In hcheng99/nphPower: Sample Size Calculation under Non-Proportional Hazards

knitr::opts_chunk$set(
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package: nphPower

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.

Installation

You can install the released version of nphPower from CRAN with:

install.packages("nphPower")

library("nphPower")

And the development version from GitHub with:

# install.packages("devtools")
#devtools::install_github("hcheng99/nphPower")

Example 1 - perform maximum weighted logrank test

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

wmax <- gen.wgt(method = "Maxcombo")

Perform the test using pooled Kaplan-Meier estimate of CDF as base function and visualize the weight functions

t1 <- MaxLRtest(tmpd, Wlist = wmax, base = c("KM"),
  alternative = c("two.sided"))
plot(t1)

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)

Example 3 - sample size under nonproportional hazard

Design 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)

Example 4 - trial data simulation

A 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)