Package 'SimHaz' reference manual

Title:	Simulated Survival and Hazard Analysis for Time-Dependent Exposure
Description:	Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.
Authors:	Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer:	Nusrat Rabbee <[email protected]>
License:	GPL (>= 2)
Version:	0.2
Built:	2025-02-20 03:44:06 UTC
Source:	https://github.com/rabbeelab/simhaz

Simulated Survival and Hazard Analysis for time-dependent

Description

This package generates power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject time's in the study.

Details

Package:	SimHaz
Type:	Package
Version:	0.2
Date:	2015-12-09
License:	GPL-2
Depends: R (>= 3.1.1) Imports: survival

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[email protected]>

Examples

# Simulate a dataset of 600 subjects with time-dependent exposure without
# considering minimum follow-up time or minimum post-exposure follow-up time.
# Specifically, 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%.

df <- tdSim.method1(N = 600, duration = 24, lambda = log(2)/24, rho = 1, 
   beta = 0.3, rateC = log(2)/14, exp.prop = 0.2, 
   prop.fullexp  = 0, maxrelexptime = 1, min.futime = 0,
   min.postexp.futime = 0)
   
# We recommend setting nSim to at least 500. It is set to 10 in the example to
# reduce run time for CRAN submission.

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

# Simulate a dataset of 600 subjects with time-dependent exposure without
# considering minimum follow-up time or minimum post-exposure follow-up time.
# Specifically, 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%.

df <- tdSim.method1(N = 600, duration = 24, lambda = log(2)/24, rho = 1, 
   beta = 0.3, rateC = log(2)/14, exp.prop = 0.2, 
   prop.fullexp  = 0, maxrelexptime = 1, min.futime = 0,
   min.postexp.futime = 0)
   
# We recommend setting nSim to at least 500. It is set to 10 in the example to
# reduce run time for CRAN submission.

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

Calculate power and betahat for Cox proportional Hazard model in the case of exposure matching.

Description

This functions runs nSim (Number of simulations, specified by the user) Monte Carlo simulations, each time it calling tdSim.method1 internally. This function is used in the case of exposure matching where exposed subject is matched with non-exposed subject according to the user-input ratio. Users specify the which Cox model approach to analyze the data with clusters(matching sets in this case).

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.exp.matching(nSim, N_match, duration = 24, med.TTE.Control = 24, 
	rho = 1, med.TimeToCensor = 14, beta, matching.ratio, type, scenario,
	method, prop.fullexp = 0,maxrelexptime = 1, min.futime = 0,
    min.postexp.futime = 0, output.fn, simu.plot = FALSE)
getpower.exp.matching(nSim, N_match, duration = 24, med.TTE.Control = 24, 
	rho = 1, med.TimeToCensor = 14, beta, matching.ratio, type, scenario,
	method, prop.fullexp = 0,maxrelexptime = 1, min.futime = 0,
    min.postexp.futime = 0, output.fn, simu.plot = FALSE)

Arguments

`nSim`	Number of simulations.
`N_match`	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.
`matching.ratio`	Matching ratio used in exposure matching.For 1:1, the user should specify 1.input value 3 corresponds to 1:3 (exposed : unexposed). input value 0.25 corresponds to 4:1 (ie. 1:0.25)
`type`	A text string indicating the what type of dataset is of interest. Either one of "fixed" or "td" should be inputted.
`method`	Specified which Cox model approach to analyze the data with clusters. Can be one of the following: 'frailty', 'fixed effects', 'strata', 'Model with Independence Assumption'.
`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. Only applies when type is "td". When type is "td", the value is automatically 1.
`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 defaul 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.method1.

Value

A data.frame object with columns corresponding to

`i_scenario`	Scenario name specified by the user
`i_type`	Dataset type specified by the user
`i_N_match`	Number of matching set specified by the user
`i_matching.ratio`	Matching ratio used in exposure matching 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_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).
`i_beta`	Value of the input beta
`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
`variance`	Variance of the betahat from the simulations

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[email protected]>

References

Therneau T (2015). A Package for Survival Analysis in S. version 2.38,

http://CRAN.R-project.org/package=survival

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 100 matches
# 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. 


