Package 'dejaVu' reference manual

Title:	Multiple Imputation for Recurrent Events
Description:	Performs reference based multiple imputation of recurrent event data based on a negative binomial regression model, as described by Keene et al (2014) <doi:10.1002/pst.1624>.
Authors:	Nikolas Burkoff [aut], Paul Metcalfe [aut], Jonathan Bartlett [aut, cre], David Ruau [aut]
Maintainer:	Jonathan Bartlett <[email protected]>
License:	GPL (>=2)
Version:	0.3.1
Built:	2024-11-08 05:00:06 UTC
Source:	https://github.com/jwb133/dejavu

Create a Dropout Mechanism with constant dropout rate

Description

Creates an MCAR DropoutMechanism object where subject i dropout is exponentially distributed with rate Ri where Ri = C*exp(Xi) for constant C and Xi a random normal variable with mean 0 and standard deviation sigma

Usage

ConstantRateDrop(rate, var = 0)
ConstantRateDrop(rate, var = 0)

Arguments

`rate`	`C` described in the details
`var`	`sigma^2` described in the details section, by default = 0

Value

A DropoutMechanism object

Examples

ConstantRateDrop(rate=0.0025)
ConstantRateDrop(rate=0.0025,var=1)
ConstantRateDrop(rate=0.0025)
ConstantRateDrop(rate=0.0025,var=1)

Create a copy reference `ImputeMechanism` object

Description

Missing counts for subjects in both arms are imputed by assuming the rate before and dropout are both equal to the control (reference) estimated rate. This corresponds to what is usually termed the copy reference assumption.

Usage

copy_reference(proper = TRUE)
copy_reference(proper = TRUE)

Arguments

proper

If proper=TRUE then proper imputation is performed, in which each imputation is created based on parameters values drawn from the (approximate) posterior distribution of the imputation model. If proper=FALSE, improper imputation is performed. This means all imputed datasets are generated conditional on the maximum likelihood estimates of the parameters.

Value

An ImputeMechanism object

Examples

 
sim <- SimulateComplete(study.time=365,number.subjects=50,
                        event.rates=c(0.01,0.005),dispersions=0.25)
sim.with.MCAR.dropout <- SimulateDropout(sim,
                                         drop.mechanism = ConstantRateDrop(rate = 0.0025))
fit <- Simfit(sim.with.MCAR.dropout)
imps <- Impute(fit, copy_reference(), 10)

sim <- SimulateComplete(study.time=365,number.subjects=50,
                        event.rates=c(0.01,0.005),dispersions=0.25)
sim.with.MCAR.dropout <- SimulateDropout(sim,
                                         drop.mechanism = ConstantRateDrop(rate = 0.0025))
fit <- Simfit(sim.with.MCAR.dropout)
imps <- Impute(fit, copy_reference(), 10)

A function which creates a DropOut Mechanism object

Description

A function which creates a DropOut Mechanism object

Usage

CreateNewDropoutMechanism(
  type,
  text,
  cols.needed = vector("character"),
  GetDropTime,
  parameters = NULL
)
CreateNewDropoutMechanism(
  type,
  text,
  cols.needed = vector("character"),
  GetDropTime,
  parameters = NULL
)

Arguments

`type`	The type of mechanism (e.g. "MCAR" or "MNAR")
`text`	A short string describing the mechanism (only used for printing)
`cols.needed`	Which columns in the SingleSim$data data frame must be included for this drop out mechanism to work. This option could allow drop out mechanism which depend on covariates to be included.
`GetDropTime`	A function with two arguments event.times and data, the corresponding entries from the SingleSim object. This function should return a list of dropout times (if a subject does not dropout its dropout time should be their current censored.time (i.e. the study follow up time))
`parameters`	A list of named parameters for the mechanism (only used for printing) or NULL if none

Value

A DropoutMechanism.object

A function which creates an Impute Mechanism object

Description

A function which creates an Impute Mechanism object

Usage

CreateNewImputeMechanism(
  name,
  cols.needed = vector("character"),
  impute,
  parameters = NULL
)
CreateNewImputeMechanism(
  name,
  cols.needed = vector("character"),
  impute,
  parameters = NULL
)

