eventPred 0.2.0

• allow the use of treatment labels for by-treatment prediction
• include usubjid in subject-level data sets
• use the quantile method for predicted date if all simulated data sets attain the target number of events
• add log-logistic event model and log-logistic dropout model
• change parameterization of Weibull distribution to be consistent with log-logistic and log-normal distributions in the AFT family
• add AIC to enrollment, event and dropout model fits
• check the required number of events/dropouts for event/dropout model fits
• add “model averaging” and “spline” as additional dropout_model options
• update enroll_fit, event_fit, and dropout_fit for prior incorporation

eventPred 0.1.5

• update design stage prediction with one treatment arm
• allow ongoing subjects with last known date before data cutoff
• update the calculation of ongoing subjects to accommodate ongoing subjects with last known date before data cutoff
• update time for new subjects to start with day 1 and update totalTime calculation for newEvents to remove double count of day 1
• update predictEnrollment to remove calculation of d0, c0, and r0
• add names to event_pred_day
• add nyears and nreps to prediction results

eventPred 0.1.4

• add validity checks for input dataset variables
• update totalTime calculation for observed data
• use method=“Nelder-Mead” as the default optimization algorithm for flexsurvspline
• add by-treatment prediction

eventPred 0.1.3

• update the description of internal datasets
• update summarizeObserved to remove adt from adsl
• add Royston and Parmar (2002) spline event model

eventPred 0.1.2

• add mean and variance to prediction output
• update the BIC weight for model averaging
• add more details for model fit parameters
• add day 1 to enrollment plot
• allow prior piecewise Poisson enrollment and piecewise exponential event or dropout models to have additional cut points beyond the observed data range
• update internal data sets

eventPred 0.1.1

• add stage and to_predict information in getPrediction output
• add the cutoff time point to the number of ongoing subjects
• change the default model for dropout to exponential
• require trialsdt in input data set

eventPred 0.1.0

• added the piecewise Poisson model to fitEnrollment and predictEnrollment at the analysis stage
• added number of dropouts
• added number of subjects at risk
• added a data set when the enrollment has completed
• corrected the x-axis title for predictEnrollment and predictEvent
• updated alogrithm to allow one piece piecewise Poisson enrollment model and one piece piecewise exponential time-to-event model
• modified the weight calculation for model averaging to avoid underflow
• used weighted BIC for model averaging
• removed the dropout_model parameter for summarizeObserved
• changed the default number of knots of the b-spline enrollment model to zero
• replaced first and last with slice of dplyr in summarizeObserved
• improved the initial value for the time-decay enrollment model parameters
• added showplot to fitEnrollment, fitEvent and fitDropout
• sped up the calculations of quantiles
• added target_n to predictEnrollment output and target_d to predictEvent output
• removed the cutoff date from ongoing_pred_df before data cutoff
• restricted enrollment model fitting to the last randomization date
• added piecewise exponential dropout model
• use delta method to obtain the variance of model parameters for pooled population
• replace randomization probabilities with treatment allocation within a randomization block
• allow number of subjects to differ among simulated data sets
• remove custom date axis

eventPred 0.0.1

• Initial release