## Example 2 - sample size under proportional hazard The design setting is: 5 years’ entry time and 5 years’ follow-up time; Median survival for control group is 10 years. The hazard ratio comparing treatment and control is 0.5.The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
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Package: nphPower provides functions to perform combination test including maximum weighted logrank test (MWLR) and projection test, to calculate sample size with MWLR in a simulation-free approach allowing for staggered entry, drop-out etc, to visualize the design parameters and to simulate survival data with flexible design input.
You can install the released version of nphPower from CRAN with:
install.packages("nphPower")And the development version from GitHub with:
This is a basic example which shows you how to perform maximum weighted logrank test.
Load the lung data and only keep columns for analysis.
lung <- nphPower::lung
tmpd <- with(lung, data.frame(time = SurvTime, stat = 1-censor, grp = Treatment))Generate the weight functions for maxcombo test
Perform the test using pooled Kaplan-Meier estimate of CDF as base function and visualize the weight functions
## Example 2 - sample size under proportional hazard The design setting is: 5 years’ entry time and 5 years’ follow-up time; Median survival for control group is 10 years. The hazard ratio comparing treatment and control is 0.5.
t_enrl <- 5; t_fup <- 5 ; lmd0 <- -log(0.2)/10 ; HR <- 0.5
eg1 <- pwr2n.LR(method = "schoenfeld", lambda0 = lmd0,
lambda1 = lmd0*HR, entry = t_enrl, fup = t_fup)
#> ------------------------------------------
#> -----Summary of the Input Parameters-----
#> ------------------------------------------
#> __Parameter__ __Value__
#> Method schoenfeld
#> Lambda1/Lambda0/HR 0.08/0.161/0.5
#> Entry Time 5
#> Follow-up Time 5
#> Allocation Ratio 1
#> Type I Error 0.05
#> Type II Error 0.1
#> Alternative two.sided
#> Drop-out Parameter Not Provided
#> ------------------------------------------
#> -----Summary of the Output Parameters-----
#> ------------------------------------------
#> __Parameter__ __Value__
#> Number of Events 87.479
#> Number of Total Sampe Size 153.173
#> Overall Event Rate 0.571Design setting: patients are enrolled within 12 months and the last enrolled patient has at least 18 months’ follow-up. The medial survival time for control group is 12 months. The treatment has delayed effects. The hazard ratio is 0.75 after 6 months. Maxcombo test is used.
t_enrl <- 12; t_fup <- 18; lmd0 <- log(2)/12
f_hr_delay <- function(x){(x<=6)+(x>6)*0.75}
f_haz0 <- function(x){lmd0*x^0}
snph1 <- pwr2n.NPH(entry = t_enrl, fup = t_fup, Wlist = wmax,
k = 50, ratio = 2, CtrlHaz = f_haz0, hazR = f_hr_delay)
#> -----Summary of the Input Parameters-----
#> parameter value
#> Method MaxLR
#> Entry Time 12
#> Follow-up Time 18
#> Allocation Ratio 2
#> Type I Error 0.05
#> Type II Error 0.1
#> Alternative two.sided
#> Number of Weights 4
#> -----Summary of the Output Parameters-----
#> parameter value
#> Number of Events 1198.779
#> Number of Total Sampe Size 1720.967
#> Asymptotic Power 0.900
#> Overall Event Rate 0.697A time-to-event data set with settings in example 3 is simulated.
N <- round(snph1$totalN, digits = 0)
set.seed(12345)
simu1 <- simu.trial(type = "time", trial_param = c(N,t_enrl,
t_fup), bsl_dist = "weibull", bsl_param = c(1,lmd0),
HR_fun = f_hr_delay, ratio = 1)
#> Notes: Drop-outs are not considered in the simulation.
#> -------- Summary of the Simulation --------
#> parameter value
#> 1 Trial Type: time
#> 2 Entry Time: 12
#> 3 Maximum Study Duration: 30
#> 4 Number of Subjects: 1721
#> 5 Number of Events: 1259More functions can be found in the package.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.