ALassoSurvIC: Adaptive Lasso for the Cox Regression with Interval Censored and
Possibly Left Truncated Data
Penalized variable selection tools for the Cox
proportional hazards model with interval censored and possibly
left truncated data. It performs variable selection via
penalized nonparametric maximum likelihood estimation with an
adaptive lasso penalty. The optimal thresholding parameter can be
searched by the package based on the profile Bayesian information
criterion (BIC). The asymptotic validity of the methodology is
established in Li et al. (2019 <doi:10.1177/0962280219856238>).
The unpenalized nonparametric maximum likelihood estimation for
interval censored and possibly left truncated data is also
available.
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