getpower.method2: 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.

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.

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.

Value

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

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.

Examples

Run this code

# 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)

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