Title:	Event Prediction
Version:	0.2.9
Date:	2025-06-10
Description:	Predicts enrollment and events at the design or analysis stage using specified enrollment and time-to-event models through simulations.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	data.table (≥ 1.14.10), magrittr (≥ 2.0.0), ggplot2 (≥ 3.5.1), plotly (≥ 4.10.1), survival (≥ 2.41-3), splines (≥ 3.5.0), Matrix (≥ 1.2-14), mvtnorm (≥ 1.1-3), rstpm2 (≥ 1.6.1), numDeriv (≥ 2016.8-1.1), purrr (≥ 1.0.2), flexsurv (≥ 2.2.2), erify (≥ 0.4.0), stats (≥ 3.5.0), shiny (≥ 1.7.1), rlang (≥ 1.0.6), lrstat (≥ 0.2.12)
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Depends:	R (≥ 3.5.0)
VignetteBuilder:	knitr
LazyData:	true
URL:	https://github.com/kaifenglu/eventPred
BugReports:	https://github.com/kaifenglu/eventPred/issues
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-06-10 14:41:28 UTC; kaife
Author:	Kaifeng Lu [aut, cre]
Maintainer:	Kaifeng Lu <kaifenglu@gmail.com>
Repository:	CRAN
Date/Publication:	2025-06-10 16:00:02 UTC

Event Prediction

Description

Predicts enrollment and events at the design stage using assumed enrollment and treatment-specific time-to-event models, or at the analysis stage using blinded or unblinded data and specified enrollment and time-to-event models through simulations.

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. The eventPred package provides enrollment and event prediction capabilities using assumed enrollment and treatment-specific time-to-event models at the design stage, using blinded or unblinded data and specified enrollment and time-to-event models at the analysis stage.

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 eventPred 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 eventPred package also offers several time-to-event models, including exponential, Weibull, log-logistic, log-normal, piecewise exponential, model averaging of Weibull and log-normal, spline, and cox. For time to dropout, the same set of model 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 eventPred 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 eventPred 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 eventPred 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(\lambda).

  • Time-decay: \theta = (\log(\mu), \log(\delta))'.

  • B-spline: \log(\lambda(t)) = B(t)' \theta, B(t) = (B_1(t), \ldots, B_{k+4}(t))' are the B-spline basis with k inner knots.

  • Piecewise Poisson: \theta_j = \log(\lambda_j) for the jth time interval. The left endpoints of time intervals, denoted as accrualTime, are considered fixed.

• Event or dropout models

  Let x denote the covariates for a subject. Let \beta denote the regression coefficients and \sigma denote the scale parameter of the AFT model,

  \log(T) = \beta' x + \sigma \epsilon.

  • Exponential: \log(\lambda) = \theta' x. In other words, \theta = -\beta.

  • Weibull: \log(\texttt{weibull scale}) = \theta_1' x, \log(\texttt{weibull shape}) = -\theta_2. In other words, \theta = (\beta', \log(\sigma))'.

  • Log-logistic: For the logistic distribution of \log(T), \texttt{location} = \theta_1' x, \log(\texttt{scale}) = \theta_2. In other words, \theta = (\beta', \log(\sigma))'.

  • Log-normal: For the normal distribution of \log(T), \texttt{mean} = \theta_1' x, \log(\texttt{sd}) = \theta_2. In other words, \theta = (\beta', \log(\sigma))'.

  • Piecewise exponential: \log(\lambda_j) = \theta_{1j} + \theta_2' x for the jth time interval, \theta = (\theta_1', \theta_2')'. The left endpoints of time intervals, denoted as piecewiseSurvivalTime for event model and piecewiseDropoutTime for dropout model, are considered fixed.

  • Model averaging: \theta = (\theta_{\texttt{weibull}}', \theta_{\texttt{lnorm}}')'. 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 w_1, is considered fixed.

  • Spline: Let S(t|x) denote the survival function given covariates x. We model a transformation of the survival function as a cubic spine:

    g(S(t|x)) = c(u) + \theta_2' x,

    where

    c(u) = \gamma_1 + \gamma_2 u + \gamma_3 v_1(u) + \cdots + \gamma_{k+2} v_k(u)

    is the cubic spline in u=\log(t), \theta = (\theta_1', \theta_2')', \theta_1 = (\gamma_1, \ldots, \gamma_{k+2})', assuming k inner knots (k = \texttt{knots}), and v_1(u), \ldots, v_k(u) are the basis of the Royston/Parmar spline. The transformation is given as follows:

    • For scale = "hazard", g(S(t)) = \log(-\log(S(t))).

    • For scale = "odds", g(S(t)) = \log(1/S(t)-1).

    • For scale = "normal", g(S(t)) = -\Phi^{-1}(S(t)).

