PSsurvival 0.2.1

New features

• marCoxph() and surveff() fits can now be pooled across multiply imputed datasets with the standard mice workflow, pool(with(imp, ...)). This is enabled by new tidy() and df.residual() S3 methods for both classes. See the “Multiple imputation with mice” section of the main vignette.

PSsurvival 0.2.0

Breaking Changes

• API Unification: Changed estimand parameter to weight_method in surveff() and marCoxph() for consistency across all package functions
  • estimand = "ATE" → weight_method = "IPW"
  • estimand = "overlap" → weight_method = "OW"
  • estimand = "ATT" → weight_method = "ATT" (name unchanged, but parameter name changed)
• Changed trim parameter from character to logical in surveff(), marCoxph(), and weightedKM()
  • trim = "symmetric" → trim = TRUE
  • trim = NULL → trim = FALSE
• Removed alpha parameter and asymmetric trimming support from all functions due to poor statistical performance in practice
  • Only symmetric trimming (Crump extension for multiple treatments) is now supported
• Default delta is now NULL (automatic selection based on number of treatment groups) instead of a fixed value

New Features

• New function: weightedKM() for weighted Kaplan-Meier estimation with propensity score weights
  • Supports IPW, overlap weights, ATT, and classical unweighted Kaplan-Meier
  • Uses classic weighted Greenwood variance formula
  • Handles binary and multiple treatment groups
• New S3 methods for weightedKM objects:
  • plot.weightedKM(): Visualize weighted Kaplan-Meier or cumulative risk curves with confidence intervals
  • summary.weightedKM(): Tabular summaries with confidence intervals
  • print.weightedKM(): Formatted console output
• Risk table support in plot.weightedKM():
  • Display number at risk and cumulative events below survival/risk curves
  • Customizable statistics, breaks, height, and font size
  • Automatic group name positioning and formatting
• Enhanced visualization options:
  • Three confidence interval types: plain, log, log-log (default)
  • Cumulative risk plots (1 - survival)
  • Customizable colors, widths, transparency, and legends

Documentation

• Unified weighting method descriptions across all main functions (surveff(), marCoxph(), weightedKM())
• Added comprehensive @details sections explaining IPW, OW, and ATT weighting methods
• Updated all examples to use new API with weight_method parameter

Internal

• Moved ggplot2 and cowplot from Suggests to Imports (required for plotting functions)
• Parameter mapping layer in surveff() and marCoxph() to transform user-facing API to internal implementation

Migration Guide for Existing Users

If upgrading from version 0.1.0:

• Replace estimand = "ATE" with weight_method = "IPW"
• Replace estimand = "overlap" with weight_method = "OW"
• Replace estimand = "ATT" with weight_method = "ATT"
• Replace trim = "symmetric", delta = 0.1 with trim = TRUE, delta = 0.1
• Replace trim = NULL with trim = FALSE (or simply omit, as FALSE is default)
• Remove alpha parameter entirely (asymmetric trimming no longer supported)
• Set delta = NULL to use automatic defaults (0.1 for binary, 0.067 for 3 groups, 1/(2J) for J ≥ 4)

Example migration:

# Old (v0.1.0)
result <- surveff(
  data = mydata,
  ps_formula = Z ~ X1 + X2,
  censoring_formula = Surv(time, event) ~ X1,
  estimand = "overlap",
  censoring_method = "weibull"
)

# New (v0.2.0)
result <- surveff(
  data = mydata,
  ps_formula = Z ~ X1 + X2,
  censoring_formula = Surv(time, event) ~ X1,
  weight_method = "OW",
  censoring_method = "weibull"
)

PSsurvival 0.1.0

• Initial CRAN release

• Documentation improvements for CRAN resubmission:

  • Added @return tags to print methods
  • Removed examples from internal functions
  • Updated examples to use \donttest{} with executable code
  • Enhanced output documentation for all exported functions

• Implements propensity score weighting methods for estimating counterfactual survival functions and marginal hazard ratios in observational studies with time-to-event outcomes

• Main functions:

  • surveff(): Estimates counterfactual survival curves and survival differences over time
  • marCoxph(): Estimates marginal hazard ratios via weighted Cox regression

• Supports binary and multiple (>2) treatment groups

• Weighting methods:

  • Inverse probability weighting (ATE)
  • Treated/target group weighting (ATT)
  • Overlap weighting

• Propensity score trimming:

  • Symmetric trimming (Crump et al.)
  • Asymmetric trimming (Sturmer et al.)

• Censoring adjustment methods:

  • Weibull accelerated failure time models
  • Cox proportional hazards models

• Variance estimation:

  • Analytical M-estimation (binary treatment with Weibull censoring)
  • Bootstrap (all configurations, with full or stratified resampling)

• Comprehensive documentation including vignette with examples

• Based on methods from Cheng et al. (2022) doi:10.1093/aje/kwac043 and Li & Li (2019) doi:10.1214/19-AOAS1282