Package 'InformativeCensoring' reference manual

Title:	Multiple Imputation for Informative Censoring
Description:	Multiple Imputation for Informative Censoring. This package implements two methods. Gamma Imputation described in <DOI:10.1002/sim.6274> and Risk Score Imputation described in <DOI:10.1002/sim.3480>.
Authors:	David Ruau [aut], Nikolas Burkoff [aut], Jonathan Bartlett [aut, cre], Dan Jackson [aut], Edmund Jones [aut], Martin Law [aut], Paul Metcalfe [aut]
Maintainer:	Jonathan Bartlett <[email protected]>
License:	GPL (>= 2) \| file LICENSE
Version:	0.3.6
Built:	2024-10-27 03:50:39 UTC
Source:	https://github.com/jwb133/informativecensoring

Perform methods of multiple imputation for time to event data

Description

Perform methods of multiple imputation for time to event data

Details

See Nonparametric comparison of two survival functions with dependent censoring via nonparametric multiple imputation. Hsu and Taylor Statistics in Medicine (2009) 28:462-475 for Hsu's method

See Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation. Jackson et al., Statistics in Medicine (2014) 33:4681-4694 for Jackson's method

Author(s)

[email protected]

Specify the columns of the data frame required by score imputation method

Description

Specify the columns of the data frame required by score imputation method

Usage

col.headings(arm, has.event, time, Id, DCO.time, to.impute, censor.type = NULL)
col.headings(arm, has.event, time, Id, DCO.time, to.impute, censor.type = NULL)

Arguments

`arm`	column name which will contain the subject's treatment group
`has.event`	column name which will contain whether the subject has an event (1) or not(0)
`time`	column name of censoring/event time
`Id`	column name of subject Id
`DCO.time`	column name of the time at which the subject would have been censored had they not had an event before data cut off
`to.impute`	column name of the logical column as to whether events should be imputed
`censor.type`	column name of the column containing the reason for censoring, 0=had event, 1=non-administrative censoring 2=administrative censoring – only subjects with 1 in this column count as having an ‘event’ in the Cox model for censoring (optionally used – if not used then all subjects who are censored are used)

Value

A list contain the given arguments

Test Cox proportional hazards assumption

Description

See cox.zph function in the survival package

Usage

cox.zph(fit, transform = "km", global = TRUE, ...)
cox.zph(fit, transform = "km", global = TRUE, ...)

Arguments

`fit`	the result of fitting a Cox regression model, using the `coxph` or `coxme` functions.
`transform`	a character string specifying how the survival times should be transformed before the test is performed. Possible values are `"km"`, `"rank"`, `"identity"` or a function of one argument.
`global`	should a global chi-square test be done, in addition to the per-variable or per-term tests tests.
`...`	Additional arguments to cox.zph, for example `index` if fit is a `GammaStatList` object

Extract a single risk score/gamma imputed data set/model fit

Description

Extract a single risk score/gamma imputed data set/model fit

Usage

## S3 method for class 'GammaImputedSet'
ExtractSingle(x, index)

## S3 method for class 'GammaStatList'
ExtractSingle(x, index)

ExtractSingle(x, index)

## S3 method for class 'ScoreImputedSet'
ExtractSingle(x, index)

## S3 method for class 'ScoreStatList'
ExtractSingle(x, index)
## S3 method for class 'GammaImputedSet'
ExtractSingle(x, index)

## S3 method for class 'GammaStatList'
ExtractSingle(x, index)

ExtractSingle(x, index)

## S3 method for class 'ScoreImputedSet'
ExtractSingle(x, index)

## S3 method for class 'ScoreStatList'
ExtractSingle(x, index)

Arguments

`x`	The multiple imputed object
`index`	Integer, which imputed data set/model fit should be returned

Value

The individual data set/model fit

Perform gamma-Imputation for a given data set

Description

This function performs the Imputation described in Relaxing the independent censoring assumptions in the Cox proportional hazards model using multiple imputation. (2014) D. Jackson et al. Statist. Med. (33) 4681-4694