    The hazard, odds, and normal scales correspond to extensions of the Weibull, log-logistic, and log-normal distributions, respectively.

  • Cox: Let t_1 < \cdots < t_M denote the distinct observed event times, \lambda_j denote the estimated baseline hazard rate in the jth time interval, (t_{j-1}, t_j], and \beta denote the regression coefficients (log hazard ratios) from the Cox model. The model parameters including the baseline hazards are \theta = (\log(\lambda_1),\ldots,\log(\lambda_M), \beta^T)^T.

The eventPred 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.

Author(s)

Kaifeng Lu, kaifenglu@gmail.com

References

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.

Xiaoxi Zhang and Qi Long. Stochastic modeling and prediction for accrual in clinical trials. Stat in Med. 2010; 29:649-658.

Patrick Royston and Mahesh K. B. Parmar. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat in Med. 2002; 21:2175-2197.

Final enrollment and event data after achieving the target number of events

Description

A data frame with 300 rows and 7 columns:

trialsdt

The trial start date

usubjid

The unique subject ID

randdt

The randomization date

treatment

The treatment group number

treatment_description

Description of the treatment group

time

The day of event or censoring since randomization

event

The event indicator: 1 for event, 0 for non-event

dropout

The dropout indicator: 1 for dropout, 0 for non-dropout

cutoffdt

The cutoff date

For ongoing subjects, both event and dropout are equal to 0.

Usage

finalData

Format

An object of class tbl_df (inherits from tbl, data.frame) with 300 rows and 9 columns.

Fit time-to-dropout model

Description

Fits a specified time-to-dropout model to the dropout data.

Usage

fitDropout(
  df,
  dropout_model = "exponential",
  piecewiseDropoutTime = 0,
  k_dropout = 0,
  scale_dropout = "hazard",
  m_dropout = 5,
  showplot = TRUE,
  by_treatment = FALSE,
  covariates = NULL,
  generate_plot = TRUE,
  interactive_plot = TRUE
)

Arguments

Value

A list of results from the model fit including key information such as the dropout model, model, the estimated model parameters, theta, the covariance matrix, vtheta, as well as the Akaike Information Criterion, aic, and Bayesian Information Criterion, bic.

If the piecewise exponential model is used, the location of knots used in the model, piecewiseDropoutTime, will be included in the list of results.

If the model averaging option is chosen, the weight assigned to the Weibull component is indicated by the w1 variable.

If the spline option is chosen, the knots and scale will be included in the list of results.

If the cox option is chosen, the list of results will include model, theta, vtheta, aic, bic, and piecewiseDropoutTime. Here

\theta = (\log(\lambda_1), \ldots, \log(\lambda_M), \beta^T)^T,

M denotes the number of distinct observed dropout times, t_1 < \cdots < t_M, \lambda_j denotes the estimated baseline hazard rate in the jth dropout time interval, (t_{j-1}, t_j], and \beta represents the regression coefficients (log hazard ratios) from the Cox model. For a fair comparison, the estimation of baseline hazards is incorporated into the aic and bic values. In addition, \mbox{piecewiseDropoutTime} = (0, t_1, \ldots, t_M). To extend the survival curve beyond the last observed dropout time, a weighted average of the hazard rates from the final m_dropout dropout time intervals is used. The weights are proportional to the lengths of those intervals, i.e.,

\lambda_{M+1} = \sum_{j=M-m_{\rm{dropout}}+1}^{M} w_j \lambda_j,

where w_j = (t_j - t_{j-1})/(t_M - t_{M-m_{\rm{dropout}}}) for j=M-m_{\rm{dropout}}+1,\ldots,M.

When fitting the dropout model by treatment, the outcome is presented as a list of lists, where each list element corresponds to a specific treatment group.

The fitted time-to-dropout survival curve is also returned.

Author(s)

Kaifeng Lu, kaifenglu@gmail.com

References

Patrick Royston and Mahesh K. B. Parmar. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat in Med. 2002; 21:2175-2197.

Examples

dropout_fit <- fitDropout(
  df = interimData2,
  dropout_model = "exponential")

Fit enrollment model

Description

Fits a specified enrollment model to the enrollment data.

Usage

fitEnrollment(
  df,
  enroll_model = "b-spline",
  nknots = 0,
  accrualTime = 0,
  showplot = TRUE,
  generate_plot = TRUE,
  interactive_plot = TRUE
)

Arguments

Details

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 \lambda(t) = \exp(B(t)' \theta), where B(t) represents the B-spline basis functions.

Value

A list of results from the model fit including key information such as the enrollment model, model, the estimated model parameters, theta, the covariance matrix, vtheta, the Akaike Information Criterion, aic, and the Bayesian Information Criterion, bic, as well as the design matrix x for the B-spline enrollment model, and accrualTime for the piecewise Poisson enrollment model.

