historical_survival: Historical data for survival analysis

In bayesCT: Simulation and Analysis of Adaptive Bayesian Clinical Trials

Description

Wrapper function for historical data from time-to-event outcome.

Usage

historical_survival(
  time = NULL,
  treatment = NULL,
  event = NULL,
  discount_function = "identity",
  alpha_max = 1,
  fix_alpha = FALSE,
  weibull_scale = 0.135,
  weibull_shape = 3,
  method = "fixed",
  .data = NULL
)

Arguments

time	vector. exposure time for the subjects. It must be the same length as the treatment variable.
treatment	vector. treatment assignment for patients, 1 for treatment group and 0 for control group
event	vector. The status indicator, normally 0=alive, 1=dead. Other choices are TRUE/FALSE (TRUE = death) or 1/2 (2=death). For censored data, the status indicator is 0=right censored, 1 = event at time. Although unusual, the event indicator can be omitted, in which case all subjects are assumed to have an event.
discount_function	character. If incorporating historical data, specify the discount function. Currently supports the Weibull function (discount_function="weibull"), the scaled-Weibull function (discount_function="scaledweibull"), and the identity function (discount_function="identity"). The scaled-Weibull discount function scales the output of the Weibull CDF to have a max value of 1. The identity discount function uses the posterior probability directly as the discount weight. Default value is "identity". See bdpnormal for more details.
alpha_max	scalar. Maximum weight the discount function can apply. Default is 1. For a two-arm trial, users may specify a vector of two values where the first value is used to weight the historical treatment group and the second value is used to weight the historical control group.
fix_alpha	logical. Fix alpha at alpha_max? Default value is FALSE.
weibull_scale	scalar. Scale parameter of the Weibull discount function used to compute alpha, the weight parameter of the historical data. Default value is 0.135. For a two-arm trial, users may specify a vector of two values where the first value is used to estimate the weight of the historical treatment group and the second value is used to estimate the weight of the historical control group. Not used when discount_function = "identity".
weibull_shape	scalar. Shape parameter of the Weibull discount function used to compute alpha, the weight parameter of the historical data. Default value is 3. For a two-arm trial, users may specify a vector of two values where the first value is used to estimate the weight of the historical treatment group and the second value is used to estimate the weight of the historical control group. Not used when discount_function = "identity".
method	character. Analysis method with respect to estimation of the weight paramter alpha. Default method "mc" estimates alpha for each Monte Carlo iteration. Alternate value "fixed" estimates alpha once and holds it fixed throughout the analysis. See the the bdpsurvival vignette vignette("bdpsurvival-vignette", package="bayesDP") for more details.
.data	NULL. stores the historical time, treatment and event , please do not fill it in.

Value

a list with historical data for time-to-event outcome with the discount function.

Examples

historical_survival(time      = rexp(10, 0.01),
                    treatment = rep(10, 1),
                    event     = rep(10, 1))

bayesCT documentation built on July 2, 2020, 2:34 a.m.