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NEWS
riskdiff 0.2.0 Major New Features: Causal Inference with IPTW ð¬ Inverse Probability of Treatment Weighting (IPTW)
calc_risk_diff_iptw(): Complete implementation for causal effect estimation in observational studies- Propensity Score Modeling: Logistic regression with comprehensive diagnostics and balance assessment
- Multiple Causal Estimands:
- ATE (Average Treatment Effect): Population-level causal effects
- ATT (Average Treatment Effect on Treated): Effects among those who received treatment
- ATC (Average Treatment Effect on Controls): Effects among those who did not receive treatment
- Weight Stabilization: Stabilized IPTW weights with optional trimming for extreme values
- Robust Inference: Bootstrap and sandwich estimator confidence intervals accounting for propensity score uncertainty
𧪠Causal Inference Diagnostics
- Covariate Balance Assessment: Standardized mean differences before and after weighting
- Effective Sample Size Calculation: Proper accounting for weight-induced variance inflation
- Propensity Score Overlap: Visual and numerical assessment of positivity assumption
- Weight Distribution Analysis: Comprehensive diagnostics for extreme weights
- Balance Tables: Publication-ready covariate balance summaries
Enhanced Statistical Methods Boundary Detection System
- Comprehensive Detection: Automatic identification of statistical boundary conditions including:
- Upper bound issues (fitted probabilities near 1)
- Lower bound issues (fitted probabilities near 0)
- Separation and quasi-separation scenarios
- Integration with IPTW for robust causal estimation
- Enhanced Confidence Intervals: Robust interval estimation methods for boundary cases using profile likelihood
- Automatic Fallback: Intelligent model selection with detailed convergence diagnostics
Improved Core Functionality
- Enhanced Missing Data Handling: More sophisticated approaches to incomplete covariate data
- Improved Convergence Diagnostics: Better detection and handling of model fitting challenges
- Enhanced Validation: More comprehensive input validation and informative error messages
- Performance Optimization: Improved computational efficiency for large epidemiological datasets
Testing and Quality Assurance Comprehensive Test Suite (322+ Tests, Zero Failures)
- IPTW-Specific Testing: Extensive validation of causal inference methods including:
- Propensity score model fitting under various scenarios
- Weight calculation and stabilization accuracy
- Covariate balance assessment correctness
- Bootstrap confidence interval coverage properties
- Boundary Condition Stress Testing: Rigorous validation of challenging statistical scenarios
- Missing Data Torture Tests: Extensive validation across multiple missing data patterns
- Real-World Dataset Integration: Full compatibility testing with complex epidemiological data
- Performance Testing: Validation with large datasets and complex stratification
Statistical Validation
- Simulation Studies: Validated against known data-generating processes with various confounding patterns
- Literature Benchmarks: Compared against established causal inference methods and results
- Balance Assessment: Comprehensive validation of covariate balance evaluation methods
- Bootstrap Coverage: Empirical validation of confidence interval coverage properties
Documentation and Examples Enhanced Documentation
- Causal Inference Methodology: Detailed explanation of IPTW theory and implementation
- Practical Examples: Real-world applications using
cachar_sample dataset
- Best Practices Guide: Recommendations for observational study analysis
- Diagnostic Interpretation: How to assess and interpret covariate balance and weight diagnostics
Updated Examples
- Observational Studies: Complete workflow from confounding assessment to causal effect estimation
- RCT Analysis: Baseline prognostic factor adjustment in randomized trials
- Sensitivity Analysis: Approaches for assessing robustness to unmeasured confounding
- Publication-Ready Output: Formatted tables and visualizations for research dissemination
Dataset Integration Enhanced cachar_sample Dataset
- Full IPTW Compatibility: Dataset optimized for demonstrating causal inference methods
- Realistic Confounding Patterns: Authentic relationships between covariates, treatments, and outcomes
- Missing Data Scenarios: Representative patterns for testing missing data handling
- Multiple Treatment Variables: Support for various causal questions and estimands
API and Interface New Functions
calc_risk_diff_iptw(): Main IPTW causal effect estimation functioncalc_iptw_weights(): Propensity score estimation and weight calculationassess_balance(): Covariate balance evaluation before and after weighting- Enhanced print methods: Specialized output formatting for causal inference results
Enhanced Existing Functions
calc_risk_diff(): Improved boundary detection and convergence handlingformat_risk_diff(): Enhanced formatting with boundary condition informationcreate_rd_table(): Support for IPTW results and causal inference formatting
Statistical Foundation Literature Integration
All causal inference methods implemented according to established best practices:
- Hernán & Robins (2020): Modern causal inference methodology
- Rosenbaum & Rubin (1983): Propensity score theory and application
- Austin (2011): IPTW implementation best practices
- Lunceford & Davidian (2004): Estimation methods for causal effects
- Cole & Hernán (2008): Constructing inverse probability weights
Methodological Rigor
- Assumption Checking: Tools for assessing key causal inference assumptions
- Sensitivity Analysis: Framework for evaluating robustness to violations
- Effect Modification: Support for subgroup analyses with proper causal interpretation
- Publication Standards: Output formatted according to epidemiological reporting guidelines
Performance and Scalability Computational Efficiency
- Large Dataset Support: Optimized for epidemiological cohorts with 10,000+ observations
- Memory Management: Efficient handling of weight calculations and bootstrap procedures
- Parallel Processing: Support for multi-core bootstrap confidence interval calculation
- Progress Tracking: User feedback for long-running causal inference procedures
Numerical Stability
- Robust Weight Calculation: Stable computation even with extreme propensity scores
- Overflow Protection: Safe handling of very large or small weights
- Convergence Monitoring: Comprehensive diagnostics for propensity score model fitting
- Boundary Integration: Seamless handling of boundary conditions in causal estimation
riskdiff 0.1.0 Initial CRAN Release
[Previous version content remains unchangedâ¦]
Development Philosophy
The riskdiff package bridges the gap between traditional epidemiological methods and modern causal inference, making sophisticated statistical techniques accessible to public health researchers worldwide. Version 0.2.0 aims to democratise causal inference for global health research.
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