Provides functions to implement group sequential procedures that allow for early stopping to declare efficacy using a surrogate marker and the possibility of futility stopping.   More details are available in: Parast, L. and Bartroff, J (2024) <doi:10.1093/biomtc/ujae108>. A tutorial for this package can be found at <https://www.laylaparast.com/surrogateseq>. A Shiny App implementing the methods can be found at <https://parastlab.shinyapps.io/SurrogateSeqApp/>.

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.

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>.