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SurrogateRank - Rank-Based Test to Evaluate a Surrogate Marker

Uses a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. Details are described in Parast et al (2024) <doi:10.1093/biomtc/ujad035>, in Hughes A et al (2025) <doi:10.1002/sim.70241>, and in Hughes A et al (2026) <doi:10.48550/arXiv.2605.03819>. A tutorial for this package can be found at <https://www.laylaparast.com/surrogaterank> and a Shiny App implementing the package can be found at <https://parastlab.shinyapps.io/SurrogateRankApp/>.

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4.43 score 2 stars 1 dependents 1 scripts 246 downloads

Rsurrogate - Robust Estimation of the Proportion of Treatment Effect Explained by Surrogate Marker Information

Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.

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4.26 score 1 stars 5 dependents 24 scripts 284 downloads

landpred - Landmark Prediction of a Survival Outcome

Nonparametric methods for landmark prediction of long-term survival outcomes, incorporating covariate and short-term event information. The package supports the construction of flexible varying-coefficient models that use discrete covariates, as well as multiple continuous covariates. The goal is to improve prediction accuracy when censored short-term events are available as predictors, using robust nonparametric procedures that do not require correct model specification and avoid restrictive parametric assumptions found in alternative methods. More information on these methods can be found in Parast et al. 2012 <doi:10.1080/01621459.2012.721281>, Parast et al. 2011 <doi:10.1002/bimj.201000150>, and Parast and Cai 2013 <doi:10.1002/sim.5776>. A tutorial for this package is available here: <https://www.laylaparast.com/landpred>.

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4.23 score 2 dependents 14 scripts 238 downloads

SurrogateOutcome - Estimation of the Proportion of Treatment Effect Explained by Surrogate Outcome Information

Provides functions to estimate the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, et al (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1> and Wang et al (2025) "Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival" (Under Review) <doi:10.48550/arXiv.2412.14129>. A tutorial for this package can be found at <https://www.laylaparast.com/surrogateoutcome>.

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3.18 score 162 downloads

hetsurr - Assessing Heterogeneity in the Utility of a Surrogate Marker

Provides a function to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate. The main function can be used for either a continuous or discrete baseline covariate. More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press.

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3.18 score 1 dependents 234 downloads

hetsurrSurv - Assessing Heterogeneity in Surrogacy Using Censored Data

Provides functions to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate using censored (survival data), and to test for heterogeneity across multiple time points. More details will be available in the future in: Parast, L., Tian L, Cai, T. (2023) "Assessing Heterogeneity in Surrogacy Using Censored Data." Under Review.

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3.00 score 124 downloads

hettest - Testing for a Treatment Effect Using a Heterogeneous Surrogate Marker

Provides functions to test for a treatment effect using surrogate marker information accounting for heterogeneity in the utility of the surrogate.

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3.00 score 1 stars 136 downloads

SBdecomp - Estimation of the Proportion of SB Explained by Confounders

Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 <doi: 10.1002/sim.8549>.

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2.70 score 1 stars 197 downloads

SurrogateSeq - Group Sequential Testing of a Treatment Effect Using a Surrogate Marker

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 will be available in the future in: Parast, L. and Bartroff, J (2024) "Group Sequential Testing of a Treatment Effect Using a Surrogate Marker".

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2.70 score 1 scripts 158 downloads

longsurr - Longitudinal Surrogate Marker Analysis

Assess the proportion of treatment effect explained by a longitudinal surrogate marker as described in Agniel D and Parast L (2021) <doi:10.1111/biom.13310>; and estimate the treatment effect on a longitudinal surrogate marker as described in Wang et al. (2025) <doi:10.1093/biomtc/ujaf104>.

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2.70 score 207 downloads

survivalsurrogate - Evaluate a Longitudinal Surrogate with a Censored Outcome

Provides influence function-based methods to evaluate a longitudinal surrogate marker in a censored time-to-event outcome setting, with plug-in and targeted maximum likelihood estimation options. Details are described in: Agniel D and Parast L (2025). "Robust Evaluation of Longitudinal Surrogate Markers with Censored Data." Journal of the Royal Statistical Society: Series B <doi:10.1093/jrsssb/qkae119>. A tutorial for this package can be found at <https://www.laylaparast.com/survivalsurrogate> and a Shiny App implementing the package can be found at <https://parastlab.shinyapps.io/survivalsurrogateApp/>.

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1.30 score 2 scripts 185 downloads

landest - Landmark Estimation of Survival and Treatment Effect

Provides functions to estimate survival and a treatment effect using a landmark estimation approach.

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1.08 score 12 scripts 257 downloads

freebird - Estimation and Inference for High Dimensional Mediation and Surrogate Analysis

Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) <doi:10.1002/sim.9352> and Zhou et al (2020) <doi:10.1093/biomet/asaa016>. This package relies on the optimization software 'MOSEK', <https://www.mosek.com>.