Arguments

`name`	The method name (used for printing)
`cols.needed`	which columns of the SingleSim data frame are required by the method, typically `c("censored.time","observed.events","arm")`
`impute`	A function which takes a `SingleSimFit` object and outputs the details for a single imputed data set, specifically a list with two elements: `new.censored.times` - a vector of times subjects were censored (after taking into account imputation) and `newevent.times` - a list of vectors where the vectors contain the imputed event times for the subjects (these vectors do not contain the observed event times before subject drop out). If a subject has no imputed events then the vector `numeric(0)` is returned.
`parameters`	A list of named parameters describing the method (used for printing) - or NULL if none

Value

A ImputeMechanism.object

Create `Scenario` object from list of Fit Summaries

Description

Create Scenario object from list of Fit Summaries

Usage

CreateScenario(object, description = "")
CreateScenario(object, description = "")

Arguments

`object`	Either a list of `summary.SingleSimFit` or `summary.ImputeSimFit` objects
`description`	A character string describing the scenario (used for printing)

Value

A Scenario object

Data frame of covariates for simulating recurrent events

Description

This object allows covariates to be included in the simulation procedure The object is created using the MakeDejaData function

Arguments

`data`	A data frame containing the subject
`arm`	character the column name of the treatment arm for each subject
`rate`	character the column name of the rate to be used when simulating
`Id`	character the column name of subject Id

Structure

The above components must be included in a DejaData Object

DropoutMechanism object

Description

An object which defines a specific mechanism which takes a complete SingleSim object and returns a set of drop out times for subjects

Arguments

`type`	The type of mechanism (e.g. "MCAR" or "MNAR")
`text`	A short string describing the mechanism (only used for printing)
`cols.needed`	Which columns in the SingleSim$data data frame must be included for this drop out mechanism to work. This option could allow drop out mechanism which depend on covariates to be included.
`GetDropTime`	A function with two arguments event.times and data, the corresponding entries from the SingleSim object. This function should return a list of dropout times (if a subject does not dropout its dropout time should be their current censored.time (i.e. the study follow up time))
`parameters`	A list of named parameters for the mechanism (only used for printing) or NULL if none

Details

It is possible to create user defined mechanisms, however, certain common mechanisms have already been implemented. For example see ConstantRateDrop and LinearRateChangeDrop

Only the GetDropTime and cols.needed entries are required for calculation, the other entries are used for printing the object

print.DropoutMechanism methods is defined.

Structure

The following components must be included in a DropoutMechanism Object

Expand event counts into a list of event times

Description

This function exists to allow clinical trial data which typically gives event counts over time to be plugged into this software, which relies on actual event counts.

Usage

expandEventCount(count, time)
expandEventCount(count, time)

Arguments

`count`	a vector of event counts. All entries must be non-negative.
`time`	a matching (strictly positive) vector of followup times.

Details

This function always produces a warning: anyone relying on this function to actually analyze data should take great care.

Value

a list of vectors of event times

Examples

expandEventCount(count=c(0, 20), time=c(10, 20))
expandEventCount(count=c(0, 20), time=c(10, 20))

Extract the results of running a scenario

Description

This function is a wrapper around CreateScenario See the user guide vignette for an example of using this function

Usage

extract_results(answer, name, description)
extract_results(answer, name, description)

Arguments

`answer`	A named list of lists
`name`	The name of the lists of answer which should be extracted and put together into a `sc`
`description`	The description parameter to be passed into the `CreateScenario` function

Value

A Scenario object

Output a single imputed data set

Description

Output a single imputed data set

Usage

GetImputedDataSet(imputeSim, index)
GetImputedDataSet(imputeSim, index)

Arguments

`imputeSim`	A `ImputeSim` object which contains multiple imputed data sets
`index`	numeric, which of the multiple imputed data sets to output

Value

A SingleSim object with status="imputed"