Usage

gammaImpute(
  formula,
  data,
  m,
  gamma,
  gamma.factor,
  bootstrap.strata = rep(1, nrow(data)),
  DCO.time,
  ...,
  parallel = c("no", "multicore", "snow")[1],
  ncpus = 1L,
  cl = NULL
)
gammaImpute(
  formula,
  data,
  m,
  gamma,
  gamma.factor,
  bootstrap.strata = rep(1, nrow(data)),
  DCO.time,
  ...,
  parallel = c("no", "multicore", "snow")[1],
  ncpus = 1L,
  cl = NULL
)

Arguments

`formula`	The model formula to be used when fitting the models to calculate the cumulative hazard. A formula for coxph can include strata terms but not cluster or tt and only right-censored `Surv` objects can be used. Note the function does not allow multiple strata to be written as `strata(W1)+strata(W2)`, use `strata(W1,W2)` instead
`data`	A time to event data set for which event times are to be imputed
`m`	The number of imputations to be created
`gamma`	Either column name containing the value of gamma or a vector of values giving the subject specific size of the step change in the log hazard at censoring. If a subject has NA in this column then no imputation is performed for this subject (i.e. the subject's censored time remains unchanged after imputation). If a subject has already had an event then the value of gamma is ignored. If `gamma.factor` is also used then the subject specific gamma are all multipled by `gamma.factor`. At least one of `gamma` and `gamma.factor` must be included.
`gamma.factor`	If used, a single numeric value. If no `gamma` then the step change in log hazard for all subjects at censoring is given by `gamma.factor`. If `gamma` is used then for each subject, the step change in log hazard is given by `gamma.factor` multiplied by the subject specific gamma. At least one of `gamma` and `gamma.factor` must be included.
`bootstrap.strata`	The strata argument for stratified bootstrap sampling, see argument `strata` for the function `boot::boot` for further details. If argument is not used then standard sampling with replacement will be used
`DCO.time`	Either column name containing the subject's data cutoff time or a vector of DCO.times for the subjects or a single number to be used as the DCO.time for all subjects (if imputed events are > this DCO.time then subjects are censored at DCO.time in imputed data sets)
`...`	Additional parameters to be passed into the model fit function
`parallel`	The type of parallel operation to be used (if any).
`ncpus`	integer: number of processes to be used in parallel operation: typically one would chose this to be the number of available CPUs
`cl`	An optional parallel or snow cluster for use if `parallel="snow"`. If not supplied, a cluster on the local machine is created for the duration of the call.

Details

See the Gamma Imputation vignette for further details

Value

A GammaImputedSet.object containing the imputed data sets

Examples


## Not run: 
data(nwtco)
nwtco <- nwtco[1:500,]

#creating 2 imputed data sets (m=2) for speed, would normally create more
ans <- gammaImpute(formula=Surv(edrel,rel)~histol + instit,
                   data = nwtco, m=2, gamma.factor=1, DCO.time=6209)

#subject specific gamma (multiplied by gamma.factor to give the jump)
#NA for subjects that are not to be imputed
jumps <- c(rep(NA,10),rep(1,490))
DCO.values <- rep(6209,500)

ans.2 <- gammaImpute(formula=Surv(edrel,rel)~histol + instit + strata(stage),
                   data = nwtco, m=2, bootstrap.strata=strata(nwtco$stage),
                   gamma=jumps, gamma.factor=1, DCO.time=DCO.values)

#can also use column names
nwtco$gamma <- jumps
nwtco$DCO.time <- DCO.values
ans.3 <- gammaImpute(formula=Surv(edrel,rel)~histol + instit + strata(stage),
                   data = nwtco, m=2, bootstrap.strata=strata(nwtco$stage),
                   gamma="gamma", DCO.time="DCO.time")

## End(Not run)

## Not run: 
data(nwtco)
nwtco <- nwtco[1:500,]

#creating 2 imputed data sets (m=2) for speed, would normally create more
ans <- gammaImpute(formula=Surv(edrel,rel)~histol + instit,
                   data = nwtco, m=2, gamma.factor=1, DCO.time=6209)

#subject specific gamma (multiplied by gamma.factor to give the jump)
#NA for subjects that are not to be imputed
jumps <- c(rep(NA,10),rep(1,490))
DCO.values <- rep(6209,500)

ans.2 <- gammaImpute(formula=Surv(edrel,rel)~histol + instit + strata(stage),
                   data = nwtco, m=2, bootstrap.strata=strata(nwtco$stage),
                   gamma=jumps, gamma.factor=1, DCO.time=DCO.values)