ret = getpower.exp.matching(nSim=10, N_match=100,duration=24,
    med.TTE.Control=24,rho=1, med.TimeToCensor=14, beta=0.7,
    matching.ratio=3,type="td", scenario="exposure_matching",  
    method="marginal", prop.fullexp=0,maxrelexptime=1,
    min.futime=0, min.postexp.futime=0, output.fn="result_matching",
    simu.plot=FALSE)

# 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 100 matches
# 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. 


ret = getpower.exp.matching(nSim=10, N_match=100,duration=24,
    med.TTE.Control=24,rho=1, med.TimeToCensor=14, beta=0.7,
    matching.ratio=3,type="td", scenario="exposure_matching",  
    method="marginal", prop.fullexp=0,maxrelexptime=1,
    min.futime=0, min.postexp.futime=0, output.fn="result_matching",
    simu.plot=FALSE)

Calculate betahat bias and variance for different matching ratios in the case of exposure matching in the Cox proportional Hazard model

Description

This function is used to explore what matching ratios should be used in the case of exposure matching. It simulate a large population (100000 subjects) in order to estimate a betahat value of that population. Then in repeated simulation, a subset of that population is drawn and exposure matching is done according to a user-specified list of ratios in order to compare the bias in the betahat estimate from the betahat value estimated from the large populations well as the variance of the betahat estimate for each ratio.

Usage


getpower.exp.matching.opt(nSim, N, ratios=c(1,0.25,0.333,0.5,2,3,4,5),
    duration=24, med.TTE.Control=24,rho=1,med.TimeToCensor=14,beta, 
	exp.prop,type,scenario, method,  prop.fullexp=0,maxrelexptime=1,
	min.futime=0,min.postexp.futime=0, output.fn=NULL,simu.plot=FALSE) 
getpower.exp.matching.opt(nSim, N, ratios=c(1,0.25,0.333,0.5,2,3,4,5),
    duration=24, med.TTE.Control=24,rho=1,med.TimeToCensor=14,beta, 
	exp.prop,type,scenario, method,  prop.fullexp=0,maxrelexptime=1,
	min.futime=0,min.postexp.futime=0, output.fn=NULL,simu.plot=FALSE)

Arguments

`nSim`	Number of simulations.
`N`	Number of subjects to be screened.
`ratios`	Specification for matching ratio as a list of numbers. For 1:1, the user should specify 1.input value 3 corresponds to 1:3 (exposed : unexposed). input value 0.25 corresponds to 4:1 (ie. 1:0.25). A list of c(1,0.25, 2) corresponds to the following matching ratios: 1:1, 4:1, 1:2
`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`	test
`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.
`method`	Specified which Cox model approach to analyze the data with clusters(matching sets in this case). Can be one of the following: 'frailty', 'fixed effects', 'strata', 'Model with Independence Assumption'.
`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. Only applies when type is "td". When type is "td", the value is automatically 1.
`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 defaul 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.method1.

Value

A data.frame object with columns corresponding to

`i_scenario`	Scenario name specified by the user
`i_type`	Dataset type 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_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).
`i_beta`	value of the input beta
`N_match`	Number of matching sets
`N_exposed`	Number of exposed subjects
`N_unexposed`	Number of unexposed subjects
`bhat`	Simulated value of regression coefficient (log hazard ratio)
`pow`	Simulated statistical power from the Cox regression model on data with time-dependent exposure
`actual_beta`	Value of betahat based on the population of 100000 subjects.
`bias`	Value of betahat - actual_betahat(approximated from the big population)
`variance`	Variance of the betahat from the simulations
`RE1`	The variance of betahat of the ratio in that row divided by variance of betahat for the 1:1 matching
`RE2`	The variance of betahat of the ratio in that row divided by variance of the betahat for the closest ratio based on the exposure proportion in the population

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[email protected]>

References

Therneau T (2015). A Package for Survival Analysis in S. version 2.38,

http://CRAN.R-project.org/package=survival

Examples

# We recommend setting nSim to at least 500. It is set to 5 in the example to
# reduce run time for CRAN submission.

# Run 5 simulations. Each time simulate a dataset of 400 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. 


ret = getpower.exp.matching.opt(nSim=5, N=400, ratios=c(1), duration=24,
    med.TTE.Control=24, rho=1,med.TimeToCensor=14,beta=0.5, exp.prop=0.3, 
    type="td",scenario="opt_exp_matching", method="marginal",prop.fullexp=0,
    maxrelexptime=1,min.futime=0,min.postexp.futime=0, output.fn="opt_matching",
    simu.plot=FALSE)

