Bootstrap standard error estimates
Function to obtain bootstrap standard error estimates for the parameter
estimates of the get_estimates function, under the generalized
linear model (GLM) or accelerated failure time (AFT) setting for the analysis
of a normally-distributed or censored time-to-event primary outcome.
bootstrap_se(setting = "GLM", BS_rep = 1000, Y = NULL, X = NULL, K = NULL, L = NULL, C = NULL)
setting |
String with value |
BS_rep |
Integer indicating the number of bootstrap samples that are drawn. |
Y |
Numeric input vector for the primary outcome. |
X |
Numeric input vector for the exposure variable. |
K |
Numeric input vector for the intermediate outcome. |
L |
Numeric input vector for the observed confounding factor. |
C |
Numeric input vector for the censoring indicator under the AFT setting (must be coded 0 = censored, 1 = uncensored). |
Under the GLM setting for the analysis of a normally-distributed primary outcome Y, bootstrap standard error estimates are obtained for the estimates of the parameters α0, α1, α2, α3, σ1^2, α4, αXY, σ2^2 in the models
Y = α0 + α1*K + α2*X + α3*L + ε1, ε1 ~ N(0,σ1^2)
Y* = Y - mean(Y) - α1*(K-mean(K))
Y* = α0 + αXY*X + ε2, ε2 ~ N(0,σ2^2),
accounting for the additional variability from the 2-stage approach.
Under the AFT setting for the analysis of a censored time-to-event primary outcome, bootstrap standard error estimates are similarly obtained of the parameter estimates of α0, α1, α2, α3, σ1, α4, αXY, σ2^2
Returns a vector with the bootstrap standard error estimates of the parameter estimates.
dat <- generate_data(setting = "GLM", n = 100)
# For illustration use here only 100 bootstrap samples, recommended is using 1000
bootstrap_se(setting = "GLM", BS_rep = 100, Y = dat$Y, X = dat$X,
K = dat$K, L = dat$L)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.