Title: | Group Sequential Design for Historical Control Trial with Survival Outcome |
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Description: | It provides functions to design historical controlled trials with survival outcome by group sequential method. The options for interim look boundaries are efficacy only, efficacy & futility or futility only. It also provides the function to monitor the trial for any unplanned look. The package is based on Jianrong Wu, Xiaoping Xiong (2016) <doi:10.1002/pst.1756> and Jianrong Wu, Yimei Li (2020) <doi:10.1080/10543406.2019.1684305>. |
Authors: | Tushar Patni [aut, cre], Yimei Li [aut], Jianrong Wu [aut], Arzu Onar-Thomas [aut] |
Maintainer: | Tushar Patni <[email protected]> |
License: | GPL-3 |
Version: | 0.7.2 |
Built: | 2025-02-12 04:26:36 UTC |
Source: | https://github.com/cran/HCTDesign |
The group sequential design for historical controlled survival outcome trials with efficacy boundaries only.
EffDesign( k, alpha, beta, delta, delta0, d1, option = "OBF", param = 4, trial = "Superiority" )
EffDesign( k, alpha, beta, delta, delta0, d1, option = "OBF", param = 4, trial = "Superiority" )
k |
vector of time fraction for all planned looks: k=c(1/3,2/3,1) if the three planned looks will be carried out at 1/3, 2/3 and all of the total events in the experiment arm. |
alpha |
type I error. |
beta |
type II error. |
delta |
hazard ratio: hazard of experiment group over hazard of control group. |
delta0 |
Non-inferiority margin. |
d1 |
total number of events in the historical control group. |
option |
type of spending function: "OBF", "Gamma", "Rho" or "Pocock". Default is "OBF. |
param |
Parameter for Gamma family or Rho family. Default value is 4. |
trial |
Type of trial: "Superiority" or "Non-inferiority". Default is "Superiority". |
List of dataframes and vectors containing the details about the following: design of the trial which includes the number of looks and events; details about futility and efficacy boundaries which include transformed information time at each look, cumulative beta and alpha respectively, p-values and crossing probabilities; etam(drift parameter); d2max(maximum number of events in the experimental group); delta_used(hazard ratio used in the design).
Tushar Patni, Yimei Li, Jianrong Wu, and Arzu Onar-Thomas.
Wu J, Xiong X (2016). “Survival trial design and monitoring using historical controls.” Pharmaceutical Statistics, 15(5), 405-411.
Wu J, Li Y (2020). “Group sequential design for historical control trials using error spending functions.” Journal of Biopharmaceutical Statistics, 30(2), 351-363.
#Superiority trial with three equally spaced looks for efficacy using OBF spending function. gg<-EffDesign(k=c(0.3,0.6,1),alpha=0.05,beta=0.1,delta=0.57,d1=65,option="OBF",trial="Superiority")
#Superiority trial with three equally spaced looks for efficacy using OBF spending function. gg<-EffDesign(k=c(0.3,0.6,1),alpha=0.05,beta=0.1,delta=0.57,d1=65,option="OBF",trial="Superiority")
Calculates one-sided efficacy boundary values at the observed number of events.
EffIM( d2, dmax, last.look = FALSE, d1, etam, alpha, beta, opt = "OBF", param = 4 )
EffIM( d2, dmax, last.look = FALSE, d1, etam, alpha, beta, opt = "OBF", param = 4 )
d2 |
vector of number of events at which you want to monitor the trial. |
dmax |
maximum number of events in the experimental group calculated from design function. |
last.look |
logical which indicates whether the current look is the last look or not. Default is FALSE. If true, the post hoc power is calculated. |
d1 |
total number of events in the historical control group. |
etam |
value of the drift parameter obtained from design function. |
alpha |
type I error. |
beta |
type II error. |
opt |
type of spending function: "OBF", "Gamma", "Rho" or "Pocock". Default is "OBF". |
param |
Parameter for "gamma family" or rho family. Default value is 4. |
The number of events have to be entered sequentially. See example.