# We recommend setting nSim to at least 500. It is set to 5 in the example to
# reduce run time for CRAN submission.

# Run 5 simulations. Each time simulate a dataset of 400 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. 


ret = getpower.exp.matching.opt(nSim=5, N=400, ratios=c(1), duration=24,
    med.TTE.Control=24, rho=1,med.TimeToCensor=14,beta=0.5, exp.prop=0.3, 
    type="td",scenario="opt_exp_matching", method="marginal",prop.fullexp=0,
    maxrelexptime=1,min.futime=0,min.postexp.futime=0, output.fn="opt_matching",
    simu.plot=FALSE)

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)
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.
`N`	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. Only applies when type is "td". When type is "td", the value is automatically 1.
`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 defaul 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.method1.

Value

A data.frame object with columns corresponding to

`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

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[email protected]>

References

Therneau T (2015). A Package for Survival Analysis in S. version 2.38,

http://CRAN.R-project.org/package=survival

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 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")

# 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")

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)
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 defaul 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 <[email protected]>

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) 
# 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)

Calculate power for the Cox proportional hazard model with time-dependent exposure and multiple centers using method1

Description

This functions runs nSim (Number of simulations, specified by the user) Monte Carlo simulations, each time calling tdSim.multicenter 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.multicenter(nSim,N,duration=24,rho=1,beta,med.TimeToCensor=14,
    df,dist=NULL, method, type, scenario, prop.fullexp=0, maxrelexptime=1, 
    min.futime=0, min.postexp.futime=0, output.fn,simu.plot=FALSE) 
getpower.multicenter(nSim,N,duration=24,rho=1,beta,med.TimeToCensor=14,
    df,dist=NULL, method, type, scenario, prop.fullexp=0, maxrelexptime=1, 
    min.futime=0, min.postexp.futime=0, 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).
`rho`	Shape parameter of the Weibull distribution. Default is 1, which will generate survival times by using the exponential distribution.
`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.
`med.TimeToCensor`	Median time to censoring for all subjects. The default value is 14 (months).
`df`	A user-specified n by 4 clustering data frame with columns corresponding to cat_id (category id, which is the physician site id. It can be either text strings or integers), center.size (number of subjects within each center), cat_exp.prop (proportion of exposed subjects in each center), and med.TTE.Control (median time to control event for each center). n rows corresponds to n different centers
`dist`	The distribution of the center effect across centers. Default is NULL. If dist= 'gamma', then a random frailty effect from a gamma distribution with scale 0.5 and shape 2 is used.
`method`	Specified which Cox model approach to analyze the data with centers. Can be one of the following: 'frailty', 'fixed effects', 'strata', 'Model with Independence Assumption'.
`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. Only applies when type is "td". When type is "td", the value is automatically 1.
`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.

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.multicenter.

Value

A data.frame object with columns corresponding to

`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

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[email protected]>

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 300 subjects

input_df1 <- data.frame(cat_id = c("low","med","high"), center.size = rep(100,3), 
                        cat_exp.prop = rep(1/3, 3), med.TTE.Control=c(14,20,31))

df_strat <- getpower.multicenter(nSim = 10, N = 300, beta = 0.7, 
    df = input_df1,method="strata",  type = "td",  scenario = "strata", 
    maxrelexptime = 1/6, min.futime = 4, min.postexp.futime = 4, 
    output.fn = "output_mult1.csv")
	
# 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 300 subjects

input_df1 <- data.frame(cat_id = c("low","med","high"), center.size = rep(100,3), 
                        cat_exp.prop = rep(1/3, 3), med.TTE.Control=c(14,20,31))

df_strat <- getpower.multicenter(nSim = 10, N = 300, beta = 0.7, 
    df = input_df1,method="strata",  type = "td",  scenario = "strata", 
    maxrelexptime = 1/6, min.futime = 4, min.postexp.futime = 4, 
    output.fn = "output_mult1.csv")

Plot power curves for survival analysis with time-dependent exposure

Description

This function plots a power curve at each time and returns a subsetted data frame that match the list of input parameters.

