Package 'simexaft' reference manual

Title:	simexaft
Description:	Implement of the Simulation-Extrapolation (SIMEX) algorithm for the accelerated failure time (AFT) with covariates subject to measurement error.
Authors:	Juan Xiong <[email protected]>, Wenqing He <[email protected]>, Grace Y. Yi<[email protected]>
Maintainer:	Juan Xiong <[email protected]>
License:	GPL
Version:	1.0.7.1
Built:	2024-12-10 06:49:46 UTC
Source:	CRAN

SIMEX algorithm for the accelerated failure time model with mismeasured covariates

Description

Implementation of Simulation-Extrapolation (SIMEX) algorithm for the accelerated failure time (AFT) model with mismeasured covariates.

Details

Package:	simexaft
Type:	Package
Version:	1.0.7
Date:	2014-01-19
License:	GPL
Imports:	mvtnorm, survival
LazyLoad:	yes

Author(s)

Juan Xiong <[email protected]>, Wenqing He <[email protected]>, Grace Y. Yi<[email protected]>

Maintainer: Juan Xiong <[email protected]>

References

Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F. and Hothorn, T. (2011). mvtnorm: Multivariate Normal and t Distributions. R package version 0.9-9991, URL http://CRAN. R-project.org/package=mvtnorm.

He, W., Yi, G. Y. and Xiong, J. (2007). Accelerated Failure Time Models with Covariates Subject to Measurement Error. Statistics in Medicine, 26, 4817-4832.

Therneau, T. and Lumley, T. (2011). survival: Survival Analysis, Including Penalised Likelihood. R package version 2.36-10, URL http://CRAN.R-project.org/package=survival.

Busselton Health Study

Description

This dataset is a subset of the Busselton Health study. The Busselton Health study was a repeated cross-sectional survey that was conducted to the community of Busselton in Western Australian.

Usage

data(BHS)data(BHS)

Format

A data frame with 100 observations on the following 18 variables.

PAIR: spouse pair id number
AGE: age at survey
SEX: sex
SBP: systolic blood pressure
DBP: diastolic blood pressure
BMI: body mass index
CHOL: cholesterol level
DIABETES: history of diabetes
RXHYPER: on blood pressure treatment
CHID: history of coronary heart disease
SMOKE: smoking status
DRINKING: alcohol consumption level
SURVTIME: survival time from survey data to date last known alive
DTHCENS: censoring indicator
CHDCENS: indicator of the death from coronary heart disease
CVDCENS: indicator of the death from cardiovascular disease
SMOKE1: indicator of ex-smoker
SMOKE2: indicator of current smoker

Details

This dataset is a subset of the Busselton Health study. The Busselton Health study was a repeated cross-sectional survey that was conducted to the community of Busselton in Western Australian.

Source

He, W., Yi, G. Y. and Xiong, J. (2007). Accelerated Failure Time Models with Covariates Subject to Measurement Error. Statistics in Medicine, 26, 4817-4832.

Knuiman, M. W., Cullent, K. J., Bulsara, M. K., Welborn, T. A. and Hobbs, M. S. T. (1994). Mortality trends, 1965 to 1989, in Busselton, The Site of Repeated Health Surveys and Interventions. Australian Journal of Public Health, 18, 129-135.

Linear Extrapolation Method

Description

Linear extrapolation step of SIMEX algorithm.

Usage

linearextrapolation(A1, A2, A3, lambda)
linearextrapolation(A1, A2, A3, lambda)

Arguments

`A1`	estimates obtained from each level of labmda.
`A2`	variances estimates obtained from each level of lambda.
`A3`	scale estimates obtained from each level of lambda.
`lambda`	vector of lambdas, the grids for the extrapolation step.

Value

`reg1`	extrapolation back to lambda=-1 yield the SIMEX estimates
`reg2`	extrapolation back to lambda=-1 yield the SIMEX estimates of variances
`scalereg`	extrapolation back to lambda=-1 yield the SIMEX estimates of scale

Author(s)

Juan Xiong, Wenqing He and Grace Y. Yi

References

He, W., Yi, G. Y. and Xiong, J. (2007). Accelerated Failure Time Models with Covariates Subject to Measurement Error. Statistics in Medicine, 26, 4817-4832.