Examples

sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
sim.with.MCAR.dropout <- SimulateDropout(sim,
  drop.mechanism = ConstantRateDrop(rate = 0.0025))
fit <- Simfit(sim.with.MCAR.dropout)
imps <- Impute(fit, copy_reference(), 10)
imp1 <- GetImputedDataSet(imps, 1)

sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
sim.with.MCAR.dropout <- SimulateDropout(sim,
  drop.mechanism = ConstantRateDrop(rate = 0.0025))
fit <- Simfit(sim.with.MCAR.dropout)
imps <- Impute(fit, copy_reference(), 10)
imp1 <- GetImputedDataSet(imps, 1)

Import an existing data frame for use with the package

Description

Import an existing data frame for use with the package

Usage

ImportSim(
  dejaData,
  event.times,
  status,
  study.time,
  censored.time = NULL,
  actual.events = NULL,
  allow.beyond.study = FALSE
)
ImportSim(
  dejaData,
  event.times,
  status,
  study.time,
  censored.time = NULL,
  actual.events = NULL,
  allow.beyond.study = FALSE
)

Arguments

`dejaData`	a `DejaData` object contain the subject covariates and treatment arm
`event.times`	A list of vectors, containing the observed event times of each subject. If no events are observed then numeric(0) should be used. See example in this help file for more details
`status`	The status of the data set imported, either "complete" (if all subjects complete their follow up period) or "dropout" (if not)
`study.time`	The total follow up time according to study protocol
`censored.time`	If status is "dropout", this is a vector of the times at which each subject is censored
`actual.events`	If status is "dropout" and the total number of events (i.e. not just the number observed) is known (e.g. if a different simulation procedure was used) a vector of total number of events should be included. If the number is not known or status is "complete" then this should be set to NULL
`allow.beyond.study`	Whether or not to allow imported data with events after the nominal end of study.

Value

A SingleSim object

Examples


covar.df <- data.frame(Id=1:6,
                       arm=c(rep(0,3),rep(1,3)),
                       Z=c(0,1,1,0,1,0))
 
dejaData <- MakeDejaData(covar.df,arm="arm",Id="Id") 


event.times <- list(c(25,100,121,200,225),
                    c(100,110),c(55),numeric(0),
                    150,45)     

complete.dataset <- ImportSim(dejaData, event.times,
                    status="complete",
                    study.time=365)
 
censored.time  <- c(365,178,100,245,200,100)

dropout.dataset <- ImportSim(dejaData, event.times,
                    status="dropout",
                    study.time=365,
                    censored.time=censored.time)

covar.df <- data.frame(Id=1:6,
                       arm=c(rep(0,3),rep(1,3)),
                       Z=c(0,1,1,0,1,0))
 
dejaData <- MakeDejaData(covar.df,arm="arm",Id="Id") 


event.times <- list(c(25,100,121,200,225),
                    c(100,110),c(55),numeric(0),
                    150,45)     

complete.dataset <- ImportSim(dejaData, event.times,
                    status="complete",
                    study.time=365)
 
censored.time  <- c(365,178,100,245,200,100)

dropout.dataset <- ImportSim(dejaData, event.times,
                    status="dropout",
                    study.time=365,
                    censored.time=censored.time)

Produce imputed data sets

Description

Given a SingleSimFit object (with impute.parameters not NULL) and an imputation mechanism, create a collection of imputed data sets

Usage

Impute(fit, impute.mechanism, N)
Impute(fit, impute.mechanism, N)

Arguments

`fit`	A `SingleSimFit` object
`impute.mechanism`	An `ImputeMechanism` object
`N`	The number of data sets to impute

Value

An ImputeSim object

Examples

sim <- SimulateComplete(study.time=365,number.subjects=50,
    event.rates=c(0.01,0.005),dispersions=0.25)
sim.with.MCAR.dropout <- SimulateDropout(sim,
    drop.mechanism = ConstantRateDrop(rate = 0.0025))
fit <- Simfit(sim.with.MCAR.dropout)
imps <- Impute(fit, copy_reference(), 10)

sim <- SimulateComplete(study.time=365,number.subjects=50,
    event.rates=c(0.01,0.005),dispersions=0.25)
sim.with.MCAR.dropout <- SimulateDropout(sim,
    drop.mechanism = ConstantRateDrop(rate = 0.0025))
fit <- Simfit(sim.with.MCAR.dropout)
imps <- Impute(fit, copy_reference(), 10)