Usage

plot_power(table_df, N, type, exp.prop, min.futime, min.postexp.futime,
    show.plot = FALSE, newplot = FALSE, col = NULL, lty, lwd, pch)
plot_power(table_df, N, type, exp.prop, min.futime, min.postexp.futime,
    show.plot = FALSE, newplot = FALSE, col = NULL, lty, lwd, pch)

Arguments

`table_df`	A data frame read from a .csv file of a format output from the getpower.method1 function.
`N`	Number of subjects needs to be screened
`type`	A text string indicating the what type of dataset is of interest. Either one of "fixed" or "td" should be inputted
`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
`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
`show.plot`	A logical value indicating whether to output a power curve or not. The default value is TRUE
`newplot`	A logical value indicating whether to create a new plot or add to an existing plot
`col`, `lty`, `lwd`, `pch`	Graphical parameters as in the regular plot function in R

Details

The gist of this function is that the user can check the plot with the values in the output data frame. Moreover, it is flexible that the user can choose to only output the data frame in order to plot their own graph (such as add titles or legends) based on the data. The user can also add as many lines as possible to an existing power curve plot so that he/she is allowed to compare different scenarios.

Value

A data.frame object with columns corresponding to

`i_N`	Number of subjects needs to be screened, specified by the user
`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
`i_beta`	Input value of regression coefficient (log hazard ratio)
`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 <[email protected]>

Examples

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

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")
	
ret2 <- 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 = 0, output.fn ="output.csv")
	
# Read in .csv file as a data frame

tb <-  read.csv("output.csv", header = TRUE, sep = ",")

	# Visualize the subsetted data frame of interest and create a new plot

visualize1 <- plot_power(table_df = tb, N = 600, type = "td", exp.prop = 0.2,
    min.futime = 4, min.postexp.futime = 4, show.plot = TRUE, newplot = TRUE,
    col = "red", lty = 1, lwd = 2, pch = 16)

# Add a different power curve to the previously created plot

visualize2 <- plot_power(table_df = tb, N = 600, type = "td", exp.prop=0.2, 
    min.futime = 4, min.postexp.futime = 0, show.plot = TRUE, newplot = FALSE,
    col = "blue", lty = 1, lwd = 2, pch = 16)
# We recommend setting nSim to at least 500. It is set to 10 in the example to
# reduce run time for CRAN submission.

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")
	
ret2 <- 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 = 0, output.fn ="output.csv")
	
# Read in .csv file as a data frame

tb <-  read.csv("output.csv", header = TRUE, sep = ",")

	# Visualize the subsetted data frame of interest and create a new plot

visualize1 <- plot_power(table_df = tb, N = 600, type = "td", exp.prop = 0.2,
    min.futime = 4, min.postexp.futime = 4, show.plot = TRUE, newplot = TRUE,
    col = "red", lty = 1, lwd = 2, pch = 16)

# Add a different power curve to the previously created plot

visualize2 <- plot_power(table_df = tb, N = 600, type = "td", exp.prop=0.2, 
    min.futime = 4, min.postexp.futime = 0, show.plot = TRUE, newplot = FALSE,
    col = "blue", lty = 1, lwd = 2, pch = 16)

Make an incidence plot from simulated data.

Description

Create an incidence plot ordered by follow-up time from a survival data simulated.

Usage

plot_simuData(data, title="Sample Survival Data")
plot_simuData(data, title="Sample Survival Data")

Arguments

`data`	A dataframe of survival data containing the following columns: id, start, stop, status, x
`title`	Title of the graph

Details

This makes the incidence plot of the survival data based on the input dataframe from the tdSim.method1 or tdSim.method2 function. More generally, this function would also works with a dataframe containing survival data with the same columns name as indicated above.

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[email protected]>

Examples

dat <- tdSim.method2(500, duration=24,lambda12=1.3,lambda23=0.04, 
    lambda13=0.03, exp.prop=0.2,rateC=0.05, min.futime=4, min.postexp.futime=4)
	
plot_simuData(dat, title='method2_filter')
dat <- tdSim.method2(500, duration=24,lambda12=1.3,lambda23=0.04, 
    lambda13=0.03, exp.prop=0.2,rateC=0.05, min.futime=4, min.postexp.futime=4)
	
plot_simuData(dat, title='method2_filter')

Simulate 1 dataframe (1 simulation) of time-dep exposure in the case of exposure matching

Description

This function simulate Survival Data. It generates a simulated dataset with time-dependent exposure with a user-specified list of parameters as input as well as matching id in order to do exposure matching.

