the study.
The enrollment model which can be specified as "Poisson", "Time-decay", "B-spline", or "Piecewise Poisson". By default, it is set to "B-spline".
The number of inner knots for the B-spline enrollment model. By default, it is set to 0.
The day lags to compute the average enrollment rate to carry forward for the B-spline enrollment model. By default, it is set to 30.
The accrual time intervals for the piecewise Poisson model. Must start with 0, e.g., c(0, 30) breaks the time axis into 2 accrual intervals: [0, 30) and [30, Inf). By default, it is set to 0.
The prior of enrollment model parameters.
The event model used to analyze the event data
which can be set to one of the following options:
"exponential", "Weibull", "log-logistic", "log-normal",
"piecewise exponential", "model averaging", "spline", or
"cox model".
The model averaging uses the exp(-bic/2) weighting and
combines Weibull and log-normal models. By default, it is set to
"model averaging".
A vector that specifies the time intervals for the piecewise exponential survival distribution. Must start with 0, e.g., c(0, 60) breaks the time axis into 2 event intervals: [0, 60) and [60, Inf). By default, it is set to 0.
The number of inner knots of the spline event model of
Royston and Parmar (2002). The default
k=0 gives a Weibull, log-logistic or log-normal model,
if scale is "hazard", "odds", or "normal", respectively.
The knots are chosen as equally-spaced quantiles of the log
uncensored survival times. The boundary knots are chosen as the
minimum and maximum log uncensored survival times.
If "hazard", the log cumulative hazard is modeled as a spline function. If "odds", the log cumulative odds is modeled as a spline function. If "normal", -qnorm(S(t)) is modeled as a spline function.
The number of event time intervals to extrapolate the hazard function beyond the last observed event time.
The prior of event model parameters.
The dropout model used to analyze the dropout data
which can be set to one of the following options:
"none", "exponential", "Weibull", "log-logistic", "log-normal",
"piecewise exponential", "model averaging", "spline", or
"cox model".
The model averaging uses the exp(-bic/2) weighting and
combines Weibull and log-normal models. By default, it is set to
"exponential".
A vector that specifies the time intervals for the piecewise exponential dropout distribution. Must start with 0, e.g., c(0, 60) breaks the time axis into 2 event intervals: [0, 60) and [60, Inf). By default, it is set to 0.
The number of inner knots of the spline dropout model of
Royston and Parmar (2002). The default
k_dropout=0 gives a Weibull, log-logistic or log-normal model,
if scale_dropout is "hazard", "odds", or "normal", respectively.
The knots are chosen as equally-spaced quantiles of the log
uncensored survival times. The boundary knots are chosen as the
minimum and maximum log uncensored survival times.
If "hazard", the log cumulative hazard for dropout is modeled as a spline function. If "odds", the log cumulative odds is modeled as a spline function. If "normal", -qnorm(S(t)) is modeled as a spline function.
The number of dropout time intervals to extrapolate the hazard function beyond the last observed dropout time.
The prior of dropout model parameters.
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.
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 plots. By default, it is set to TRUE.
A Boolean variable to control whether or not to
predict by treatment group. By default, it is set to FALSE.
The number of treatment groups for enrollment prediction
at the design stage. By default, it is set to 1.
It is replaced with the actual number of
treatment groups in the observed data if df is not NULL.
The treatment allocation in a randomization block.
By default, it is set to NULL, which yields equal allocation
among the treatment groups.
The treatment labels for treatments in a
randomization block for design stage prediction.
It is replaced with the treatment_description
in the observed data if df is not NULL.
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 prior of event model parameters in the presence of covariates.
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 prior of dropout model parameters in the presence of covariates.
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
For the time-decay model, the mean function is
\(\mu(t) = (\mu/\delta)(t - (1/\delta)(1 - \exp(-\delta t)))\)
and the rate function is
\(\lambda(t) = (\mu/\delta)(1 - \exp(-\delta t))\).
For the B-spline model, the daily enrollment rate is approximated as
\(\lambda(t) = \exp(B(t)' \theta)\),
where B(t) represents the B-spline basis functions.
The enroll_prior variable should be a list that
includes model to specify the enrollment model
(poisson, time-decay, or piecewise poisson),
theta and vtheta to indicate the parameter
values and the covariance matrix. One can use a very small
value of vtheta to fix the parameter values.
For the piecewise Poisson enrollment model, the list
should also include accrualTime. It should be noted
that the B-spline model is not appropriate for use as prior.
For event prediction by treatment with prior information,
the event_prior (dropout_prior) variable should be
a list with one element per treatment. For each treatment, the
element should include model to specify the event (dropout)
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, spline, and
cox model options are not appropriate for use as prior.
If the event prediction is not by treatment while the prior
information is given by treatment, then each element of
event_prior (dropout_prior) should also include
w to specify the weight of the treatment in a
randomization block. If the prediction is not by treatment and
the prior is given for the overall study, then event_prior
(dropout_prior) is a flat list with model,
theta, and vtheta. For the piecewise exponential
event (dropout) model, it should also include
piecewiseSurvivalTime (piecewiseDropoutTime) to
indicate the location of knots.
For analysis-stage enrollment and event prediction, the
enroll_prior, event_prior, and
dropout_prior are either set to NULL to
use the observed data only, or specify the prior distribution
of model parameters to be combined with observed data likelihood
for enhanced modeling flexibility.
# Event prediction after enrollment completion
set.seed(3000)
pred <- getPrediction(
df = interimData2, to_predict = "event only",
target_d = 200,
event_model = "weibull",
dropout_model = "exponential",
pilevel = 0.90, nreps = 100)
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