A list containing efficacy boundary values along with the p-values and transformed information time for the current look. Post-hoc power is also calculated in case of early stopping of the trial.
Tushar Patni, Yimei Li, Jianrong Wu, and Arzu Onar-Thomas.
Wu J, Xiong X (2016). “Survival trial design and monitoring using historical controls.” Pharmaceutical Statistics, 15(5), 405-411.
Wu J, Li Y (2020). “Group sequential design for historical control trials using error spending functions.” Journal of Biopharmaceutical Statistics, 30(2), 351-363.
#Interim look for the trial when the number of events is 13(first look). gg<-EffIM(c(13),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE) #Interim look for the trial when the number of events is 35(second look). gg<-EffIM(c(13,35),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE)
#Interim look for the trial when the number of events is 13(first look). gg<-EffIM(c(13),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE) #Interim look for the trial when the number of events is 35(second look). gg<-EffIM(c(13,35),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE)
The group sequential design for historical controlled survival outcome trials with futility boundaries only.
FutDesign( k, alpha, beta, delta, d1, option = "OBF", param = 4, trial = "Superiority", delta0 )
FutDesign( k, alpha, beta, delta, d1, option = "OBF", param = 4, trial = "Superiority", delta0 )
k |
vector of time fraction for all planned looks: k=c(1/3,2/3,1) if the three planned looks will be carried out at 1/3, 2/3 and all of the total events in the experiment arm. |
alpha |
type I error. |
beta |
type II error. |
delta |
hazard ratio: hazard of experiment group over hazard of control group. |
d1 |
total number of events in the historical control group. |
option |
type of spending function: "OBF", "Gamma", "Rho" or "Pocock". Default is "OBF. |
param |
Parameter for Gamma family or Rho family. Default value is 4. |
trial |
Type of trial: "Superiority" or "Non-inferiority". Default is "Superiority". |
delta0 |
Non-inferiority margin. |
List of dataframes and vectors containing the details about the following: design of the trial which includes the number of looks and events; details about futility and efficacy boundaries which include transformed information time at each look, cumulative beta and alpha respectively, p-values and crossing probabilities; etam(drift parameter); d2max(maximum number of events in the experimental group); delta_used(hazard ratio used in the design).
Tushar Patni, Yimei Li, Jianrong Wu, and Arzu Onar-Thomas.
Wu J, Xiong X (2016). “Survival trial design and monitoring using historical controls.” Pharmaceutical Statistics, 15(5), 405-411.
Wu J, Li Y (2020). “Group sequential design for historical control trials using error spending functions.” Journal of Biopharmaceutical Statistics, 30(2), 351-363.
#Sequential superiority trial for three equally spaced looks for OBF spending function. gg<-FutDesign(k=c(0.3,0.6,1),alpha=0.05,beta=0.1,delta=0.57,d1=65,option="OBF",trial="Superiority")
#Sequential superiority trial for three equally spaced looks for OBF spending function. gg<-FutDesign(k=c(0.3,0.6,1),alpha=0.05,beta=0.1,delta=0.57,d1=65,option="OBF",trial="Superiority")
Calculates one-sided futility boundary values at the observed number of events.
FutIM( d2, dmax, last.look = FALSE, d1, etam, alpha, beta, opt = "OBF", param = 4 )
FutIM( d2, dmax, last.look = FALSE, d1, etam, alpha, beta, opt = "OBF", param = 4 )
d2 |
vector of number of events at which you want to monitor the trial. |
dmax |
maximum number of events in the experimental group caculated from design function. |
last.look |
logical which indicates whether the current look is the last look or not. Default is FALSE. |
d1 |
total number of events in the historical control group. |
etam |
value of the drift parameter obtained from design function. |
alpha |
type I error. |
beta |
type II error. |
opt |
type of spending function: "OBF", "Gamma", "Rho" or "Pocock". Default is "OBF". |
param |
Parameter for Gamma family or Rho family. Default value is 4. |
The number of events have to be entered sequentially. See example.