Therneau, T. and Lumley, T. (2011). survival: Survival Analysis, Including Penalised Likelihood. R package version 2.36-10, URL http://CRAN.R-project.org/package=survival.

Plot the Extrapolation Curve

Description

A function to give the plot of the extrapolation curve for any covariables of the AFT model.

Usage

plotsimexaft(obj, var, extrapolation=c("linear","quadratic","both"), ylimit)
plotsimexaft(obj, var, extrapolation=c("linear","quadratic","both"), ylimit)

Arguments

`obj`	an object returned by the function "simexaft".
`var`	a character string of any covariate used in the AFT model.
`extrapolation`	a character string giving the type of the extrapolation method, the default is set to be linear extrapolation.
`ylimit`	the y limits of the plot.

Details

The green points are the average of estimates of B iteration for each labmda.

The linear extrapolation curve is in blue, the corresponding SIMEX estimate is the solid red circle.

The quadratic extrapolation curve is in red, the corresponding SIMEX estimate is the solid blue circle.

The "both" option of the extrapolation method gives both linear and quadratic extrapolation curves.

Author(s)

Juan Xiong, Wenqing He and Grace Y. Yi

Examples

###########example for the dataset with known variance.################
library("simexaft")
library("survival")
data("BHS")
dataset <- BHS
dataset$SBP <- log(dataset$SBP-50)

set.seed(120)
formula <- Surv(SURVTIME,DTHCENS)~SBP+CHOL+AGE+BMI+SMOKE1+SMOKE2

ind <- c("SBP", "CHOL")
err.mat <- diag(rep(0.5625, 2))

### fit an AFT model with quadratic extrapolation
out2 <- simexaft(formula = formula, data = dataset, SIMEXvariable = ind, 
        repeated = FALSE, repind = list(), err.mat = err.mat, B = 50, 
        lambda=seq(0, 2, 0.1),extrapolation="quadratic", dist="weibull")

summary(out2)

plotsimexaft(out2,"SBP","both",ylimit=c(-3,1))

###########example for the dataset with known variance.################
library("simexaft")
library("survival")
data("BHS")
dataset <- BHS
dataset$SBP <- log(dataset$SBP-50)

set.seed(120)
formula <- Surv(SURVTIME,DTHCENS)~SBP+CHOL+AGE+BMI+SMOKE1+SMOKE2

ind <- c("SBP", "CHOL")
err.mat <- diag(rep(0.5625, 2))

### fit an AFT model with quadratic extrapolation
out2 <- simexaft(formula = formula, data = dataset, SIMEXvariable = ind, 
        repeated = FALSE, repind = list(), err.mat = err.mat, B = 50, 
        lambda=seq(0, 2, 0.1),extrapolation="quadratic", dist="weibull")

summary(out2)

plotsimexaft(out2,"SBP","both",ylimit=c(-3,1))

Print Method for the SIMEXAFT Class

Description

Printing the most important values in a clear way.

Usage

## S3 method for class 'simexaft'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'simexaft'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

`x`	object of class SIEMXAFT.
`digits`	number of digits to be printed.
`...`	arguments passed to other functions.

Author(s)

Juan Xiong, Wenqing He and Grace Y. Yi

References

He, W., Yi, G. Y. and Xiong, J. (2007). Accelerated Failure Time Models with Covariates Subject to Measurement Error. Statistics in Medicine, 26, 4817-4832.

Therneau, T. and Lumley, T. (2011). survival: Survival Analysis, Including Penalised Likelihood. R package version 2.36-10, URL http://CRAN.R-project.org/package=survival.

Quadratic Extrapolation Method

Description

Quadratic extrapolation step of SIMEX algorithm.

Usage

quadraticextrapolation(A1, A2, A3, lambda)
quadraticextrapolation(A1, A2, A3, lambda)

Arguments

`A1`	estimates obtained from each level of labmda.
`A2`	variances estimates obtained from each level of lambda.
`A3`	scale estimates obtained from each level of lambda.
`lambda`	vector of lambdas, the grids for the extrapolation step.