ImputeMechanism object

Description

An object which defines a mechanism for taking a SingleSimFit object and imputing missing data to create a ImputeSim

Arguments

`name`	The method name (used for printing)
`cols.needed`	which columns of the SingleSim data frame are required by the method, typically `c("censored.time","observed.events","arm")`
`impute`	A function which takes a `SingleSimFit` object and outputs the details for a single imputed data set, specifically a list with two elements: `new.censored.times` - a vector of times subjects were censored (after taking into account imputation) and `newevent.times` - a list of vectors where the vectors contain the imputed event times for the subjects (these vectors do not contain the observed event times before subject drop out). If a subject has no imputed events then the vector `numeric(0)` is returned.
`parameters`	A list of named parameters describing the method (used for printing) - or NULL if none

Details

It is possible to create user defined mechanisms, however, common mechanisms have already been implemented. For example see weighted_j2r

A print.ImputeMechanism method is defined.

Structure

The following components must be included in an ImputeMechanism Object

Examples

j2r <- weighted_j2r(trt.weight=0)
j2r <- weighted_j2r(trt.weight=0)

ImputeSim object

Description

This object contains a collection of imputed data sets derived from a SingleSimFit object and ImputeMechanism

Arguments

`singleSim`	The `SingleSim` object from which the imputed data sets have been derived
`impute.mechanism`	The `ImputeMechanism` object used to perform the imputation
`imputed.values`	A matrix with 1 column per imputed data set and two rows: newevent.times a list of vectors containing the imputed event times (not including the events which were observed) and new.censored.times - a vector containing the times at which subjects (with imputed data) are now censored
`dropout`	A vector containing the number of subjects who have dropped out in each arm, for whom data is to be imputed Use `GetImputedDataSet` to extract a single imputed data set and use `Simfit` to fit a model to the set of data sets

ImputeSimFit object

Description

An object which contains both a set of imputed data sets (ImputeSim object) and a set of models fitted to them

Arguments

`imputeSim`	The `ImputeSim` object for which models have been fitted
`summaries`	A list of `summary.SingleSimFit` objects containing the model fits for each of the imputed data sets

Details

Calling summary.ImputeSimFit will apply Rubin's formula to calculate estimates for the treatment effect and standard error

Functions summary.ImputeSimFit and as.data.frame.ImputeSimFit have been implemented

Create a Dropout Mechanism with drop out rate which changes by a fixed constant after every event

Description

Creates an MAR DropoutMechanism object where subject i has piecewise exponential dropout rate where the rate changes by a constant amount after each event, specifically after j events the subject has rate Rij = Cj*exp(Xij) where Cj=C+j*D for constants C, D and Xij is a standard normal variable with mean 0 and standard deviation sigma

Usage

LinearRateChangeDrop(starting.rate, rate.change, var = 0)
LinearRateChangeDrop(starting.rate, rate.change, var = 0)

Arguments

`starting.rate`	`C`, see description section.
`rate.change`	`D`, see description section. Note if `D<0`, `Cj` could be negative for large `j`, this is not possible and the rate remains constant if the next change would set `Cj<=0`
`var`	`sigma^2`, see description section

Value

A DropoutMechanism object

Examples

LinearRateChangeDrop(starting.rate=0.0025,rate.change=0.0005)
LinearRateChangeDrop(starting.rate=0.0025,rate.change=-0.00001,var=1)
LinearRateChangeDrop(starting.rate=0.0025,rate.change=0.0005)
LinearRateChangeDrop(starting.rate=0.0025,rate.change=-0.00001,var=1)

Create a `DejaData` object

Description

This object is can be used to create a SingleSim object with subject specific rates

Usage

MakeDejaData(data, arm, Id, rate = NULL)
MakeDejaData(data, arm, Id, rate = NULL)