A list containing futility boundary values along with the p-values and transformed information time for the current look.Post-hoc power is also calculated in case of early stopping of the trial.
Tushar Patni, Yimei Li, Jianrong Wu, and Arzu Onar-Thomas.
Wu J, Xiong X (2016). “Survival trial design and monitoring using historical controls.” Pharmaceutical Statistics, 15(5), 405-411.
Wu J, Li Y (2020). “Group sequential design for historical control trials using error spending functions.” Journal of Biopharmaceutical Statistics, 30(2), 351-363.
#Interim look for the trial when the number of events is 13(first look). gg<-FutIM(c(13),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE) #Interim look for the trial when the number of events is 35(second look). gg<-FutIM(c(13,35),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE)
#Interim look for the trial when the number of events is 13(first look). gg<-FutIM(c(13),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE) #Interim look for the trial when the number of events is 35(second look). gg<-FutIM(c(13,35),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE)
The group sequential design for historical controlled survival outcome trials with both efficacy and futility boundaries.
HCTSurvDesign( k, alpha, beta, delta, d1, option = "OBF", param = 4, trial = "Superiority", delta0 )
HCTSurvDesign( k, alpha, beta, delta, d1, option = "OBF", param = 4, trial = "Superiority", delta0 )
k |
vector of time fraction for all planned looks: k=c(1/3,2/3,1) if the three planned looks will be carried out at 1/3, 2/3 and all of the total events in the experiment arm. |
alpha |
type I error. |
beta |
type II error. |
delta |
hazard ratio: hazard of experiment group over hazard of control group. |
d1 |
total number of events in the historical control group. |
option |
type of spending function: "OBF", "Gamma", "Rho" or "Pocock". Default is "OBF". |
param |
Parameter for Gamma family or Rho family. Default value is 4. |
trial |
Type of trial: "Superiority" or "Non-inferiority". Default is "Superiority". |
delta0 |
Non-inferiority margin. |
List of dataframes and vectors containing the details about the following: design of the trial which includes the number of looks and events; details about futility and efficacy boundaries which include transformed information time at each look, cumulative beta and alpha respectively, p-values and crossing probabilities; etam(drift parameter); d2max(maximum number of events in the experimental group); delta_used(hazard ratio used in the design).
Tushar Patni, Yimei Li, Jianrong Wu, and Arzu Onar-Thomas.
Wu J, Xiong X (2016). “Survival trial design and monitoring using historical controls.” Pharmaceutical Statistics, 15(5), 405-411.
Wu J, Li Y (2020). “Group sequential design for historical control trials using error spending functions.” Journal of Biopharmaceutical Statistics, 30(2), 351-363.
#Sequential superiority trial for three equally spaced looks for OBF spending function. gg<-HCTSurvDesign(k=c(0.3,0.6,1),alpha=0.05,beta=0.1,delta=0.57,d1=65,option="OBF")
#Sequential superiority trial for three equally spaced looks for OBF spending function. gg<-HCTSurvDesign(k=c(0.3,0.6,1),alpha=0.05,beta=0.1,delta=0.57,d1=65,option="OBF")
Calculates one-sided boundary values at the observed number of events.
IM(d2, dmax, last.look = FALSE, d1, etam, alpha, beta, opt = "OBF", param = 4)
IM(d2, dmax, last.look = FALSE, d1, etam, alpha, beta, opt = "OBF", param = 4)
d2 |
vector of number of events at which you want to monitor the trial. |
dmax |
maximum number of events in the experimental group calculated from design function. |
last.look |
logical which indicates whether the current look is the last look or not. Default is FALSE. |
d1 |
total number of events in the historical control group. |
etam |
value of the drift parameter obtained from design function. |
alpha |
type I error. |
beta |
type II error. |
opt |
type of spending function: "OBF", "Gamma", "Rho" or "Pocock". Default is "OBF". |
param |
Parameter for Gamma family or Rho family. Default value is 4. |
The number of events have to be entered sequentially. See example.
