Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment Effect" Biometrics, 75(4):1253-1263. <doi:10.1111/biom.13067>.

Provides functions for the evaluation of
    surrogate endpoints when both the surrogate and the true endpoint are failure
    time variables. The approaches implemented are:
    (1) the two-step approach (Burzykowski et al, 2001) <DOI:10.1111/1467-9876.00244> with a copula model (Clayton, Plackett, Hougaard) at
    the first step and either a linear regression of log-hazard ratios at the second
    step (either adjusted or not for measurement error);
    (2) mixed proportional hazard models estimated via mixed Poisson GLM
    (Rotolo et al, 2017 <DOI:10.1177/0962280217718582>).

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>.