Arguments

`data`	A data frame containing the subject
`arm`	character the column name of the treatment arm for each subject
`Id`	character the column name of subject Id
`rate`	character the column name of the rate to be used when simulating (or NULL, if using DejaData to import a data set, see `ImportSim`)

Value

A DejaData object

Examples


set.seed(232)

my.df <- data.frame(Id=1:100,
                    arm=c(rep(0,50),rep(1,50)),
                    covar=rbinom(n=100,size=1,prob=0.5))

my.df$rate <- 0.0025 + my.df$covar*0.002 + (1-my.df$arm)*0.002

my.dejaData <- MakeDejaData(my.df,arm="arm",rate="rate",Id="Id")

set.seed(232)

my.df <- data.frame(Id=1:100,
                    arm=c(rep(0,50),rep(1,50)),
                    covar=rbinom(n=100,size=1,prob=0.5))

my.df$rate <- 0.0025 + my.df$covar*0.002 + (1-my.df$arm)*0.002

my.dejaData <- MakeDejaData(my.df,arm="arm",rate="rate",Id="Id")

S3 generic to output the number of subjects in a given object

Description

S3 generic to output the number of subjects in a given object

Usage

numberSubjects(x)
numberSubjects(x)

Arguments

`x`	The object

Value

The number of subjects

Examples

sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
numberSubjects(sim)
sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
numberSubjects(sim)

Scenario object

Description

This class contains a collection of model fit summaries and summarizing this object will calculate overall summary statistics such as power/type I error

Arguments

`description`	A string containing a description of the scenario
`summaries`	A list of either `summary.ImputeSimFit` or `summary.SingleSimFit` objects

Details

Functions as.data.frame.Scenario and summary.Scenario have been implemented

Simulated recurrent event data.

Description

A simulated dataset containing a randomised treatment group, follow-up time, and number of events, for 500 patients. The planned follow-up period for the study was 1 year, but some patients dropped out early and so their follow-up ended prematurely (i.e. before 1 year)

Usage

simData
simData

Format

A data frame with 500 rows and 3 variables:

z: a binary variable indicating randomised treatment group
y: number of events observed during patient's follow-up
fupTime: the time in years the patient was followed up for

...

Source

Simulated data

S3 generic for fitting models

Description

S3 generic for fitting models

Usage

Simfit(x, family = "negbin", equal.dispersion = TRUE, covar = NULL, ...)
Simfit(x, family = "negbin", equal.dispersion = TRUE, covar = NULL, ...)

Arguments

`x`	The S3 object
`family`	Either "negbin" for fitting a negative binomial model (using `MASS::glm.nb`), "poisson" for fitting a poisson model (`glm`) or "quasipoisson" for fitting a quasipoisson model `glm`
`equal.dispersion`	logical, should the arms have the same dispersion parameter when fitting negative binomial models
`covar`	A formula containing the additional covariates to be used when calling `glm.nb` if no covariates are included in the model this should be NULL, for example `~covar1 + covar2` See vignette for further details
`...`	Additional arguments to be passed to `glm` or `glm.nb`

Value

A SingleSimFit object

Examples

 
set.seed(1234)
sim <- SimulateComplete(study.time=1,number.subjects=50,
      event.rates=c(0.1,0.05),dispersions=0.1)
summary(Simfit(sim,equal.dispersion=TRUE))


set.seed(1234)
sim <- SimulateComplete(study.time=1,number.subjects=50,
      event.rates=c(0.1,0.05),dispersions=0.1)
summary(Simfit(sim,equal.dispersion=TRUE))

Simulate a complete data set

Description

Simulate a complete data set of a recurrent event clinical trial without dropouts using a negative binomial model with given rates and dispersion parameters

Usage

SimulateComplete(
  study.time,
  dejaData = NULL,
  number.subjects = NULL,
  event.rates = NULL,
  dispersions
)
SimulateComplete(
  study.time,
  dejaData = NULL,
  number.subjects = NULL,
  event.rates = NULL,
  dispersions
)

