getpower.method2 {SimHaz}

Calculate power for the Cox proportional hazard model with time-dependent exposure using method 2

Description

This functions runs nSim (Number of simulations, specified by the user) Monte Carlo simulations, each time calling tdSim.method2 internally. The function returns a data frame of scenario-specific input parameters- and also output statistical power. The user has the option to append the output to a file with file name specified in the input parameters list.

Usage

getpower.method2(nSim = 500, N, duration = 24, scenario, lambda12,
  lambda23 = NULL, lambda13, HR = NULL, exp.prop, rateC, min.futime, 
  min.postexp.futime, output.fn, simu.plot = FALSE)

Arguments

nSim

Number of simulations.

N

Number of subjects to be screened.

duration

Length of the study in months; the default value is 24 (months).

scenario

Any text string inputted by the user as an option to name a scenario that is being simulated. The use can simply put " " if he/she decides to not name the scenario.

lambda12

Lambda12 parameter to control time to exposure.

lambda23

Lambda23 parameter to control time to event after exposure.

lambda13

Lambda13 parameter to control time to event in the control group.

HR

Hazard Ratio. This input is optional. If HR is set and lambda23 is not set, lambda23 = lambda13*HR.

exp.prop

A numeric value between 0 and 1 (not include 0 and 1) that represents the proportion of subjects that are assigned with an exposure.

rateC

Rate of the exponential distribution to generate censoring times.

min.futime

A numeric value that represents minimum follow-up time (in months). The default value is 0, which means no minimum follow-up time is considered. If it has a positive value, this argument will help exclude subjects that only spend a short amount of time in the study.

min.postexp.futime

A numeric value that represents minimum post-exposure follow-up time (in months). The default value is 0, which means no minimum post-exposure follow-up time is considered. If it has a positive value, this argument will help exclude subjects that only spend a short amount of time in the study after their exposure.

output.fn

A .csv filename to write in the output. If the filename does not exist, the function will create a new .csv file for the output.

simu.plot

A logical value indicating whether or not to output an incidence plot.The default value is FALSE.

Details

The function calculates power based on the Cox regression model, which calls the coxph function from the survival library using the the simulated data from tdSim.method2.

Value

A data.frame object with columns corresponding to

i_scenario

Scenario name specified by the user

i_N

Number of subjects needs to be screened, specified by the user

i_min.futime

Minimum follow-up time to be considered, specified by the user

i_min.postexp.futime

Minimum post-exposure follow-up time to be considered, specified by the user

i_exp.prop

Exposure rate specified by the user

i_lambda12

Lambda12 parameter to control time to exposure

i_lambda23

Lambda23 parameter to control time to event after exposure

i_lambda13

Lambda13 parameter to control time to event in the control group

i_rateC

Rate of the exponential distribution to generate censoring times. Calculated from median time to censoring, which is specified by the user. i_beta Input value of regression coefficient (log hazard ratio)

N_eff

Simulated number of evaluable subjects, which is the resulting number of subjects with or without considering minimum follow-up time and/or minimum post-exposure follow-up time

N_effexp_p

Simulated proportion of exposed subjects with or without considering minimum follow-up time and/or minimum post-exposure follow-up time

bhat

Simulated value of regression coefficient (log hazard ratio)

HR

Simulated value of hazard ratio

d

Simulated number of events in total

d_c

Simulated number of events in control group

d_exp

Simulated number of events in exposed group

mst_c

Simulated median survival time in control group

mst_exp

Simulated median survival time in exposed group

pow

Simulated statistical power from the Cox regression model on data with time-dependent exposure

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[e-mail]>

Examples

# We recommend setting nSim to at least 500. It is set to 10 in the example to
# reduce run time for CRAN submission.

# Run 10 simulations. Each time simulate a dataset of 600 subjects

ret <- getpower.method2(nSim=10, N=600, duration=24, scenario="test",
  lambda12=1.3, lambda23=0.04, lambda13=0.03, HR=NULL,exp.prop=0.2, rateC=0.05,
  min.futime=4, min.postexp.futime=4,output.fn="database.csv", simu.plot=FALSE)