biv.rec.fit {BivRec} | R Documentation |
This function allows the user to evaluate covariate effects on two alternating recurrent events gap times (referred as type I and type II gap times) under the assumption that the two gap times follow accelerated failure time (AFT) models. See details for the estimation methods provided.
biv.rec.fit(formula, data, method, CI)
formula |
A formula with six variables indicating the bivariate alternating gap time response on the left of the ~ operator and the covariates on the right. The six variables on the left must have the same length and be given as ID + episode + xij + yij + delta_x + delta_y ~ covariates, where
|
data |
A data frame that includes all the vectors/covariates listed in the formula above. |
method |
A string indicating which method to use to estimate effects of the covariates. See details. |
CI |
The level to be used for confidence intervals. Must be between 0.50 and 0.99, where 0.99 would give 99% CI. The default is 0.95. CI=NULL gives point estimates without confidence intervals. |
Two different estimation methods are available:
method = "Lee.et.al" (default) is a U-statistics-based smooth estimating function approach. See Lee CH, Huang C-Y, Xu G, Luo X (2017) for further details.
method = "Chang" is a rank-based estimating function approach. See Chang (2004) for further details. Note that following the Chang method, the variances of the estimated regression coefficients are approximated using the resampling techniques developed by Parzen, Wei and Ying (1994). This approximation requires extensive computing time for a relatively small sample size. In addition, using the Chang method does not guarantee convergence for the estimation of the coefficients.
A BivRec list object containing:
covariate.effects: A data frame summarizing effects of the covariates including the point estimate, standard error and confidence interval.
formula: The formula used to specify components of bivariate recurrent response and covariates.
Chang S-H. (2004). Estimating marginal effects in accelerated failure time models for serial sojourn times among repeated events. Lifetime Data Analysis, 10: 175-190. https://doi.org/10.1023/B:LIDA.0000030202.20842.c9
Lee C, Huang CY, Xu G, Luo X (2017). Semiparametric regression analysis for alternating recurrent event data. Statistics in Medicine, 37: 996-1008. https://doi.org/10.1002/sim.7563
Parzen MI, Wei LJ, Ying Z (1994). A resampling method based on pivotal estimating functions. Biometrika, 81: 341-350. https://doi.org/10.1093/biomet/81.2.341
library(BivRec) # Simulate bivariate alternating recurrent event data set.seed(1234) biv.rec.data <- biv.rec.sim(nsize=150, beta1=c(0.5,0.5), beta2=c(0,-0.5), tau_c=63, set=1.1) # Apply Lee C, Huang CY, Xu G, Luo X (2017) method using one covariate fit.lee <- biv.rec.fit(formula = id + epi + xij + yij + d1 + d2 ~ a1, data=biv.rec.data, method="Lee.et.al", CI=NULL) fit.lee$covariate.effects ## Not run: #This is an example with longer runtime. library(BivRec) # Simulate bivariate alternating recurrent event data set.seed(1234) biv.rec.data <- biv.rec.sim(nsize=150, beta1=c(0.5,0.5), beta2=c(0,-0.5), tau_c=63, set=1.1) # Apply Lee C, Huang CY, Xu G, Luo X (2017) method using multiple covariates # and 99% confidence intervals. fit.lee <- biv.rec.fit(formula = id + epi + xij + yij + d1 + d2 ~ a1 + a2, data=biv.rec.data, method="Lee.et.al", CI=0.99) fit.lee$covariate.effects ## End(Not run) # To apply Chang (2004) method use method="Chang"