Arguments

`study.time`	The study follow up period
`dejaData`	If not NULL this should contain a `DejaData` object. If this is used then number.subjects and event.rates arguments are ignored
`number.subjects`	The number of subjects, if a vector `c(a,b)` then `a` subjects on the control arm and `b` subjects on the active arm. If `number.subjects` is a single number then both arms have the given number of subjects.
`event.rates`	The rate parameter(s) for the negative binomial model (if single parameter then it is used for both arms)
`dispersions`	The dispersion parameter(s) for the negative binomial model (if single parameter then it is used for both arms)

Details

Each subject's events are described by a Poisson process with a subject specific rate given by lambda/study.time where study.time is the study follow up period and lambda has a gamma distribution with shape=1/dispersion and scale=dispersion*event.rate*study.time

Different dispersions, event.rates and number of subjects can be specified for both arms of the trial

Value

A SingleSim object with status='complete'

Examples

sim <- SimulateComplete(study.time=365,number.subjects=50,
                        event.rates=c(0.01,0.005),dispersions=0.25)
sim2 <- SimulateComplete(study.time=365,number.subjects=c(50,75),
                        event.rates=c(0.01,0.005),dispersions=c(0,0.25))
sim <- SimulateComplete(study.time=365,number.subjects=50,
                        event.rates=c(0.01,0.005),dispersions=0.25)
sim2 <- SimulateComplete(study.time=365,number.subjects=c(50,75),
                        event.rates=c(0.01,0.005),dispersions=c(0,0.25))

Simulate subject dropout

Description

This function takes a complete recurrent event data set and drop out mechanism and creates a data set set with dropout

Usage

SimulateDropout(simComplete, drop.mechanism)
SimulateDropout(simComplete, drop.mechanism)

Arguments

`simComplete`	A `SingleSim` object (with `status="complete"`)
`drop.mechanism`	A `DropoutMechanism` object

Value

A SingleSim object with status='dropout'

Examples

sim <- SimulateComplete(study.time=365,number.subjects=50,
                        event.rates=c(0.01,0.005),dispersions=0.25)
                        
sim.with.MCAR.dropout <- SimulateDropout(sim,
                      drop.mechanism = ConstantRateDrop(rate = 0.0025)) 
sim.with.MAR.dropout <- SimulateDropout(sim, 
                     drop.mechanism = LinearRateChangeDrop(
                                          starting.rate = 0.0025,
                                          rate.change = 0.0005))

sim <- SimulateComplete(study.time=365,number.subjects=50,
                        event.rates=c(0.01,0.005),dispersions=0.25)
                        
sim.with.MCAR.dropout <- SimulateDropout(sim,
                      drop.mechanism = ConstantRateDrop(rate = 0.0025)) 
sim.with.MAR.dropout <- SimulateDropout(sim, 
                     drop.mechanism = LinearRateChangeDrop(
                                          starting.rate = 0.0025,
                                          rate.change = 0.0005))

SingleSim Object

Description

A class containing the data for a single simulation. Depending on the value of status, this may be a complete data set, a set including subject dropouts or a data set after multiple imputation

print.SingleSim and summary.SingleSim methods are defined.

Arguments

`data`	The data frame, one row per subject containing (at least) the following columns Id, arm, censored.time, observed.events and actual.events
`event.times`	A list of event times. event.times[[1]] is a list of event times for subject with Id 1 The length of event.times[[1]] = the number of observed events of subject with Id 1
`status`	Either "complete", "dropout" or "imputed" denoting the status of the data set.
`subject.rates`	A vector of the specific rates used for the Poisson process for subjects when generating the data
`dropout.mechanism`	If status is not "complete" then this contains the `DropoutMechanism` object used to perform the subject dropout. See `DropoutMechanism.object`.
`impute.mechanism`	If the status is "imputed" then this contains the `ImputeMechanism` object used to perform the imputation. See `ImputeMechanism.object`
`study.time`	The study follow up period (see `SimulateComplete`)
`event.rates`	The control/active event rates (see `SimulateComplete`), if data set was generated without using these (e.g. the dejaData argument was used) then this is set to NULL
`dispersions`	The control/active dispersion rates (see `SimulateComplete`)