Usage


tdSim.exp.matching(N_match, duration=24,lambda, rho=1, beta,
	rateC,matching.ratio=3,prop.fullexp=0,maxrelexptime=1,min.futime=0,
	min.postexp.futime=0)
tdSim.exp.matching(N_match, duration=24,lambda, rho=1, beta,
	rateC,matching.ratio=3,prop.fullexp=0,maxrelexptime=1,min.futime=0,
	min.postexp.futime=0)

Arguments

`N_match`	Number of matching sets
`duration`	Length of the study in Months. The default value is 24 (months)
`lambda`	Scale parameter of the Weibull distribution, which is calculated as log(2) / median time to event for control group
`rho`	Shape parameter of the Weibull distribution, which is defaulted as 1, as we generate survival times by using the exponential distribution
`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
`rateC`	Rate of the exponential distribution to generate censoring times, which is calculated as log(2) / median time to censoring
`matching.ratio`	Matching ratio used in exposure matching.For 1:1, the user should specify 1.input value 3 corresponds to 1:3 (exposed : unexposed). input value 0.25 corresponds to 4:1 (ie. 1:0.25).
`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

Details

Simulata a Survival dataset using a modified version of illness-death model controlled by lambda12, lambda23, lambda13

Value

A data.frame object with columns corresponding to

`id`	Integer that represents a subject's identification number
`start`	For counting process formulation. Represents the start of each time interval
`stop`	For counting process formulation. Represents the end of each time interval
`status`	Indicator of event. status = 1 when event occurs and 0 otherwise
`x`	Indicator of exposure. x = 1 when exposed and 0 otherwise
`match_id`	Integer that represents a subject's matching set.

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[email protected]>

Examples



df = tdSim.exp.matching<-function(N_match, duration=24,lambda, rho=1, 
    beta, rateC,matching.ratio=3,  prop.fullexp=0,maxrelexptime=1,min.futime=0,
    min.postexp.futime=0)

df = tdSim.exp.matching<-function(N_match, duration=24,lambda, rho=1, 
    beta, rateC,matching.ratio=3,  prop.fullexp=0,maxrelexptime=1,min.futime=0,
    min.postexp.futime=0)

Simulate 1 dataframe (1 simulation) of time-dep exposure under method 1

Description

This function generates a simulated dataset with time-dependent exposure under method 1 with a user-specified list of parameters as input. Survival times and censoring times are generated from the exponential distribution.

Usage

tdSim.method1(N, duration = 24, lambda, rho = 1, beta, rateC, exp.prop, 
    prop.fullexp  = 0, maxrelexptime = 1, min.futime = 0, min.postexp.futime = 0)
tdSim.method1(N, duration = 24, lambda, rho = 1, beta, rateC, exp.prop, 
    prop.fullexp  = 0, maxrelexptime = 1, min.futime = 0, min.postexp.futime = 0)

Arguments

`N`	Number of subjects needs to be screened
`duration`	Length of the study in Months. The default value is 24 (months)
`lambda`	Scale parameter of the Weibull distribution, which is calculated as log(2) / median time to event for control group
`rho`	Shape parameter of the Weibull distribution, which is defaulted as 1, as we generate survival times by using the exponential distribution
`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
`rateC`	Rate of the exponential distribution to generate censoring times, which is calculated as log(2) / median time to censoring
`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
`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

Details

If no minimum follow-up time or minimum post-exposure follow-up time is considered (min.fut = 0 and min.postexp.fut = 0), then the output data frame will have N subjects. If we consider minimum follow-up time or both, then the output data frame will have at most N subjects.

