survival_analysis {bayesCT}  R Documentation 
Function to analyze Bayesian trial for timetoevent data which allows early stopping and incorporation of historical data using the discount function approach
survival_analysis( time, treatment, event = NULL, time0 = NULL, treatment0 = NULL, event0 = NULL, surv_time = NULL, h0 = 0, breaks = NULL, alternative = "greater", N_impute = 10, number_mcmc = 10000, prob_ha = 0.95, futility_prob = 0.1, expected_success_prob = 0.9, prior = c(0.1, 0.1), discount_function = "identity", fix_alpha = FALSE, alpha_max = 1, weibull_scale = 0.135, weibull_shape = 3, method = "fixed" )
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. 
time0 
vector. Historical exposure time for the subjects. It must be the same length as the treatment variable. 
treatment0 
vector. the historical treatment assignment for patients, 1 for treatment group and 0 for control group. 
event0 
vector. Historical 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. 
surv_time 
scalar. scalar. Survival time of interest for computing the probability of survival for a single arm (OPC) trial. Default is overall, i.e., current+historical, median survival time. 
h0 
scalar. Threshold for comparing two mean values. Default is

breaks 
vector. Breaks (interval starts) used to compose the breaks of the piecewise exponential model. Do not include zero. Default breaks are the quantiles of the input times. 
alternative 
character. The string specifying the alternative
hypothesis, must be one of 
N_impute 
scalar. Number of imputations for Monte Carlo simulation of missing data. 
number_mcmc 
scalar. Number of Monte Carlo Markov Chain draws in sampling posterior. 
prob_ha 
scalar. Probability of alternative hypothesis. 
futility_prob 
scalar. Probability of stopping early for futility. 
expected_success_prob 
scalar. Probability of stopping early for success. 
prior 
vector. Prior values of the gamma rate, Gamma(a0, b0). The default is set to Gamma(.1, .1). 
discount_function 
character. If incorporating historical data, specify
the discount function. Currently supports the Weibull function
( 
fix_alpha 
logical. Fix alpha at alpha_max? Default value is FALSE. 
alpha_max 
scalar. Maximum weight the discount function can apply. Default is 1. For a twoarm 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. 
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 twoarm 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 twoarm 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 " 
a list of output for the Bayesian trial for timetoevent.
prob_of_accepting_alternative
scalar. The input parameter of probability of accepting the alternative.
margin
scalar. The margin input value of difference between mean estimate of treatment and mean estimate of the control.
alternative
character. The input parameter of alternative hypothesis.
alpha_max
scalar. The alpha_max input.
N_treatment
scalar. The number of patients enrolled in the experimental group for each simulation.
event_treatment
scalar. The number of events in the experimental group for each simulation.
N_control
scalar. The number of patients enrolled in the control group for each simulation.
event_control
scalar. The number of events in the control group for each simulation.
N_enrolled
scalar. The number of patients enrolled in the trial (sum of control and experimental group for each simulation. )
N_complete
scalar. The number of patients whose time passes the surv_time.
alpha_discount
vector. The alpha discount funtion used for control and treatment.
post_prob_accept_alternative
vector. The final probability of accepting the alternative hypothesis after the analysis is done.
est_final
scalar. The final estimate of the difference in posterior estimate of treatment and posterior estimate of the control group.
stop_futility
scalar. Did the trial stop for futility during imputation of patient who had loss to follow up? 1 for yes and 0 for no.
stop_expected_success
scalar. Did the trial stop for early success during imputation of patient who had loss to follow up? 1 for yes and 0 for no.