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.

Last updated 2 years ago

3.12 score 4 packages 11 scripts 220 downloads

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.

Last updated 10 months ago

2.30 score 1 stars 194 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.

Last updated 1 years ago

2.00 score 146 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.

Last updated 3 years ago

2.00 score 1 stars 219 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>.

Last updated 3 years ago

2.00 score 1 stars 150 downloads

landpred - Landmark Prediction of a Survival Outcome

Provides functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307, <doi:10.1002/bimj.201000150>.

Last updated 1 years ago

1.82 score 2 packages 11 scripts 220 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>.

Last updated 2 years ago

1.52 score 1 packages 11 scripts 178 downloads

landest - Landmark Estimation of Survival and Treatment Effect

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

Last updated 1 years ago

1.08 score 12 scripts 251 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>.

Last updated 3 years ago

1.04 score 11 scripts 183 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>.

Last updated 2 years ago

1.00 score 151 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>.

Last updated 2 years ago

1.00 score 176 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>.

Last updated 2 years ago

1.00 score 3 scripts 148 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>.

Last updated 2 years ago

1.00 score 165 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>.

Last updated 2 years ago

1.00 score 2 scripts 154 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>.

Last updated 2 years ago

1.00 score 206 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, Tian, Cai (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1>. The main functions are (1) R.q.event() which calculates the proportion of the treatment effect (the difference in restricted mean survival time at time t) explained by surrogate outcome information observed up to a selected landmark time, (2) R.t.estimate() which calculates the proportion of the treatment effect explained by primary outcome information only observed up to a selected landmark time, and (3) IV.event() which calculates the incremental value of the surrogate outcome information.

Last updated 3 years ago

1.00 score 150 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.

Last updated 3 years ago

1.00 score 159 downloads