Value

A data.frame object with columns corresponding to

`id`	Integer that represents a subject's identification number
`start`	For counting process formulation. Represents the start of each time interval
`stop`	For counting process formulation. Represents the end of each time interval
`status`	Indicator of event. status = 1 when event occurs and 0 otherwise
`x`	Indicator of exposure. x = 1 when exposed and 0 otherwise

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[email protected]>

References

Therneau and C. Crowson (2015). Using Time Dependent Covariates and Time Dependent Coefficients in the Cox Model.

https://cran.r-project.org/web/packages/survival/vignettes/timedep.pdf

Examples

# Simulate a dataset of 600 subjects with time-dependent exposure without
# considering minimum follow-up time or minimum post-exposure follow-up time.
# Specifically, 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%.

df1 <- tdSim.method1(N = 600, duration = 24, lambda = log(2)/24, rho = 1, 
   beta = 0.3, rateC = log(2)/14, exp.prop = 0.2, prop.fullexp  = 0, 
   maxrelexptime = 1, min.fut = 0, min.postexp.fut = 0)

# 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. Other parameters remain the same as
# in the first case.

df2 <- tdSim.method1(N = 600, duration = 24, lambda = log(2)/24, rho = 1, 
   beta = 0.3, rateC = log(2)/14, exp.prop = 0.2, prop.fullexp  = 0, 
   maxrelexptime = 1, min.fut = 4, min.postexp.fut = 4)

# 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. Other parameters remain the same as in the first case.

df3 <- tdSim.method1(N = 600, duration = 24, lambda = log(2)/24, rho = 1, 
   beta = 0.3, rateC = log(2)/14, exp.prop = 0.2, prop.fullexp  = 0,
   maxrelexptime = 1/6, min.fut = 4,min.postexp.fut = 4)
# Simulate a dataset of 600 subjects with time-dependent exposure without
# considering minimum follow-up time or minimum post-exposure follow-up time.
# Specifically, 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%.

df1 <- tdSim.method1(N = 600, duration = 24, lambda = log(2)/24, rho = 1, 
   beta = 0.3, rateC = log(2)/14, exp.prop = 0.2, prop.fullexp  = 0, 
   maxrelexptime = 1, min.fut = 0, min.postexp.fut = 0)

# 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. Other parameters remain the same as
# in the first case.

df2 <- tdSim.method1(N = 600, duration = 24, lambda = log(2)/24, rho = 1, 
   beta = 0.3, rateC = log(2)/14, exp.prop = 0.2, prop.fullexp  = 0, 
   maxrelexptime = 1, min.fut = 4, min.postexp.fut = 4)

# 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. Other parameters remain the same as in the first case.

df3 <- tdSim.method1(N = 600, duration = 24, lambda = log(2)/24, rho = 1, 
   beta = 0.3, rateC = log(2)/14, exp.prop = 0.2, prop.fullexp  = 0,
   maxrelexptime = 1/6, min.fut = 4,min.postexp.fut = 4)

Simulate 1 dataframe (1 simulation) of time-dep exposure under method 2

Description

This function simulate Survival Data. It generates a simulated dataset with time-dependent exposure under method 2 with a user-specified list of parameters as input.

Usage


tdSim.method2(N,duration, lambda12, lambda23=NULL, lambda13, 
    HR=NULL, exp.prop,rateC, min.futime = 0, min.postexp.futime = 0)
tdSim.method2(N,duration, lambda12, lambda23=NULL, lambda13, 
    HR=NULL, exp.prop,rateC, min.futime = 0, min.postexp.futime = 0)

Arguments

`N`	Number of subjects
`duration`	Duration of the study. This is used in censoring
`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

Details

Simulata a Survival dataset using a modified version of illness-death model controlled by lambda12, lambda23, lambda13

Value

A data.frame object with columns corresponding to

`id`	Integer that represents a subject's identification number
`start`	For counting process formulation. Represents the start of each time interval
`stop`	For counting process formulation. Represents the end of each time interval
`status`	Indicator of event. status = 1 when event occurs and 0 otherwise
`x`	Indicator of exposure. x = 1 when exposed and 0 otherwise

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[email protected]>

Examples


sim_data <- tdSim.method2(500, duration=24,lambda12=1.3,lambda23=0.04, 
    lambda13=0.03, exp.prop=0.2,rateC=0.05, min.futime=4, min.postexp.futime=4)
sim_data <- tdSim.method2(500, duration=24,lambda12=1.3,lambda23=0.04, 
    lambda13=0.03, exp.prop=0.2,rateC=0.05, min.futime=4, min.postexp.futime=4)

Simulate 1 dataframe (1 simulation) of time-dependent exposure with multiple centers

Description

This function allows the user to input a data frame with multi-center parameters and generates a simulated dataset with time-dependent exposure. In particular, the output dataset has a column corresponding to the center id, which will be used as a clustering variable in the Cox regression model in power calculation.

