Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.

suRtex was designed for easy descriptive statistic reporting of categorical survey data (e.g., Likert scales) in LaTeX. suRtex takes a matrix or data frame and produces the LaTeX code necessary for a sideways table creation. Mean, median, standard deviation, and sample size are optional.

Fit Cox non-proportional hazards models with time-varying coefficients. 
    Both unpenalized procedures (Newton and proximal Newton) and penalized procedures 
    (P-splines and smoothing splines) are included using B-spline basis functions for 
    estimating time-varying coefficients. For penalized procedures, cross validations, 
    mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for 
    carrying out post-estimation visualization, summarization, point-wise confidence 
    interval and hypothesis testing are also provided.
    For more information, see Wu et al. (2022) <doi: 10.1007/s10985-021-09544-2> and 
    Luo et al. (2023) <doi:10.1177/09622802231181471>.

Performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included.

Performs two-sample comparisons based on average hazard with survival weight (AHSW) or general censoring-free incidence rate (CFIR) proposed by Uno and Horiguchi (2023) <doi:10.1002/sim.9651>.

Provides a variety of functions to estimate
        time-dependent true/false positive rates and AUC curves from a
        set of censored survival data.

Tests for equality of two survival functions based on integrated weighted differences of two Kaplan-Meier curves.

Three new methods to perform outlier detection in a survival context. In total there are six methods provided, the first three methods are traditional residual-based outlier detection methods, the second three are the concordance-based. Package developed during the work on the two following publications: Pinto J., Carvalho A. and Vinga S. (2015) <doi:10.5220/0005225300750082>; Pinto J.D., Carvalho A.M., Vinga S. (2015) <doi:10.1007/978-3-319-27926-8_22>.