#can also use column names
nwtco$gamma <- jumps
nwtco$DCO.time <- DCO.values
ans.3 <- gammaImpute(formula=Surv(edrel,rel)~histol + instit + strata(stage),
                   data = nwtco, m=2, bootstrap.strata=strata(nwtco$stage),
                   gamma="gamma", DCO.time="DCO.time")

## End(Not run)

`GammaImputedData` object

Description

An object which contains

Slots

data: A data frame containing the time to event data with 3 new columns impute.time and impute.event, the imputed event/censoring times and event indicators (for subjects whose data is not imputed these columns contain the unchanged event/censoring time and event indicator) and internal_gamma_val which is the value of gamma used for each subject in this data set
default.formula: The default model formula which will be used when fitting the imputed data

`GammaImputedSet` object

Description

An object which contains the set of gamma imputed data frames. Use the ExtractSingle function to extract a single GammaImputedData objects. Use the ImputeStat function to fit models to the entire set of imputed data frames

Details

It contains the following:

Slots

data: A data frame containing the unimputed time to event data (along with a column internal_gamma_val which is the value of gamma used for each subject in this data set)
m: The number of imputed data sets
gamma.factor: The value of gamma.factor used with the imputation
impute.time: A matrix (1 column per imputed data set) containing the imputed times
impute.event: A matrix (1 column per imputed data set) containing the imputed event indicators
default.formula: The default model formula which will be used when fitting the imputed data

`GammaStat` object

Description

An S3 object which contains the point estimate and test statistic after fitting a model to a GammaImputedData object.

Details

The function print.GammaStat has been implemented

The object contains the following:

Slots

model: The model used to create the fit
method: The model used for the fit
estimate: A point estimate of the test statistic
var: The estimate for the variance of the test statistic

`GammaStatList` object

Description

The object containing the results of fitting models to a GammaImputedSet object.

Details

A summary.GammaStatList has been implemented which performs Rubin's multiple imputation rules.

The object contains the following

Slots

fits: A list of GammaStat objects containing the model fits for the imputed data sets
statistics: A list with two elements: estimates and vars which contain the coefficient estimates and their variances one column per covariate one row per imputed data set
m: The number of model fits

S3 generic to fit model(s) to risk score/gamma Imputed objects

Description

S3 generic to fit model(s) to risk score/gamma Imputed objects

Usage

## S3 method for class 'GammaImputedData'
ImputeStat(
  object,
  method = c("Cox", "weibull", "exponential")[1],
  formula = NULL,
  ...
)

## S3 method for class 'GammaImputedSet'
ImputeStat(
  object,
  method = c("Cox", "weibull", "exponential")[1],
  formula = NULL,
  ...,
  parallel = c("no", "multicore", "snow")[1],
  ncpus = 1L,
  cl = NULL
)

ImputeStat(
  object,
  method = c("logrank", "Wilcoxon", "Cox", "weibull", "exponential")[1],
  formula,
  ...
)

## S3 method for class 'ScoreImputedSet'
ImputeStat(
  object,
  method = c("logrank", "Wilcoxon", "Cox")[1],
  formula = NULL,
  ...,
  parallel = c("no", "multicore", "snow")[1],
  ncpus = 1L,
  cl = NULL
)
## S3 method for class 'GammaImputedData'
ImputeStat(
  object,
  method = c("Cox", "weibull", "exponential")[1],
  formula = NULL,
  ...
)

## S3 method for class 'GammaImputedSet'
ImputeStat(
  object,
  method = c("Cox", "weibull", "exponential")[1],
  formula = NULL,
  ...,
  parallel = c("no", "multicore", "snow")[1],
  ncpus = 1L,
  cl = NULL
)

ImputeStat(
  object,
  method = c("logrank", "Wilcoxon", "Cox", "weibull", "exponential")[1],
  formula,
  ...
)

## S3 method for class 'ScoreImputedSet'
ImputeStat(
  object,
  method = c("logrank", "Wilcoxon", "Cox")[1],
  formula = NULL,
  ...,
  parallel = c("no", "multicore", "snow")[1],
  ncpus = 1L,
  cl = NULL
)