Value

`reg1`	extrapolation back to lambda=-1 yield the SIMEX estimates
`reg2`	extrapolation back to lambda=-1 yield the SIMEX estimates of variances
`scalereg`	extrapolation back to lambda=-1 yield the SIMEX estimates of scale

Author(s)

Juan Xiong, Wenqing He and Grace Y. Yi

References

He, W., Yi, G. Y. and Xiong, J. (2007). Accelerated Failure Time Models with Covariates Subject to Measurement Error. Statistics in Medicine, 26, 4817-4832.

Therneau, T. and Lumley, T. (2011). survival: Survival Analysis, Including Penalised Likelihood. R package version 2.36-10, URL http://CRAN.R-project.org/package=survival.

rhDNase Data Set

Description

This is a dataset reported by Fuchs et al. (1994) for a double-blind randomized multicenter clinical trial designed to evaluate the effect of rhDNase, a recombinant deoxyribonuclease I enzyme, versus placebo on the occurrence of respiratory exacerbations among patients with cystic fibrosis. Data on the occurrence and resolution of all exacerbations were recorded for 645 patients in this trial. For more details about the dataset feature, see Cook and Lawless (2007). Here we only include the first record of the patients that have etype=1.

Usage

data(rhDNase)data(rhDNase)

Format

A data frame with 641 observations on the following 11 variables.

id: patient identifier
trt: the treatment assignment, trt=1 if patient receive rhDNase and 0 if patent receive placebo
fev: baseline measurement of forced expiratory volume
fev2: baseline measurement of forced expiratory volume
time1: the start of a period indicating when subjects become " at risk" for a transition
time2: if etype=1 then time2 corresponds the onset of an exacerbation (or censoring) and if etype=2 then time2 corresponds to the time of a resolution of an exacerbation (or censoring)
status: status equals 1 if time2 is a transition time and equals 0 if it is a censoring time
etype: the indicator of the nature of the event time recorded in time2
enum: the cumulative number of lines in the data frame for each individual
enum1: the cumulative number of exacerbation-free periods
enum2: a numeric vector

Source

Cook, R. J. and Lawless, J. F. (2007). The Statistical Analysis of Recurrent Events. Springer, New York.

SIMEX Algorithm for Accelerated Failure Time Model with Covariates Subject to Measurement Error

Description

Implementation of the SIMEX algorithm for Accelerated Failure Time model with covariates subject to measurement error.

Usage

simexaft(formula = formula(data), data = parent.frame(), 
        SIMEXvariable, repeated = FALSE, repind = list(), 
        err.mat = err.mat, B = 50, lambda = seq(0, 2, 0.1), 
        extrapolation = "quadratic", dist = "weibull")
simexaft(formula = formula(data), data = parent.frame(), 
        SIMEXvariable, repeated = FALSE, repind = list(), 
        err.mat = err.mat, B = 50, lambda = seq(0, 2, 0.1), 
        extrapolation = "quadratic", dist = "weibull")

Arguments

`formula`	specifies the model to be fitted, with the variables coming with data. This argument has the same format as the formula argument in the existing R function "survreg".
`data`	optional data frame in which to interpret the varialbes occurring in the formula.
`SIMEXvariable`	the index of the covariate variables that are subject to measurement error.
`repeated`	set to TRUE or FALSE to indicate if there are repeated measurements for the mis-measured variables.
`repind`	the index of the repeated measurement variables for each mis-measured variable. It has an R list form. If repeated = TRUE, repind must be specify.
`err.mat`	specifies the variables with measurement error, If repeated = FALSE, err.mat must be specify.
`B`	the number of simulated samples for the simulation step. The default is set to be 50.
`lambda`	the vector of lambdas, the grids for the extrapolation step.
`extrapolation`	specifies the function form for the extrapolation step. The options are linear, quadratic and both. The default is set to be quadratic.(first 4 letters are enough)
`dist`	specifies a parametric distribution that is assumed in AFT model. This argument is the same as the dist option in the existing R function "survreg". These include "weibull", "exponential", "gaussian", "logistic", "lognormal", and "loglogistic".