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1.04 score 11 scripts 219 downloads

SurrogateTest - Early Testing for a Treatment Effect using Surrogate Marker Information

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>.

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1.04 score 11 scripts 208 downloads

SurrogateParadoxTest - Empirical Testing of Surrogate Paradox Assumptions

Provides functions to nonparametrically assess assumptions sufficient to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of conditional mean functions, and non-negative residual treatment effect. Details are described in: Hsiao E, Tian L, and Parast L (2026). "Avoiding the surrogate paradox: an empirical framework for assessing assumptions." Journal of Nonparametric Statistics <doi:10.1080/10485252.2025.2498609>. There are also functions to assess resilience to the surrogate paradox via calculation of the resilience probability, the resilience bound, and the resilience set. Details will be available in Hsiao E, Tian L, and Parast L, "Resilience Measures for the Surrogate Paradox" (Under Review). Lastly, there is a function to assess resilience to the surrogate paradox in the met-analytic setting, described in Hsiao E and Parast L, "A Functional-Class Meta-Analytic Framework for Quantifying Surrogate Resilience" (Under Review). A tutorial for this package can be found at <https://www.laylaparast.com/surrogateparadoxtest>.

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openblascpp

1.00 score 3 scripts 503 downloads

CMFsurrogate - Calibrated Model Fusion Approach to Combine Surrogate Markers

Uses a calibrated model fusion approach to optimally combine multiple surrogate markers. Specifically, two initial estimates of optimal composite scores of the markers are obtained; the optimal calibrated combination of the two estimated scores is then constructed which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained (PTE) by the final combined score. The primary function, pte.estimate.multiple(), estimates the PTE of the identified combination of multiple surrogate markers. Details are described in Wang et al (2022) <doi:10.1111/biom.13677>. A tutorial for the package is available at <https://www.laylaparast.com/cmfsurrogate> and a Shiny App is available at <https://parastlab.shinyapps.io/CMFsurrogateApp/>.

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1.00 score 212 downloads

etsi - Efficient Testing Using Surrogate Information

Provides functions for treatment effect estimation, hypothesis testing, and future study design for settings where the surrogate is used in place of the primary outcome for individuals for whom the surrogate is valid, and the primary outcome is purposefully measured in the remaining patients. More details are available in: Knowlton, R., Parast, L. (2024) ``Efficient Testing Using Surrogate Information," Biometrical Journal, 67(6): e70086, <doi:10.1002/bimj.70086>. A tutorial for this package can be found at <https://www.laylaparast.com/etsi>.

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1.00 score 1 scripts 118 downloads

cohetsurr - Assessing Complex Heterogeneity in Surrogacy

Provides functions to assess complex heterogeneity in the strength of a surrogate marker with respect to multiple baseline covariates, in either a randomized treatment setting or observational setting. For a randomized treatment setting, the functions assess and test for heterogeneity using both a parametric model and a semiparametric two-step model. More details for the randomized setting are available in: Knowlton, R., Tian, L., & Parast, L. (2025). "A General Framework to Assess Complex Heterogeneity in the Strength of a Surrogate Marker," Statistics in Medicine, 44(5), e70001 <doi:10.1002/sim.70001>. For an observational setting, functions in this package assess complex heterogeneity in the strength of a surrogate marker using meta-learners, with options for different base learners. More details for the observational setting will be available in the future in: Knowlton, R., Parast, L. (2025) "Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners." A tutorial for this package can be found at <https://www.laylaparast.com/cohetsurr>.

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1.00 score 1 scripts 191 downloads

PTERP - PTE and RP for Optimally-Transformed Surrogate

Evaluates the strength of a surrogate marker by estimating the proportion of treatment effect explained (PTE) and relative power(RP) for the optimally-transformed version of the surrogate. Details available in Wang et al (2022) <arXiv:2209.08414>.

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1.00 score 178 downloads

landmix - Landmark Prediction for Mixture Data

Non-parametric prediction of survival outcomes for mixture data that incorporates covariates and a landmark time. Details are described in Garcia (2021) <doi:10.1093/biostatistics/kxz052>.

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1.00 score 159 downloads

OSsurvival - Assessing Surrogacy with a Censored Outcome

Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) <doi:10.1002/sim.9185>.

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1.00 score 3 scripts 167 downloads

OptimalSurrogate - Model Free Approach to Quantifying Surrogacy

Identifies an optimal transformation of a surrogate marker such that the proportion of treatment effect explained can be inferred based on the transformation of the surrogate and nonparametrically estimates two model-free quantities of this proportion. Details are described in Wang et al (2020) <doi:10.1093/biomet/asz065>.

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1.00 score 2 scripts 165 downloads