Arguments

`object`	A `ScoreImputedData`, `ScoreImputedSet`, `GammaImputedData` or `GammaImputedSet` object to fit the model to
`method`	The type of statistical model to fit. There are three methods which can be performed when using Risk Score imputation "logrank": a logrank test using `survival::survdiff` "Wilcoxon": Peto & Peto modification of the Gehan-Wilcoxon test using `survival::survdiff` with `rho=1` "Cox": Fit a cox model using `survival::coxph` For gamma imputation the model can be "Cox" (using `survival::coxph`), "weibull" or "exponential" both using `survival::coxph`
`formula`	The model formula to fit. If no formula argument is used, then object$default.formula will be used. For risk score imputation this is `~ treatment.group` and for gamma imputation this is the formula used when fitting the Cox model For `method="Cox"`, additional covariates can be included by explictily giving a formula argument. For logrank/Wilcoxon only additional strata terms can be included. In all cases only the right hand side of the formula is required The survival object on the left hand side is created automatically E.g. for a Cox model could use formula=~arm + covar1. The cluster and tt options cannot be used See the vignettes for further details
`...`	Additional arguments which are passed into the model fit function
`parallel`	The type of parallel operation to be used (if any), can be used for `GammaImputedSet` and `ScoreImputedSet`
`ncpus`	integer: number of processes to be used in parallel operation: typically one would chose this to be the number of available CPUs, can be used for `GammaImputedSet` and `ScoreImputedSet`.
`cl`	An optional parallel or snow cluster for use if `parallel="snow"`. If not supplied, a cluster on the local machine is created for the duration of the call, can be used for `GammaImputedSet` and `ScoreImputedSet`.

Create a valid `ScoreTD` object

Description

Create a valid ScoreTD object

Usage

MakeTimeDepScore(data, Id, time.start, time.end)
MakeTimeDepScore(data, Id, time.start, time.end)

Arguments

`data`	A data frame of time dependent covariates
`Id`	The column name of the subject Id
`time.start`	The covariates are valid for the time [time.start,time.end] where time.start is the column name of time.start
`time.end`	The covariates are valid for the time [time.start,time.end] where time.end is the column name of time.end

Value

A ScoreTD object

Create a list of options which control the nearest neighbour algorithm for risk score imputation

Description

Create a list of options which control the nearest neighbour algorithm for risk score imputation

Usage

NN.options(NN = 5, w.censoring = 0.2, min.subjects = 20)
NN.options(NN = 5, w.censoring = 0.2, min.subjects = 20)

Arguments

`NN`	The (maximum) number of subjects to be included in the risk set
`w.censoring`	The weighting on the censoring risk score when calculating distances for the nearest neighbour calculation A weighting of `(1-w.censoring)` is used for the event risk score
`min.subjects`	If using time dependent score imputation include at least this number of subjects when fitting the Cox model (i.e. include some subjects who were censored/had event earlier than the cenosred observation if neccessary)

Value

A list of options used within the ScoreImputedData function

Perform risk score multiple imputation method

Description

Perform risk score multiple imputation method

Usage

ScoreImpute(
  data,
  event.model,
  censor.model = event.model,
  col.control,
  NN.control = NN.options(),
  time.dep = NULL,
  m,
  bootstrap.strata = rep(1, nrow(data)),
  ...,
  parallel = c("no", "multicore", "snow")[1],
  ncpus = 1L,
  cl = NULL
)
ScoreImpute(
  data,
  event.model,
  censor.model = event.model,
  col.control,
  NN.control = NN.options(),
  time.dep = NULL,
  m,
  bootstrap.strata = rep(1, nrow(data)),
  ...,
  parallel = c("no", "multicore", "snow")[1],
  ncpus = 1L,
  cl = NULL
)

Arguments

`data`	The data set for which imputation is required
`event.model`	The right hand side of the formula to be used for fitting the Cox model for calculating the time to event score e.g. ~Z1+Z2+Z3.
`censor.model`	The right hand side of the formula to be used for fitting the Cox model for calculating the time to censoring score if not included then `event.model` will be used
`col.control`	A list of the columns names of `data` which are used by the imputation algorithm See example below and for further details of these columns and their purpose see `col.headings`
`NN.control`	Parameters which control the nearest neighbour algorithm. See `NN.options`
`time.dep`	A ScoreTD object, to be included if the time dependent score imputation method is to be used, otherwise it should be NULL
`m`	The number of data sets to impute
`bootstrap.strata`	When performing the bootstrap procedure for fitting the models, how should the data be stratified (see strata argument to `boot::boot`). if argument is not used then standard sampling with replacement is used to generate the bootstrap data
`...`	Additional arguments passed into the Cox model Note the subset and na.action arguments should not be used (na.fail will be used when fitting the Cox model)
`parallel`	The type of parallel operation to be used (if any).
`ncpus`	integer: number of processes to be used in parallel operation: typically one would chose this to be the number of available CPUs
`cl`	An optional parallel or snow cluster for use if `parallel="snow"`. If not supplied, a cluster on the local machine is created for the duration of the call.

