BivRec-package {BivRec}R Documentation

Bivariate Alternating Recurrent Event Data Analysis

Description

Alternating recurrent event data arise frequently in biomedical and social sciences where two types of events such as hospital admissions and discharges occur alternatively over time. As such we implement a collection of nonparametric and semiparametric methods to analyze this type of data. The main functions are bivrecReg and bivrecNP. Use bivrecReg for the estimation of covariate effects on the two alternating event gap times (xij and yij) using semiparametric methods. The method options are "Lee.et.al" and "Chang". Use bivrecNP for the estimation of the joint cumulative distribution function (cdf) for the two alternating events gap times (xij and yij) as well as the marginal survival function for the Type I gap times (xij) and the conditional cdf of the Type II gap times (yij) given an interval of the Type I gap times (xij) in a nonparametric fashion. The package also provides options to simulate and visualize the data and the results of analysis.

Author(s)

Sandra Castro-Pearson, Aparajita Sur, Chi Hyun Lee, Chiung-Yu Huang, Xianghua Luo

References

  1. 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

  2. Huang CY, Wang MC. (2005). Nonparametric estimation of the bivariate recurrence time distribution. Biometrics, 61: 392-402. doi: 10.1111/j.1541-0420.2005.00328.x

  3. 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

  4. 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

See Also

Useful links:


[Package BivRec version 1.2.1 Index]