2023
An inverse probability weighted regression method that accounts for right‐censoring for causal inference with multiple treatments and a binary outcome
Yu Y, Zhang M, Mukherjee B. An inverse probability weighted regression method that accounts for right‐censoring for causal inference with multiple treatments and a binary outcome. Statistics In Medicine 2023, 42: 3699-3715. PMID: 37392070, DOI: 10.1002/sim.9826.Peer-Reviewed Original ResearchConceptsRight censoringWeighted score functionCausal treatment effectsAverage treatment effectAsymptotic propertiesCensored componentPre-specified time windowEstimation consistencyRobustness propertiesSimulation studyBinary outcomesPresence of confoundersCensoringScoring functionInverse probabilityTreatment effectsEstimationSources of biasInferenceLetter CComparative effectiveness researchTreatment switchRegression methodLogistic regression modelsInsurance claims database
2020
Veridical causal inference using propensity score methods for comparative effectiveness research with medical claims
Ross R, Shi X, Caram M, Tsao P, Lin P, Bohnert A, Zhang M, Mukherjee B. Veridical causal inference using propensity score methods for comparative effectiveness research with medical claims. Health Services And Outcomes Research Methodology 2020, 21: 206-228. PMID: 34040495, PMCID: PMC8142944, DOI: 10.1007/s10742-020-00222-8.Peer-Reviewed Original ResearchPropensity score methodsEstimate causal treatment effectsClaims-based studyCausal treatment effectsMedical insurance claimsPrivate payersScoring methodData Mart DatabaseMedical claimsSub-cohortEffectiveness researchPopulation-based inferenceInsurance claimsSelection biasCausal inferenceCompare findingsOnline version