twoPhaseSens - Sensitivity Analysis for Covariates Observed in Two-Phase Samples

Provides a structured method-comparison sensitivity-analysis workflow for linear regression when important continuous adjustment covariates are observed only in a Phase-2 subsample. Implements naive analysis, complete-case analysis, fully conditional specification multiple imputation, joint-model multiple imputation, inverse probability weighting, and augmented inverse probability weighting, with standardized tables, diagnostics, and publication-style forest plots. Methodological background is provided by Little and Rubin (2019) <doi:10.1002/9781119482260> for general missing-data methods, White and Carlin (2010) <doi:10.1002/sim.3944> for complete-case analysis with missing covariates, White, Royston and Wood (2011) <doi:10.1002/sim.4067> and van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03> for chained-equations multiple imputation, Quartagno, Grund and Carpenter (2019) <doi:10.32614/RJ-2019-028> for joint-model multiple imputation, Robins, Rotnitzky and Zhao (1994) <doi:10.1080/01621459.1994.10476818> for inverse-probability weighting with missing regressors, and Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x> for doubly robust estimation.