Usage


tdSim.multicenter(N,duration=24,rho=1,beta,rateC,df,
    prop.fullexp=0,maxrelexptime=1,min.futime=0,
    min.postexp.futime=0, dist=NULL)  
tdSim.multicenter(N,duration=24,rho=1,beta,rateC,df,
    prop.fullexp=0,maxrelexptime=1,min.futime=0,
    min.postexp.futime=0, dist=NULL)

Arguments

`N`	Number of subjects needs to be screened
`duration`	Length of the study in Months. The default value is 24 (months)
`rho`	Shape parameter of the Weibull distribution, which is defaulted as 1, as we generate survival times by using the exponential distribution
`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
`rateC`	Rate of the exponential distribution to generate censoring times, which is calculated as log(2) / median time to censoring
`df`	A user-specified n by 4 clustering data frame with columns corresponding to cat_id (category id, which is the physician site id. It can be either text strings or integers), center.size (number of subjects within each center), cat_exp.prop (proportion of exposed subjects in each center), and med.TTE.Control (median time to control event for each center). n rows corresponds to n different centers
`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
`dist`	The distribution of the center effect across centers. Default is NULL. If dist= 'gamma', then a random frailty effect from a gamma distribution with scale 0.5 and shape 2 is used.

Details

The current version of this function allows the user to input a data frame with at least 3 categories of physician sites, because the function uses a multinomial distribution to assign subjects into each category according to the corresponding category proportion

Value

A data.frame object with columns corresponding to

`id`	Integer that represents a subject's identification number
`start`	For counting process formulation. Represents the start of each time interval
`stop`	For counting process formulation. Represents the end of each time interval
`status`	Indicator of event. status = 1 when event occurs and 0 otherwise
`x`	Indicator of exposure. x = 1 when exposed and 0 otherwise
`clst_id`	For clustering in the Cox proportional hazard model. Represents label of each subject's corresponding physician site

Author(s)

Danyi Xiong, Teeranan Pokaprakarn, Hiroto Udagawa, Nusrat Rabbee
Maintainer: Nusrat Rabbee <[email protected]>

References

T. Therneau and C. Crowson (2015). Using Time Dependent Covariates and Time Dependent Coefficients in the Cox Model.

https://cran.r-project.org/web/packages/survival/vignettes/timedep.pdf

Examples

# Create a clustering data frame as input with 3 categories and a 20% weighted
# exposure proportion.
  
input_df1 <- data.frame(cat_id = c("low","med","high"), 
                        center.size = rep(100,3),  
                        cat_exp.prop = rep(1/3, 3), 
                        med.TTE.Control=c(14,20,31))

df_strat <- tdSim.multicenter(N = 300, duration =24, rateC = log(2)/14, beta = 0.7, 
    df = input_df1, maxrelexptime = 1/6, min.futime = 4, min.postexp.futime = 4)

# Create a clustering data frame as input with 3 categories and a 20% weighted
# exposure proportion.
  
input_df1 <- data.frame(cat_id = c("low","med","high"), 
                        center.size = rep(100,3),  
                        cat_exp.prop = rep(1/3, 3), 
                        med.TTE.Control=c(14,20,31))

df_strat <- tdSim.multicenter(N = 300, duration =24, rateC = log(2)/14, beta = 0.7, 
    df = input_df1, maxrelexptime = 1/6, min.futime = 4, min.postexp.futime = 4)

Package 'SimHaz'

Help Index

Simulated Survival and Hazard Analysis for time-dependent

Description

Details

Author(s)

Examples

Calculate power and betahat for Cox proportional Hazard model in the case of exposure matching.

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Calculate betahat bias and variance for different matching ratios in the case of exposure matching in the Cox proportional Hazard model

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

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

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

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

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Calculate power for the Cox proportional hazard model with time-dependent exposure and multiple centers using method1

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Plot power curves for survival analysis with time-dependent exposure

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Make an incidence plot from simulated data.

Description

Usage

Arguments

Details

Author(s)

Examples

Simulate 1 dataframe (1 simulation) of time-dep exposure in the case of exposure matching

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Simulate 1 dataframe (1 simulation) of time-dep exposure under method 1

Description

Usage

Arguments

Details

Value

Author(s)