Structure

The above components must be included in a SingleSim Object

SingleSimFit object

Description

A SingleSimFit object is returned from calling Simfit with a SingleSim object. It can be used to both impute data sets or can be summarized

Arguments

`singleSim`	The `SingleSim` object to which a model has been fitted
`model`	The model which has been fitted
`genCoeff.function`	A function which returns a list of parameters from the model fit(s) which can be used when performing the gamma imputation. It takes one argument, use.uncertainty (by default is TRUE) which if TRUE stochastically incorporates uncertainty into the parameter estimates in preparation for use with imputation If a Poisson/quasi-Poisson model was fitted to the `SingleSimFit` object then this will be NULL
`equal`	dispersion whether equal dispersions were used when fitting model(s) to the data

Details

A summary.SingleSimFit method has been implemented

S3 generic to output the number of subjects in each arm for a given object

Description

S3 generic to output the number of subjects in each arm for a given object

Usage

subjectsPerArm(x)
subjectsPerArm(x)

Arguments

`x`	The object

Value

A vector of the number of subjects in each arm

Examples

sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
subjectsPerArm(sim)
sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
subjectsPerArm(sim)

summary.ImputeSimFit object

Description

The summary of a ImputeSimFit object. Rubin's formula is used to combine the test statistics into a single summary

Arguments

`treatment.effect`	The mean of the estimated treatment.effect from the imputed data
`se`	The standard error of the (log) treatment effect calculated using Rubin's formula
`df`	The number of degrees of freedom used to calculate the p-value
`adjusted.df`	The number of degrees of freedom used to calculate the adjusted p-value (this should be used if the complete data number of degrees of freedom is small)
`dispersion`	The mean of the estimated dispersion parameter
`pval`	The p-value for the test log(treatment.effect)=0 using Rubin's formula
`adjusted.pval`	The p-value for the test log(treatment.effect)=0 using Rubin's formula and the adjusted number of degrees of freedom
`dropout`	The number of subjects who drop out (per arm) for this imputed data set
`number.subjects`	The number of subjects (per arm) for this imputed data set

Details

A print.summary.ImputeSimFit object has been implemented

summary.Scenario object

Description

This object contains the overall summary statistics for a specific scenario. It is envisioned that multiple scenarios are run and a set of summary.Scenario objects are created and these can then be used for plotting

Arguments

`treatment.effect`	The exp(mean(log(individual treatment effects))),
`se`	The mean standard error of the (log) treatment effect
`power`	The proportion of simulations for which the p-value is `< alpha`
`alpha`	The significance level used when calculating power, by default 0.05 use `summary(object,alpha=x)` to use a different p value
`use.adjusted.pval`	logical, default FALSE should the p values calculated using Rubin's formula with the adjusted number of degrees of freedom be used. Use `summary(object,use.adjusted.pval=TRUE)`, to use the adjusted p values
`description`	A string containing a description of the scenario
`dropout`	A list of summary statistics regarding number of subject dropouts

Details

A print.summary.Scenario function has been implemented

summary.SingleSim object

Description

The object returned when calling the summary function on a SingleSim object

Arguments

`status`	The status of the SingleSim object
`study.time`	The study.time from the SingleSim object
`number.subjects`	The number of subjects on each arm
`number.dropouts`	The number of subjects who dropout on each arm
`total.events`	The total number of events for each arm
`time.at.risk`	The total time at risk for each arm
`empirical.rates`	total.events/time.at.risk The `print.summary.SingleSim` method has been implemented

summary.SingleSimFit

Description

The summary object for a SingleSimFit object

Arguments

`model.summary`	The model summary from the fit
`treatment.effect`	The estimate of treatment effect from the model fit
`CI.limit`	The confidence interval limit (by default 0.95), call `summary(object,CI.limit=x)` to use CI of `x` instead.
`CI`	The confidence interval of the treatment effect
`se`	Estimate for the standard error of (log) treatment effect
`dispersion`	Estimate for the dispersion parameter or numeric(0) if Poisson/quasi-Poisson model used
`rate.estimate`	Estimate of the event rates from the model a vector c(control arm, treatment arm)
`pval`	The p value directly from the model fit (this is for the single model fit only, i.e. not using Rubin's formula)
`datastatus`	The status of SingleSim object to which the fit was applied
`df`	The number of degrees of freedom of the model
`dropout`	The number of dropouts of each arm
`number.subjects`	The number of subjects in each arm

