ROBust INference for Covariate Adjustment in Randomized Clinical Trials
Prediction Bias
Block Sum of a matrix
Confidence Interval
Contrast Functions and Jacobians
Derive Outcome Values Based on Log Hazard Ratio
Find Data in a Fit
Get Linear Model Input Data
Calculate Coefficient Estimates and Corresponding Residuals from Linea...
Obtain Adjustment for Proportion of Treatment Assignment
Confidence interval calculations which are common across effect result...
Prepare Events Table
Check Whether First Factor is Nested in Second Factor
Obtain Adjustment for Covariance Matrix
Extract Variable Names
Evaluate if Interaction Exists
Log Hazard Ratio Coefficient Matrix
Estimate Log Hazard Ratio via Score Function
Log-Rank Test via Score Function
Count Number of Events per Unique Event Time
Prepare Survival Input
Log-Rank Test Results Matrix
Check Unbiased Means of Residuals Across Randomization Strata and Trea...
Obtain the Jacobian matrix
Counterfactual Prediction
S3 Methods for prediction_cf
Randomization schema
Covariate adjusted glm model
Covariate adjusted lm model
Log Hazard Ratio Estimation and Log-Rank Test via Score Function
Covariate Adjusted and Stratified Survival Analysis
RobinCar2 Package
Sum vectors in a list
S3 Methods for surv_effect
Survival Comparison Functions
Log-Rank Score Functions for Survival Analysis
Treatment Effect
Update levels in a contrast pair
Generalized Covariance (ANHECOVA)
Heteroskedasticity-consistent covariance matrix for predictions
Performs robust estimation and inference when using covariate adjustment and/or covariate-adaptive randomization in randomized controlled trials. This package is trimmed to reduce the dependencies and validated to be used across industry. See "FDA's final guidance on covariate adjustment"<https://www.regulations.gov/docket/FDA-2019-D-0934>, Tsiatis (2008) <doi:10.1002/sim.3113>, Bugni et al. (2018) <doi:10.1080/01621459.2017.1375934>, Ye, Shao, Yi, and Zhao (2023)<doi:10.1080/01621459.2022.2049278>, Ye, Shao, and Yi (2022)<doi:10.1093/biomet/asab015>, Rosenblum and van der Laan (2010)<doi:10.2202/1557-4679.1138>, Wang et al. (2021)<doi:10.1080/01621459.2021.1981338>, Ye, Bannick, Yi, and Shao (2023)<doi:10.1080/24754269.2023.2205802>, and Bannick, Shao, Liu, Du, Yi, and Ye (2024)<doi:10.48550/arXiv.2306.10213>.