R: Fit time-to-event model

fitEvent {EventPredInCure}

R Documentation

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",
  by_treatment = FALSE,
  criterion = "both"
)

Arguments

`df`	The subject-level event data, including `time` and `event`. The data should also include `treatment` coded as 1, 2, and so on, and `treatment_description` for fitting the event model by treatment.
`event_model`	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","exponential with cured population","weibull with cured population", "log-normal with cured population","log-logistic with cured population" or "piecewise exponential with cured population". The model averaging uses the `exp(-bic/2)` weighting and combines Weibull and log-normal models. The spline model of Royston and Parmar (2002) assumes that a transformation of the survival function is modeled as a natural cubic spline function of log time. By default, it is set to "model averaging".
`piecewiseSurvivalTime`	A vector that specifies the time intervals for the piecewise exponential survival distribution or piecewise exponential with cured population. 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.
`k`	The number of inner knots of the spline. 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.
`scale`	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.
`by_treatment`	A Boolean variable to control whether or not to fit the time-to-event 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 event 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, 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.

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.

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.
Chen, Tai-Tsang. "Predicting analysis times in randomized clinical trials with cancer immunotherapy." BMC medical research methodology 16.1 (2016): 1-10.

Examples


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

[Package EventPredInCure version 1.0 Index]