Details

A print.summary.SingleSimFit method has been implemented

Examples

sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
fit <- Simfit(sim)
summary(fit)
sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
fit <- Simfit(sim)
summary(fit)

Create a weighted_j2r `ImputeMechanism` object

Description

Missing counts for a subject in the active treatment arm will be imputed according to a point (determined by trt.weight) between the means of the placebo and treatment arms, conditioned on the number of events. Missing counts for subjects in the placebo arm will be imputed according to the mean of the placebo arm, conditioned on the subject's observed number of events.

Usage

weighted_j2r(trt.weight, delta = c(1, 1), proper = TRUE)
weighted_j2r(trt.weight, delta = c(1, 1), proper = TRUE)

Arguments

`trt.weight`	See details
`delta`	If `trt.weight=1` then delta is a vector of length 2 (control.delta,treatment.delta) and the mean number of expected events for the imputed missing data is multiplied by the appropriate delta
`proper`	If `proper=TRUE` then proper imputation is performed, in which each imputation is created based on parameters values drawn from the (approximate) posterior distribution of the imputation model. If `proper=FALSE`, improper imputation is performed. This means all imputed datasets are generated conditional on the maximum likelihood estimates of the parameters.

Details

If trt.weight = 0 then imputation using this mechanism will follow the jump to reference (j2r) model whereby missing counts for subjects in both arms will be imputed according to the mean of the placebo arm conditioned on the subject's observed number of events

If trt.weight = 1 then imputation using this mechanism will follow the MAR model whereby missing counts for subjects in each arm will be imputed according to the event rate of subjects in its treatment group conditioned on the subject's observed number of events

See the User guide vignette for further details

Value

An ImputeMechanism object

Examples

sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
sim.with.MCAR.dropout <- SimulateDropout(sim,
                     drop.mechanism = ConstantRateDrop(rate = 0.0025))
fit <- Simfit(sim.with.MCAR.dropout)
imps <- Impute(fit, weighted_j2r(trt.weight=0), 10)

sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
sim.with.MCAR.dropout <- SimulateDropout(sim,
                     drop.mechanism = ConstantRateDrop(rate = 0.0025))
fit <- Simfit(sim.with.MCAR.dropout)
imps <- Impute(fit, weighted_j2r(trt.weight=0), 10)

Package 'dejaVu'

Help Index

Create a Dropout Mechanism with constant dropout rate

Description

Usage

Arguments

Value

See Also

Examples

Create a copy reference ImputeMechanism object

Description

Usage

Arguments

Value

See Also

Examples

A function which creates a DropOut Mechanism object

Description

Usage

Arguments

Value

See Also

A function which creates an Impute Mechanism object

Description

Usage

Arguments

Value

See Also

Create Scenario object from list of Fit Summaries

Description

Usage

Arguments

Value

See Also

Data frame of covariates for simulating recurrent events

Description

Arguments

Structure

DropoutMechanism object

Description

Arguments

Details

Structure

Expand event counts into a list of event times

Description

Usage

Arguments

Details

Value

Examples

Extract the results of running a scenario

Description

Usage

Arguments

Value

See Also

Output a single imputed data set

Description

Usage

Arguments

Value

See Also

Examples

Import an existing data frame for use with the package

Description

Usage

Arguments

Value

Examples

Produce imputed data sets

Description

Usage

Arguments

Value

Examples

ImputeMechanism object

Description

Arguments

Details

Structure

Create a copy reference `ImputeMechanism` object

Create `Scenario` object from list of Fit Summaries

Create a `DejaData` object