Description Usage Arguments Details Value Author(s) Examples

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.

1 2 3 | ```
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)
``` |

`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. |

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.

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 |

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee

Maintainer: Nusrat Rabbee <[email protected]>

1 2 3 4 5 6 7 8 | ```
# 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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