getpower.method1: Calculate power for the Cox proportional hazard model with time-dependent exposure using method 1

Description

This functions runs nSim (Number of simulations, specified by the user) Monte Carlo simulations, each time it calling tdSim.method1 internally. The function returns a data frame of scenario-specific parameters (including statistical power) and appends the output to a file with file name specified in the input parameters list. The user also have an option whether to plot an incidence plot or not.

Usage

getpower.method1(nSim, N, duration = 24, med.TTE.Control = 24, rho = 1, med.TimeToCensor = 14, beta, exp.prop, type, scenario, prop.fullexp = 0, maxrelexptime = 1, min.futime = 0, min.postexp.futime = 0, 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).

med.TTE.Control

Median time to event for control group; the default value is 24 (months).

rho

Shape parameter of the Weibull distribution. Default is 1, which will generate survival times by using the exponential distribution.

med.TimeToCensor

Median time to censoring for all subjects. The default value is 14 (months).

beta

A numeric value that represents the exposure effect, which is the regression coefficient (log hazard ratio) that represent the magnitude of the relationship between the exposure covariate and the risk of an event.

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.

type

A text string indicating the what type of dataset is of interest. Either one of "fixed" or "td" should be inputted.

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 decide to not name the scenario.

prop.fullexp

A numeric value in interval [0, 1) that represents the proportion of exposed subjects that are fully exposed from the beginning to the end of the study. The default value is 0, which means all exposed subjects have an exposure status transition at some point during the study.

maxrelexptime

A numeric value in interval (0, 1] that represents the maximum relative exposure time. Suppose this value is p, the exposure time for each subject is then uniformly distributed from 0 to p*subject's time in the study. The default value is 1, which means all exposed subjects have an exposure status transition at any point during the time in study.

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_type: Dataset type specified by the user
i_N: Number of subjects 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_lambda: Value of the scale parameter of the Weibull distribution to generate survival times. Calculated from median time to event for control group, which is specified by the user.
i_rho: User-specified value of the shape parameter of the Weibull distribution to generate survival times
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.method1.

References

Therneau T (2015). A Package for Survival Analysis in S. version 2.38, http://CRAN.R-project.org/package=survival

Examples

Run this code

# Install the survival package if needed.

library(survival)

# 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 with
# time-dependent exposure with both minimum follow-up time (4 months) and
# minimum post-exposure follow-up time (4 months) imposed. Also consider a
# quick exposure after entering the study for each exposed subject. Set the
# maximum relative exposure time to be 1/6. 

# Set the duration of the study to be 24 months; the median time to event for
# control group to be 24 months; exposure effect to be 0.3; median time to
# censoring to be 14 months; and exposure proportion to be 20%.


ret <- getpower.method1(nSim = 10, N = 600, b = 0.3, exp.prop = 0.2,
    type = "td", scenario = " ", maxrelexptime = 1/6, min.futime = 4,
    min.postexp.futime = 4, output.fn = "output.csv")

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