EventPredInCure-package: Event Prediction Including Cured Population
In EventPredInCure: Event Prediction Including Cured Population
EventPredInCure-package R Documentation
Event Prediction Including Cured Population
Description
Predicts enrollment and events
using assumed enrollment and/or treatment-specific time-to-event models
in the existence of the cured population. Calculate test statistics based on
the simulated data sets from the assumed enrollment and/or treatment-specific
time-to-event models in the existence of the cured population.
Details
Accurately predicting the date at which a target number
of subjects or events will be achieved is critical for the planning,
monitoring, and execution of clinical trials in the existence of the cured
population. The EventPredInCure package provides enrollment and event
prediction capabilities using assumed enrollment and treatment-specific
time-to-event models and calculate test statistics based on the simulated
data sets from the assumed enrollment and/or treatment-specific time-to-event
models in the existence of the cured population.
At the design stage, enrollment is often specified using a
piecewise Poisson process with a constant enrollment rate
during each specified time interval. At the analysis stage,
before enrollment completion, the EventPredInCure package
considers several models, including the homogeneous Poisson
model, the time-decay model with an enrollment
rate function lambda(t) = mu/delta*(1 - exp(-delta*t)),
the B-spline model with the daily enrollment rate
lambda(t) = exp(B(t)*theta), and the piecewise Poisson model.
If prior information exists on the model parameters, it can
be combined with the likelihood to yield the posterior distribution.
The EventPredInCure package offers several time-to-event models
without cured-population,including exponential, Weibull, log-logistic,
log-normal, piecewise exponential, model averaging of Weibull and log-normal,
and spline. The models including exponential, Weibull, log-logistic,
log-normal, piecewise exponential are extended to account cured-population.
In the design stage, the models including exponential, Weibull, log-logistic,
log-normal, piecewise exponential are also extended for delayed treatment
effect setting (only for generating simulated data sets in the design stage).
For time to dropout, the same set of model without cured-population and
delayed treatment effect options are considered. If enrollment is complete,
ongoing subjects who have not had the event of interest or dropped out of the
study before the data cut contribute additional events in the future.
Their event times are generated from the conditional distribution given that
they have survived at the data cut. For new subjects that need to be enrolled,
their enrollment time and event time can be generated from the
specified enrollment and time-to-event models with parameters
drawn from the posterior distribution. Time-to-dropout can be
generated in a similar fashion.
The EventPredInCure package displays the Akaike Information
Criterion (AIC), the Bayesian Information
Criterion (BIC) and a fitted curve overlaid with observed data
to help users select the most appropriate model for enrollment
and event prediction. Prediction intervals in the prediction plot
can be used to measure prediction uncertainty, and the simulated
enrollment and event data can be used for further data exploration.
The most useful function in the EventPredInCure package is
getPrediction, which combines model fitting, data simulation,
and a summary of simulation results. Other functions perform
individual tasks and can be used to select an appropriate
prediction model.
The EventPredInCure package implements a model
parameterization that enhances the asymptotic normality of
parameter estimates. Specifically, the package utilizes the
following parameterization to achieve this goal:
Enrollment models
Poisson: theta = log(rate)
Time-decay: theta = c(log(mu), log(delta))
B-spline: no reparametrization is needed. The
knots as considered fixed.
Piecewise Poisson: theta = log(rates).
The left endpoints of time intervals, denoted as
accrualTime, are considered fixed.
Event or dropout models
Exponential: theta = log(rate)
Weibull: theta = c(log(scale), -log(shape))
Log-logistic: theta = c(log(scale), -log(shape))
Log-normal: theta = c(meanlog, log(sdlog))
Piecewise exponential: theta = log(rates).
The left endpoints of time intervals, denoted as
piecewiseSurvivalTime for event model and
piecewiseDropoutTime for dropout model, are
considered fixed.
Model averaging: theta = c(log(weibull$scale),
-log(weibull$shape), lnorm$meanlog, log(lnorm$sdlog)).
The covariance matrix for theta is structured
as a block diagonal matrix, with the upper-left block
corresponding to the Weibull component and the
lower-right block corresponding to the log-normal
component. In other words, the covariance matrix is
partitioned into two distinct blocks, with no
off-diagonal elements connecting the two components.
The weight assigned to the Weibull component, denoted as
w1, is considered fixed.
Spline: theta corresponds to the coefficients
of basis vectors. The knots and scale
are considered fixed. The scale can be hazard,
odds, or normal, corresponding to extensions of Weibull,
log-logistic, and log-normal distributions, respectively.
The EventPredInCure package uses days as its primary time unit.
If you need to convert enrollment or event rates per month to
rates per day, simply divide by 30.4375.
References
Chen, Tai-Tsang. "Predicting analysis times in randomized clinical trials with cancer immunotherapy."
BMC medical research methodology 16.1 (2016): 1-10.
Royston, Patrick, and Mahesh KB Parmar.
"Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data,
with application to prognostic modelling and estimation of treatment effects."
Statistics in medicine 21.15 (2002): 2175-2197.
Bagiella, Emilia, and Daniel F. Heitjan. "Predicting analysis times in randomized clinical trials."
Statistics in medicine 20.14 (2001): 2055-2063.
Ying, Gui‐shuang, and Daniel F. Heitjan. "Weibull prediction of event times in clinical trials."
Pharmaceutical Statistics:
The Journal of Applied Statistics in the Pharmaceutical Industry 7.2 (2008): 107-120.
Zhang, Xiaoxi, and Qi Long. "Stochastic modeling and prediction for accrual in clinical trials."