Details

If the SIMEXvariable is repeated measured then you only need to use arguments repeated and repind without mention err.mat. The summary.simex will contain repind.

Value

`coefficient`	the corrected coefficients of the AFT model
`se`	the standard deviation of each coefficient
`pvalue`	the p-value for the hypothesis of that coefficient equal zero
`scalreg`	the estimate of the scale
`theta`	the estimates for every B and lambda
`lambda`	the vector of lambdas for which the simulation step should be done
`B`	the number of simulated samples for the simulation step.
`formula`	the model to be fitted in the survreg function
`err.mat`	the covariance matrix of the variables with measurement error
`repind`	the list contiains the names of the repeat measument variables
`extrapolation`	the extrapolation method: linear ,quadratic are implemented (first 4 letters are enough)
`SIMEXvariable`	the vector contains the names of the variables with meansurement error

Author(s)

Juan Xiong, Wenqing He and Grace Y. Yi

References

He, W., Yi, G. Y. and Xiong, J. (2007). Accelerated Failure Time Models with Covariates Subject to Measurement Error. Statistics in Medicine, 26, 4817-4832.

Therneau, T. and Lumley, T. (2011). survival: Survival Analysis, Including Penalised Likelihood. R package version 2.36-10, URL http://CRAN.R-project.org/package=survival.

Examples

library("simexaft")
library("survival")
data("BHS")

dataset <- BHS

dataset$SBP <- log(dataset$SBP - 50)

###Naive AFT approach
formula <- Surv(SURVTIME,DTHCENS) ~ SBP + CHOL + AGE + BMI + SMOKE1 + SMOKE2

out1 <- survreg(formula = formula, data = dataset, dist = "weibull")

summary(out1)


###fit a AFT model with quadratic extrapolation
set.seed(120)

ind <- c("SBP", "CHOL")

err.mat <- diag(rep(0.5625, 2))

out2 <- simexaft(formula = formula, data = dataset, SIMEXvariable = ind, 
        repeated = FALSE, repind = list(), err.mat = err.mat, B = 50,
        lambda = seq(0, 2, 0.1),extrapolation = "quadratic", dist = "weibull")

summary(out2)







    #################### repeated measurements #################################
    data("rhDNase")

    ###true model
    rhDNase$fev.ave <- (rhDNase$fev + rhDNase$fev2)/2

    output1 <- survreg(Surv(time2, status) ~ trt + fev.ave, data = rhDNase, 
                    dist = "weibull")

    summary(output1)


    ####sensitive analysis#####
    set.seed(120)

    fev.error <- rhDNase$fev + rnorm(length(rhDNase$fev), mean = 0, 
                                    sd = 0.15 * sd(rhDNase$fev))

    fev.error2 <- rhDNase$fev2 + rnorm(length(rhDNase$fev2),mean = 0, 
                                    sd = 0.15 * sd(rhDNase$fev2))

    dataset2 <- cbind(rhDNase[, c("time2", "status", "trt")], fev.error, fev.error2)

    formula <- Surv(time2, status) ~ trt + fev.error

    ind <- "fev.error"


    ########naive model using the average FEV value####################
    fev.error.c <- (fev.error + fev.error2)/2

    output2 <- survreg(Surv(time2, status) ~ trt + fev.error.c, data = rhDNase, 
                    dist = "weibull")

    summary(output2)


    ######use simexaft and apply the quadratic extrapolation######
    formula <- Surv(time2, status) ~ trt + fev.error

    output3 <- simexaft(formula = formula, data = dataset2, SIMEXvariable = ind, 
            repeated=TRUE,repind=list(c("fev.error", "fev.error2")), err.mat=NULL, 
            B=50, lambda=seq(0,2, 0.1), extrapolation="quadratic", dist="weibull")
            
    summary(output3)


library("simexaft")
library("survival")
data("BHS")

dataset <- BHS

dataset$SBP <- log(dataset$SBP - 50)

###Naive AFT approach
formula <- Surv(SURVTIME,DTHCENS) ~ SBP + CHOL + AGE + BMI + SMOKE1 + SMOKE2

out1 <- survreg(formula = formula, data = dataset, dist = "weibull")

summary(out1)


