simexaft: SIMEX Algorithm for Accelerated Failure Time Model with...
In simexaft: simexaft

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/simexaft.R

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

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")

`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".

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.

`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

Juan Xiong, Wenqing He and Grace Y. Yi

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.

survreg, plotsimexaft

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)