Details

Note that coxph may fail to converge and the following output Warning in fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iterations and did not converge

It is possible to use ridge regression by including a ridge term in the model formula (e.g. ~Z1+ridge(Z2,theta=1)). See ridge for further details

Value

A ScoreImputedSet object

Examples


data(ScoreInd)

col.control <- col.headings(has.event="event", time="time",
                            Id="Id",arm="arm",
                            DCO.time="DCO.time", 
                            to.impute="to.impute")

## Not run: 
ans <- ScoreImpute(data=ScoreInd,event.model=~Z1+Z2+Z3+Z4+Z5,
                   col.control=col.control, m=5,
                   bootstrap.strata=ScoreInd$arm,
                   NN.control=NN.options(NN=5,w.censoring = 0.2))

## End(Not run)

data(ScoreInd)

col.control <- col.headings(has.event="event", time="time",
                            Id="Id",arm="arm",
                            DCO.time="DCO.time", 
                            to.impute="to.impute")

## Not run: 
ans <- ScoreImpute(data=ScoreInd,event.model=~Z1+Z2+Z3+Z4+Z5,
                   col.control=col.control, m=5,
                   bootstrap.strata=ScoreInd$arm,
                   NN.control=NN.options(NN=5,w.censoring = 0.2))

## End(Not run)

`ScoreImputedData` object

Description

An object which contains

Slots

data: A data frame containing the time to event data with 2 new columns impute.time and impute.event, the imputed event/censoring times and event indicators (for subjects whose data is not imputed these columns contain the unchanged event/censoring time and event indicator )
col.control: The list of column names the risk score imputation method requires see col.headings for further details. If censor.type was not used then col.control$censor.type="using_has.event_col"
default.formula: The default model formula which will be used when fitting the imputed data using a Cox model

`ScoreImputedSet` object

Description

An object which contains the set of score imputed data frames. Use the ExtractSingle function to extract a single ScoreImputedData object. Use the ScoreStat function to fit models to the entire set of imputed data frames

Details

It contains the following:

Slots

data: A data frame containing the unimputed time to event data
col.control: The list of column names the score imputation method requires see col.headings for further details
m: The number of imputed data sets
impute.time: A matrix (1 column per imputed data set) containing the imputed times
impute.event: A matrix (1 column per imputed data set) containing the imputed event indicators
default.formula: The default model formula which will be used when fitting the imputed data using a Cox model

Simulated time to event data with 5 time independent covariates

Description

This dataset is inspired by the simulation described in Hsu and Taylor, Statistics in Medicine (2009) 28:462-475 with an additional DCO.time column

Format

A data.frame containing a row per subject with eleven columns:

Fields

Id: subject identifier
arm: factor for treatment group control=0, active=1
Z1: binary time independent covariate
Z2: continuous time independent covariate
Z3: binary time independent covariate
Z4: continuous time independent covariate
Z5: binary time independent covariate
event: event indicator (1 yes, 0 no)
time: subject censoring/event time (in years)
to.impute: logical, should an event time be imputed for this subject? (this is ignored if subject has event time)
DCO.time: The time the subject would have been censored if they had not had an event or been censored before the data cut off date

ScoreStat object

Description

An S3 object which contains the point estimate and test statistic after fitting a model to a ScoreImputedData object.

Details

The functions print.ScoreStat and as.vector.ScoreStat have been included

The object contains the following:

The test statistic should be normally distributed and hence for the logrank test Z = (O_2 - E_2)/sqrt(V_2), i.e. the square root of the standard Chi squared statistic (with the appropriate sign)

Slots

model: The model used to create the fit
method: The method used for the fit
estimate: A point estimate of the test statistic
var: The estimate for the variance of the test statistic
statistic: The test statistic given by estimate/sqrt(var)

ScoreStatList

Description

The object containing the results of fitting models to a ScoreImputedSet object.

