BivRec

BivRec performs analysis of bivariate alternating recurrent event data in R, enabling nonparametric estimation of joint gap-time distributions, semiparametric accelerated failure time regression, visualization, and simulation for longitudinal studies.

Key Features:

• Nonparametric Analysis: Implements nonparametric estimation of the joint distribution of bivariate gap times via the bivrecNP routine.
• Semiparametric Regression: Provides semiparametric regression methods under the accelerated failure time model framework via the bivrecReg routine to assess covariate effects on both gap times.
• Exploratory Data Visualization: Includes visualization tools to graphically represent gap times by groups for exploratory analysis.
• Data Simulation: Offers capability to simulate bivariate alternating recurrent event data for method evaluation and testing.

Scientific Applications:

• Chronic disease longitudinal studies: Analyzes patient transitions between two recurrent states (e.g., care and break periods) to characterize timing and dependence of recurrent events.
• Psychiatric case-register analysis (South Verona PCR): Applied to a subset of the South Verona Psychiatric Case Register (PCR) to demonstrate visualization, nonparametric estimation, regression analysis, and simulation.

Methodology:

Nonparametric estimation of the joint distribution of bivariate gap times (bivrecNP); semiparametric regression within an accelerated failure time model framework (bivrecReg); plus routines for visualization and simulation of bivariate alternating recurrent event data.