Pattern–mixture and selection models for analysing longitudinal data with monotone missing patterns

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Global sensitivity analysis for repeated measures studies with informative drop‐out: A semi‐parametric approach (2018, background): “A weaker version of this assumption, termed sequential ignorability (SI), posits that the withdrawal decision depends on outcomes through visit k, but not the outcome at the last scheduled study visit (Birmingham et al., 2003). MAR yields identification of the entire joint distribution of the outcom”
Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap (2017, methodology): “Robustness of the IPWtype estimators to unmeasured confounding bias are usually studied using pattern-mixture models (Birmingham et al., 2003) or selection models (Scharfstein et al.”
Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing (2021, result): “Similar results appear in Birmingham, Rotnitzky, and Fitzmaurice (2003), Robins, Rotnitzky, and Scharfstein (2000), Tan (2006), Graham (2011), Hristache and Patilea (2017), Franks, Amour, and Feller (2020), and Zhao, Small, and Bhattacharya (2019).”