Details

A summary.ScoreStatList has been implemented.

The object contains the following

Slots

fits: A list of ScoreStat objects containing the model fits for the imputed data sets
statistics: A ScoreStatSet object containing the statistics
m: The number of model fits

S3 generic to create a `ScoreStatSet` object

Description

S3 generic to create a ScoreStatSet object

Usage

ScoreStatSet(x)
ScoreStatSet(x)

Arguments

`x`	The object to convert into a `ScoreStatSet` object

Value

A ScoreStatSet object

An object which contains the test statistic and estimators for a set of model fits to imputed data using risk score imputation

Description

The object is a Mx3 matrix, one row per imputed data set and columns: estimate (the point estimates), var (their variances) and Z (the test statistic). M must be > 4

Details

Note the Z should be ~ standard normal (so we do not use the chi_squared test statistic see ScoreStat.object for further details)

The summary.ScoreStatSet function will apply the MI averaging procedures and estimates of the test statistic and p-value

A `ScoreTD` object

Description

This data frame holds time dependent covariates for use with risk score imputation

Details

The data frame contains the following columns: 'Id' for subject ID
'time.start' and 'time.end' the range of time for which the covariate values are valid - i.e. [time.start,time.end]
Additional columns are the time dependent covariates

All data for a single subject should be stored in consecutive rows, sorted by time and the starting time of a row should match the ending time of the previous row

Simulated time dependent variables for time to event data

Description

This data set contains time dependent covariates for the ScoreInd time to event data.

Format

A data.frame containing 1 row per subject-visit

Fields

Id: The Subject Id
start: The covariate given in each row are for a given subject from time 'start'...
end: ... until time end
W1: The value of a (binary) time dependent variable for the subject with the given 'Id' between times 'start' and 'end'
W2: The value of a (continuous) time dependent variable for the subject with the given 'Id' between times 'start' and 'end'

Summary object of `ScoreStatSet` object

Description

This object contains the multiple imputed averages/p-values of a set of estimates from risk score imputed data sets.

Details

A print.summary.ScoreStatSet function has been implemented

This object contains three lists meth1 and meth2 and methRubin meth1 averages the point estimates to produce an F test statistic, meth2 averages the test statistics and prodcues a t test statistic and methRubin follows Rubin's standard rules and is used for calculating confidence intervals

See the vignette for further details.

meth1, meth2 and methRubin are lists with the following elements: estimate: average estimator for meth1, NOTE: for meth2 this is the average test statistic,
var: estimate of variance of "estimate" field
test.stat: test statistic
distribution: distribution of statistical test (i.e. F or t)
p.value: p-value of test

Package 'InformativeCensoring'

Help Index

Perform methods of multiple imputation for time to event data

Description

Details

Author(s)

Specify the columns of the data frame required by score imputation method

Description

Usage

Arguments

Value

Test Cox proportional hazards assumption

Description

Usage

Arguments

See Also

Extract a single risk score/gamma imputed data set/model fit

Description

Usage

Arguments

Value

Perform gamma-Imputation for a given data set

Description

Usage

Arguments

Details

Value

See Also

Examples

GammaImputedData object

Description

Slots

GammaImputedSet object

Description

Details

Slots

See Also

GammaStat object

Description

Details

Slots

GammaStatList object

Description

Details

Slots

S3 generic to fit model(s) to risk score/gamma Imputed objects

Description

Usage

Arguments

See Also

Create a valid ScoreTD object

Description

Usage

Arguments

Value

Create a list of options which control the nearest neighbour algorithm for risk score imputation

Description

Usage

Arguments

Value

Perform risk score multiple imputation method

Description

Usage

Arguments

Details

Value

See Also

Examples

ScoreImputedData object

Description

Slots

ScoreImputedSet object

Description

Details

Slots

See Also

Simulated time to event data with 5 time independent covariates

Description

Format

Fields

`GammaImputedData` object

`GammaImputedSet` object

`GammaStat` object

`GammaStatList` object

Create a valid `ScoreTD` object

`ScoreImputedData` object

`ScoreImputedSet` object

S3 generic to create a `ScoreStatSet` object

A `ScoreTD` object

Summary object of `ScoreStatSet` object