A list containing efficacy and futility boundary values along with the p-values and transformed information time for the current look. Post-hoc power is also calculated in case of early stopping of the trial.
Tushar Patni, Yimei Li, Jianrong Wu, and Arzu Onar-Thomas.
Wu J, Xiong X (2016). “Survival trial design and monitoring using historical controls.” Pharmaceutical Statistics, 15(5), 405-411.
Wu J, Li Y (2020). “Group sequential design for historical control trials using error spending functions.” Journal of Biopharmaceutical Statistics, 30(2), 351-363.
#Interim look for the trial when the number of events is 13(first look). gg<-IM(c(13),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE) #Interim look for the trial when the number of events is 35(second look). gg<-IM(c(13,35),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE)
#Interim look for the trial when the number of events is 13(first look). gg<-IM(c(13),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE) #Interim look for the trial when the number of events is 35(second look). gg<-IM(c(13,35),dmax=57,alpha=0.05,beta=0.1,etam=3.0726,d1=65,opt="OBF",last.look=FALSE)
Calculates the score function of the log rank test for non-inferiority trial
sf(event, status, delta0, group, experiment, control)
sf(event, status, delta0, group, experiment, control)
event |
event time vector from person level trial data. |
status |
numeric vector indicating the status of event from person level trial data. |
delta0 |
Non-inferiority margin. |
group |
group string vector indicating the assignment of patients into control or experimental group. |
experiment |
name of experimental group as character string. |
control |
name of control group as character string. |
Returns the value of score statistic.
Tushar Patni, Yimei Li, Jianrong Wu, and Arzu Onar-Thomas.
time<-c(20,65,12,50,58,65,45,44) event<-c(1,0,0,0,1,1,1,1) group<-c(rep("exp",4),rep("cont",4)) gg<-sf(event=time,status=event,delta0=1.3,group=group,experiment="exp",control="cont")
time<-c(20,65,12,50,58,65,45,44) event<-c(1,0,0,0,1,1,1,1) group<-c(rep("exp",4),rep("cont",4)) gg<-sf(event=time,status=event,delta0=1.3,group=group,experiment="exp",control="cont")
Calculates the total number of subjects for the experimental group using the total number of events(d2max:the output from design functions) and the estimated failure probability based on the person level historical control data and proportional hazard assumption.
SM(time, event, d2max, opt = "KM", event_ind, ta, tf, delta)
SM(time, event, d2max, opt = "KM", event_ind, ta, tf, delta)
time |
event time vector from person level historical control data. |
event |
numeric vector indicating the status of event from person level historical control data. |
d2max |
maximum number of events in the experimental group calculated from the design function. |
opt |
the method of fitting survival curve-"log_normal" or "KM" (log-normal or Kaplan Meier). Default is "KM". |
event_ind |
numeric value indicating the occurrence of event. |
ta |
enrollment time. |
tf |
follow-up time. |
delta |
hazard ratio. |
Returns the value of sample size.
Tushar Patni, Yimei Li, Jianrong Wu, and Arzu Onar-Thomas.
Wu J, Xiong X (2016). “Survival trial design and monitoring using historical controls.” Pharmaceutical Statistics, 15(5), 405-411.
Wu J, Li Y (2020). “Group sequential design for historical control trials using error spending functions.” Journal of Biopharmaceutical Statistics, 30(2), 351-363.
time<-c(20,65,12,50,58,65,45,44) event<-c(1,0,0,0,1,1,1,1) d2max=57 gg<-SM(time,event,d2max,opt="log_normal",ta=4,tf=3,delta=0.57,event_ind=1)
time<-c(20,65,12,50,58,65,45,44) event<-c(1,0,0,0,1,1,1,1) d2max=57 gg<-SM(time,event,d2max,opt="log_normal",ta=4,tf=3,delta=0.57,event_ind=1)