biv.rec.fit: Deprecated: Use bivrecReg
In BivRec: Bivariate Alternating Recurrent Event Data Analysis

Deprecated function from the previous version. Use bivrecReg.

1	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 + d1 + d2 ~ covariates`, where: `id`: Vector of subject's unique identifier (i). `episode`: Vector indicating the observation or episode (j) for a subject (i). This will determine order of events for each subject. `xij`: Vector with the lengths of time spent in event of Type I for individual i in episode j. `yij`: Vector with the lengths of time spent in event of Type II for individual i in episode j. `d1`: Vector of censoring indicator corresponding to Type I gap times (xij): = 1 for uncensored, and = 0 for censored gap times. `d2`: Vector of censoring indicator corresponding to Type II gap times (yij): = 1 for uncensored, and = 0 for censored gap times. covariates: the names of the covariates in the form covariate_1 + ... + covariate_p.
`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. The default is 0.95.

Two different estimation methods are available:

method = "Lee.et.al" (default) is a U-statistics-based smooth estimating function approach. See Lee, Huang, Xu, Luo (2018) 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.

See bivrecReg

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. doi: 10.1023/B:LIDA.0000030202.20842.c9
Lee CH, Huang CY, Xu G, Luo X. (2018). Semiparametric regression analysis for alternating recurrent event data. Statistics in Medicine, 37: 996-1008. doi: 10.1002/sim.7563
Parzen MI, Wei LJ, Ying Z. (1994). A resampling method based on pivotal estimating functions. Biometrika, 81: 341-350. http://www.people.fas.harvard.edu/~mparzen/published/parzen1.pdf