###fit a AFT model with quadratic extrapolation
set.seed(120)

ind <- c("SBP", "CHOL")

err.mat <- diag(rep(0.5625, 2))

out2 <- simexaft(formula = formula, data = dataset, SIMEXvariable = ind, 
        repeated = FALSE, repind = list(), err.mat = err.mat, B = 50,
        lambda = seq(0, 2, 0.1),extrapolation = "quadratic", dist = "weibull")

summary(out2)







    #################### repeated measurements #################################
    data("rhDNase")

    ###true model
    rhDNase$fev.ave <- (rhDNase$fev + rhDNase$fev2)/2

    output1 <- survreg(Surv(time2, status) ~ trt + fev.ave, data = rhDNase, 
                    dist = "weibull")

    summary(output1)


    ####sensitive analysis#####
    set.seed(120)

    fev.error <- rhDNase$fev + rnorm(length(rhDNase$fev), mean = 0, 
                                    sd = 0.15 * sd(rhDNase$fev))

    fev.error2 <- rhDNase$fev2 + rnorm(length(rhDNase$fev2),mean = 0, 
                                    sd = 0.15 * sd(rhDNase$fev2))

    dataset2 <- cbind(rhDNase[, c("time2", "status", "trt")], fev.error, fev.error2)

    formula <- Surv(time2, status) ~ trt + fev.error

    ind <- "fev.error"


    ########naive model using the average FEV value####################
    fev.error.c <- (fev.error + fev.error2)/2

    output2 <- survreg(Surv(time2, status) ~ trt + fev.error.c, data = rhDNase, 
                    dist = "weibull")

    summary(output2)


    ######use simexaft and apply the quadratic extrapolation######
    formula <- Surv(time2, status) ~ trt + fev.error

    output3 <- simexaft(formula = formula, data = dataset2, SIMEXvariable = ind, 
            repeated=TRUE,repind=list(c("fev.error", "fev.error2")), err.mat=NULL, 
            B=50, lambda=seq(0,2, 0.1), extrapolation="quadratic", dist="weibull")
            
    summary(output3)

Summarizing Model fits for the AFT model by SIMEX method

Description

Summary method for the class SIMEXAFF.

Usage

## S3 method for class 'simexaft'
summary(object, ...)
## S3 method for class 'simexaft'
summary(object, ...)

Arguments

`object`	object of class SIMEXAFT.
`...`	further arguments.

Value

`coefficients`	a p x 3 matrix with columns for the estimated coefficient its standard error, corresponding(two-sided) p-value
`scalereg`	estimate of the scale
`extrapolation`	the extrapolation method
`SIMEXvariable`	character vector of the SIMEXvariable

Author(s)

Juan Xiong, Wenqing He and Grace Y. Yi

References

He, W., Yi, G. Y. and Xiong, J. (2007). Accelerated Failure Time Models with Covariates Subject to Measurement Error. Statistics in Medicine, 26, 4817-4832.

Therneau, T. and Lumley, T. (2011). survival: Survival Analysis, Including Penalised Likelihood. R package version 2.36-10, URL http://CRAN.R-project.org/package=survival.

Package 'simexaft'

Help Index

SIMEX algorithm for the accelerated failure time model with mismeasured covariates

Description

Details

Author(s)

References

Busselton Health Study

Description

Usage

Format

Details

Source

See Also

Linear Extrapolation Method

Description

Usage

Arguments

Value

Author(s)

References

See Also

Plot the Extrapolation Curve

Description

Usage

Arguments

Details

Author(s)

See Also

Examples

Print Method for the SIMEXAFT Class

Description

Usage

Arguments

Author(s)

References

Quadratic Extrapolation Method

Description

Usage

Arguments

Value

Author(s)

References

See Also

rhDNase Data Set

Description

Usage

Format

Source

See Also

SIMEX Algorithm for Accelerated Failure Time Model with Covariates Subject to Measurement Error

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Summarizing Model fits for the AFT model by SIMEX method

Description

Usage

Arguments

Value

Author(s)

References

See Also