The fitted enrollment curve is also returned.

Author(s)

Kaifeng Lu, kaifenglu@gmail.com

References

Xiaoxi Zhang and Qi Long. Stochastic modeling and prediction for accrual in clinical trials. Stat in Med. 2010; 29:649-658.

Examples

enroll_fit <- fitEnrollment(
  df = interimData1, enroll_model = "b-spline",
  nknots = 1)

Fit time-to-event model

Description

Fits a specified time-to-event model to the event data.

Usage

fitEvent(
  df,
  event_model = "model averaging",
  piecewiseSurvivalTime = 0,
  k = 0,
  scale = "hazard",
  m = 5,
  showplot = TRUE,
  by_treatment = FALSE,
  covariates = NULL,
  generate_plot = TRUE,
  interactive_plot = TRUE
)

Arguments

Value

A list of results from the model fit including key information such as the event model, model, the estimated model parameters, theta, the covariance matrix, vtheta, as well as the Akaike Information Criterion, aic, and Bayesian Information Criterion, bic.

If the piecewise exponential model is used, the location of knots used in the model, piecewiseSurvivalTime, will be included in the list of results.

If the model averaging option is chosen, the weight assigned to the Weibull component is indicated by the w1 variable.

If the spline option is chosen, the knots and scale will be included in the list of results.

If the cox option is chosen, the list of results will include model, theta, vtheta, aic, bic, and piecewiseSurvivalTime. Here

\theta = (\log(\lambda_1), \ldots, \log(\lambda_M), \beta^T)^T,

M denotes the number of distinct observed event times, t_1 < \cdots < t_M, \lambda_j denotes the estimated baseline hazard rate in the jth event time interval, (t_{j-1}, t_j], and \beta represents the regression coefficients (log hazard ratios) from the Cox model. For a fair comparison, the estimation of baseline hazards is incorporated into the aic and bic values. In addition, \mbox{piecewiseSurvivalTime} = (0, t_1, \ldots, t_M). To extend the survival curve beyond the last observed event time, a weighted average of the hazard rates from the final m event time intervals is used. The weights are proportional to the lengths of those intervals, i.e.,

\lambda_{M+1} = \sum_{j=M-m+1}^{M} w_j \lambda_j,

where w_j = (t_j - t_{j-1})/(t_M - t_{M-m}) for j=M-m+1,\ldots,M.

When fitting the event model by treatment, the outcome is presented as a list of lists, where each list element corresponds to a specific treatment group.

The fitted time-to-event survival curve is also returned.

Author(s)

Kaifeng Lu, kaifenglu@gmail.com

References

Patrick Royston and Mahesh K. B. Parmar. Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Stat in Med. 2002; 21:2175-2197.

Examples

event_fit <- fitEvent(
  df = interimData2,
  event_model = "piecewise exponential",
  piecewiseSurvivalTime = c(0, 180))

Enrollment and event prediction

Description

Performs enrollment and event prediction by utilizing observed data and specified enrollment and event models.

Usage

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,
  generate_plot = TRUE,
  interactive_plot = TRUE
)

Arguments

Details

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 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.

Value

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.

Author(s)

Kaifeng Lu, kaifenglu@gmail.com

Examples

# 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)

Interim enrollment and event data before enrollment completion

Description

A data frame with 225 rows and 9 columns:

trialsdt

The trial start date

usubjid

The unique subject ID

randdt

The randomization date

treatment

The treatment group number

treatment_description

Description of the treatment group

time

The day of event or censoring since randomization

event

The event indicator: 1 for event, 0 for non-event

dropout

The dropout indicator: 1 for dropout, 0 for non-dropout

cutoffdt

The cutoff date

For ongoing subjects, both event and dropout are equal to 0.

Usage

interimData1

Format

An object of class tbl_df (inherits from tbl, data.frame) with 224 rows and 9 columns.

Interim enrollment and event data after enrollment completion

Description

A data frame with 300 rows and 7 columns:

trialsdt

The trial start date

usubjid

The unique subject ID

randdt

The randomization date

treatment

The treatment group number

treatment_description

Description of the treatment group

time

The day of event or censoring since randomization

event

The event indicator: 1 for event, 0 for non-event

dropout

The dropout indicator: 1 for dropout, 0 for non-dropout

cutoffdt

The cutoff date

For ongoing subjects, both event and dropout are equal to 0.

Usage

interimData2

Format

An object of class tbl_df (inherits from tbl, data.frame) with 300 rows and 9 columns.

Profile log likelihood for piecewise exponential regression

Description

Obtains the profile log likelihood value for piecewise exponential regression.

Usage

pllik_pwexp(beta, time, event, J, tcut, x)

Arguments

Value

The profile log likelihood value for piecewise exponential regression.

