swjm: Stagewise Variable Selection for Joint Models of Semi-Competing Risks

Implements stagewise regression for penalized variable selection in joint models of recurrent events and terminal events (semi-competing risks).

Two model frameworks are supported:

• Joint Frailty Model (JFM): Cox-type model where alpha = recurrence/readmission coefficients (first p elements of theta), beta = terminal/death coefficients (second p elements).

• Joint Scale-Change Model (JSCM): AFT-type model where alpha = recurrence/readmission coefficients (first p elements of theta), beta = terminal/death coefficients (second p elements).

Three penalty types are available:

• "coop": Cooperative lasso, which encourages shared support between the recurrence and terminal event coefficient vectors.

• "lasso": Standard L1 penalty applied coordinate-wise.

• "group": Group lasso, which selects variables jointly across both processes.

Data Format

All functions expect a recurrent-event data frame with the following columns:

id

Subject identifier (integer).

t.start

Interval start time.

t.stop

Interval stop time.

event

1 for recurrent event, 0 for terminal/censoring row.

status

1 for death, 0 for alive/censored.

x1, x2, ..., xp

Covariate columns.

Author

Maintainer: Huo Lingfeng huol@example.com

Authors:

• Jiang Ziren

• Hou Jue

• Huling Jared D.

Other contributors:

• Sy Han (Steven) Chiou [contributor]

• Chiung-Yu Huang [contributor]