fitDropout {EventPredInCure}

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,
  by_treatment = FALSE,
  criterion = "both"
)

Arguments

df

The subject-level dropout data, including time and dropout. The data should also include treatment coded as 1, 2, and so on, and treatment_description for fitting the dropout model by treatment.

dropout_model

The dropout model used to analyze the dropout data which can be set to one of the following options: "exponential", "Weibull", "log-logistic", "log-normal", or "piecewise exponential". By default, it is set to "exponential".

piecewiseDropoutTime

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.

by_treatment

A Boolean variable to control whether or not to fit the time-to-dropout data by treatment group. By default, it is set to FALSE.

criterion

A character variable to denote the criterion in model selection to shown in the figure, which can be set to one of the following options: "aic","bic" or "both". By default,it is set to both.

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 Bayesian Information Criterion, bic, and Akaike Information Criterion, aic.

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

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.

References

• 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.

Examples

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