Distribution function for model averaging of Weibull and log-normal

Description

Obtains the distribution function value for model-averaging of Weibull and log-normal regression.

Usage

pmodavg(t, theta, w1, q = 0, x = 1, lower.tail = TRUE, log.p = FALSE)

Arguments

Value

The probabilities p = P(T <= t | X = x).

Distribution function for piecewise exponential regression

Description

Obtains the distribution function value for piecewise exponential regression.

Usage

ppwexp(t, theta, J, tcut, q = 0, x = 1, lower.tail = TRUE, log.p = FALSE)

Arguments

Value

The probabilities p = P(T <= t | X = x).

Predict enrollment

Description

Utilizes a pre-fitted enrollment model to generate enrollment times for new subjects and provide a prediction interval for the expected time to reach the enrollment target.

Usage

predictEnrollment(
  df = NULL,
  target_n = NA,
  enroll_fit = NULL,
  lags = 30,
  pilevel = 0.9,
  nyears = 4,
  nreps = 500,
  showsummary = TRUE,
  showplot = TRUE,
  by_treatment = FALSE,
  ngroups = 1,
  alloc = NULL,
  treatment_label = NULL,
  fix_parameter = FALSE,
  generate_plot = TRUE,
  interactive_plot = TRUE
)

Arguments

Details

The enroll_fit variable can be used for enrollment prediction at the design stage. A piecewise Poisson model can be parameterized through the time intervals, accrualTime, which is treated as fixed, and the enrollment rates in the intervals, accrualIntensity, the log of which is used as the model parameter. For the homogeneous Poisson, time-decay, and piecewise Poisson models, enroll_fit is used to specify the prior distribution of model parameters, with a very small variance being used to fix the parameter values. It should be noted that the B-spline model is not appropriate for use during the design stage.

During the enrollment stage, enroll_fit is the enrollment model fit based on the observed data. The fitted enrollment model is used to generate enrollment times for new subjects.

Value

A list of prediction results, which includes important information such as the median, lower and upper percentiles for the estimated time to reach the target number of subjects, as well as simulated enrollment data for new subjects. The data for the prediction plot is also included within the list.

Author(s)

Kaifeng Lu, kaifenglu@gmail.com

References

Xiaoxi Zhang and Qi Long. Stochastic modeling and prediction for accrual in clinical trials. Stat in Med. 2010; 29:649-658.

Examples

# Enrollment prediction at the design stage
set.seed(1000)

enroll_pred <- predictEnrollment(
  target_n = 300,
  enroll_fit = list(
    model = "piecewise poisson",
    theta = log(26/9*seq(1, 9)/30.4375),
    vtheta = diag(9)*1e-8,
    accrualTime = seq(0, 8)*30.4375),
  pilevel = 0.90, nreps = 100)

Predict event

Description

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.

Usage

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,
  generate_plot = TRUE,
  interactive_plot = TRUE
)

Arguments

Details

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.

Value

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.

Author(s)

Kaifeng Lu, kaifenglu@gmail.com

References

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.

Examples

# 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)

Piecewise exponential regression

Description

Obtains the maximum likelihood estimates for piecewise exponential regression.

Usage

pwexpreg(time, event, J, tcut, q = 0, x = 1)

Arguments

Value

The maximum likelihood estimates and the associated covariance matrix, AIC and BIC.

Quantile function for piecewise exponential regression

Description

Obtains the quantile function value for piecewise exponential regression.

Usage

qpwexp(p, theta, J, tcut, q = 0, x = 1, lower.tail = TRUE, log.p = FALSE)

Arguments

Value

The quantiles t such that P(T <= t | X = x) = p.

Run Shiny app

Description

Runs the event prediction Shiny app.

Usage

runShinyApp_eventPred()

Author(s)

Kaifeng Lu, kaifenglu@gmail.com

Summarize observed data

Description

Provides an overview of the observed data, including the trial start date, data cutoff date, enrollment duration, number of subjects enrolled, number of events and dropouts, number of subjects at risk, cumulative enrollment and event data, daily enrollment rates, and Kaplan-Meier plots for time to event and time to dropout.

Usage

summarizeObserved(
  df,
  to_predict = "event only",
  showplot = TRUE,
  by_treatment = FALSE,
  generate_plot = TRUE,
  interactive_plot = TRUE
)

Arguments

Value

A list that includes a range of summary statistics, data sets, and plots depending on the value of to_predict.

Author(s)

Kaifeng Lu, kaifenglu@gmail.com

Examples

observed1 <- summarizeObserved(
  df = interimData1,
  to_predict = "enrollment and event")

observed2 <- summarizeObserved(
  df = interimData2,
  to_predict = "event only")