Propensity Score Methods for Survival Analysis
Propensity Score Weighting for PSsurvival Package
Calculate Overlap Weights
Check Data Structure
Check if Variables Exist in Data
Survival Effect Estimation with Weibull Censoring Scores
Estimate Censoring Scores Using Cox Regression
Censoring Score Estimation
Propensity Score Estimation for PSsurvival Package
Weighted Kaplan-Meier Estimation with Classic Greenwood Variance
Estimate Propensity Score Weights
Marginal Cox Model Estimation with Propensity Score Weighting
Generate Bootstrap Sample Indices
Marginal Cox Model with Propensity Score Weighting
Plot Method for surveff Objects
Plot Method for Weighted Kaplan-Meier Estimates
Print Method for marCoxph Objects
Print Method for surveff Objects
Print Method for Weighted Kaplan-Meier Estimates
Summary Method for marCoxph Objects
Summary Method for surveff Objects
Summary Method for Weighted Kaplan-Meier Estimates
Survival Effect Estimation with Cox Censoring Scores
Estimate Counterfactual Survival Functions Using Weibull Censoring Sco...
Wrapper for Cox Survival Effect Estimation with Variance
Wrapper for Weibull Survival Effect Estimation with Variance
Survival Effect Estimation with Propensity Score Weighting
Asymmetric Propensity Score Trimming (Sturmer Extension)
Symmetric Propensity Score Trimming (Crump Extension)
Validate Censoring Formula
Validate Data Variables
Validate Method Arguments
Data Validation Functions for PSsurvival Package
Validate All Inputs for PSsurvdiff
Bootstrap Variance Estimation for Marginal Cox Model
Bootstrap Variance Estimation for Cox Survival Functions
Compute Analytical M-Estimation Variance for Binary Treatment Survival...
Bootstrap Variance Estimation for Weibull Survival Functions
Weighted Kaplan-Meier Estimation with Propensity Score Weights
Implements propensity score weighting methods for estimating counterfactual survival functions, marginal hazard ratios, and weighted Kaplan-Meier and cumulative risk curves in observational studies with time-to-event outcomes. Supports binary and multiple treatment groups with inverse probability of treatment weighting (IPW), overlap weighting (OW), and average treatment effect on the treated (ATT). Includes symmetric trimming (Crump extension) for extreme propensity scores. Variance estimation via analytical M-estimation or bootstrap. Methods based on Li et al. (2018) <doi:10.1080/01621459.2016.1260466>, Li & Li (2019) <doi:10.1214/19-AOAS1282>, and Cheng et al. (2022) <doi:10.1093/aje/kwac043>.