Statistics in Medicine 29.6 (2010): 649-658.
EventPredInCure documentation built on May 29, 2024, 11:04 a.m.
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| EventPredInCure-package | R Documentation |
Event Prediction Including Cured Population
Description
Predicts enrollment and events using assumed enrollment and/or treatment-specific time-to-event models in the existence of the cured population. Calculate test statistics based on the simulated data sets from the assumed enrollment and/or treatment-specific time-to-event models in the existence of the cured population.
Details
Accurately predicting the date at which a target number
of subjects or events will be achieved is critical for the planning,
monitoring, and execution of clinical trials in the existence of the cured
population. The EventPredInCure package provides enrollment and event
prediction capabilities using assumed enrollment and treatment-specific
time-to-event models and calculate test statistics based on the simulated
data sets from the assumed enrollment and/or treatment-specific time-to-event
models in the existence of the cured population.
At the design stage, enrollment is often specified using a
piecewise Poisson process with a constant enrollment rate
during each specified time interval. At the analysis stage,
before enrollment completion, the EventPredInCure package
considers several models, including the homogeneous Poisson
model, the time-decay model with an enrollment
rate function lambda(t) = mu/delta*(1 - exp(-delta*t)),
the B-spline model with the daily enrollment rate
lambda(t) = exp(B(t)*theta), and the piecewise Poisson model.
If prior information exists on the model parameters, it can
be combined with the likelihood to yield the posterior distribution.
The EventPredInCure package offers several time-to-event models
without cured-population,including exponential, Weibull, log-logistic,
log-normal, piecewise exponential, model averaging of Weibull and log-normal,
and spline. The models including exponential, Weibull, log-logistic,
log-normal, piecewise exponential are extended to account cured-population.
In the design stage, the models including exponential, Weibull, log-logistic,
log-normal, piecewise exponential are also extended for delayed treatment
effect setting (only for generating simulated data sets in the design stage).
For time to dropout, the same set of model without cured-population and
delayed treatment effect options are considered. If enrollment is complete,
ongoing subjects who have not had the event of interest or dropped out of the
study before the data cut contribute additional events in the future.
Their event times are generated from the conditional distribution given that
they have survived at the data cut. For new subjects that need to be enrolled,
their enrollment time and event time can be generated from the
specified enrollment and time-to-event models with parameters
drawn from the posterior distribution. Time-to-dropout can be
generated in a similar fashion.
The EventPredInCure package displays the Akaike Information
Criterion (AIC), the Bayesian Information
Criterion (BIC) and a fitted curve overlaid with observed data
to help users select the most appropriate model for enrollment
and event prediction. Prediction intervals in the prediction plot
can be used to measure prediction uncertainty, and the simulated
enrollment and event data can be used for further data exploration.
The most useful function in the EventPredInCure package is
getPrediction, which combines model fitting, data simulation,
and a summary of simulation results. Other functions perform
individual tasks and can be used to select an appropriate
prediction model.
The EventPredInCure package implements a model
parameterization that enhances the asymptotic normality of
parameter estimates. Specifically, the package utilizes the
following parameterization to achieve this goal:
Enrollment models
Poisson:
theta = log(rate)Time-decay:
theta = c(log(mu), log(delta))B-spline: no reparametrization is needed. The knots as considered fixed.
Piecewise Poisson:
theta = log(rates). The left endpoints of time intervals, denoted asaccrualTime, are considered fixed.
Event or dropout models
Exponential:
theta = log(rate)Weibull:
theta = c(log(scale), -log(shape))Log-logistic:
theta = c(log(scale), -log(shape))Log-normal:
theta = c(meanlog, log(sdlog))Piecewise exponential:
theta = log(rates). The left endpoints of time intervals, denoted aspiecewiseSurvivalTimefor event model andpiecewiseDropoutTimefor dropout model, are considered fixed.Model averaging:
theta = c(log(weibull$scale), -log(weibull$shape), lnorm$meanlog, log(lnorm$sdlog)). The covariance matrix forthetais structured as a block diagonal matrix, with the upper-left block corresponding to the Weibull component and the lower-right block corresponding to the log-normal component. In other words, the covariance matrix is partitioned into two distinct blocks, with no off-diagonal elements connecting the two components. The weight assigned to the Weibull component, denoted asw1, is considered fixed.Spline:
thetacorresponds to the coefficients of basis vectors. Theknotsandscaleare considered fixed. Thescalecan be hazard, odds, or normal, corresponding to extensions of Weibull, log-logistic, and log-normal distributions, respectively.
The EventPredInCure package uses days as its primary time unit.
If you need to convert enrollment or event rates per month to
rates per day, simply divide by 30.4375.
References
Chen, Tai-Tsang. "Predicting analysis times in randomized clinical trials with cancer immunotherapy." BMC medical research methodology 16.1 (2016): 1-10.
Royston, Patrick, and Mahesh KB Parmar. "Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects." Statistics in medicine 21.15 (2002): 2175-2197.
Bagiella, Emilia, and Daniel F. Heitjan. "Predicting analysis times in randomized clinical trials." Statistics in medicine 20.14 (2001): 2055-2063.
Ying, Gui‐shuang, and Daniel F. Heitjan. "Weibull prediction of event times in clinical trials." Pharmaceutical Statistics: The Journal of Applied Statistics in the Pharmaceutical Industry 7.2 (2008): 107-120.
Zhang, Xiaoxi, and Qi Long. "Stochastic modeling and prediction for accrual in clinical trials." Statistics in Medicine 29.6 (2010): 649-658.
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