Utilizing Propensity Scores to Estimate Causal Treatment Effects with Censored Time-Lagged Data

DOI10.1111/j.0006-341X.2001.01207.xzbMath1209.62217OpenAlexW2041834303WikidataQ32052853 ScholiaQ32052853MaRDI QIDQ3078885

Kevin J. Anstrom, Anastasios A. Tsiatis

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.2001.01207.x

zbMATH Keywords

survival analysiscensoringKaplan-Meierconfoundingpropensity scoreobservational studyinverse weighting

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)

Related Items (5)

Semiparametric estimation of treatment effect with time-lagged response in the presence of informative censoring ⋮ Unnamed Item ⋮ Propensity Score Matching with Time‐Dependent Covariates ⋮ Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data ⋮ Estimating treatment effects on the marginal recurrent event mean in the presence of a terminating event

Cites Work

• Bayesian inference for causal effects: The role of randomization
• Nonparametric Estimation from Incomplete Observations
• From Association to Causation in Observational Studies: The Role of Tests of Strongly Ignorable Treatment Assignment
• The central role of the propensity score in observational studies for causal effects
• Survey Nonresponse Adjustments for Estimates of Means
• Model-Based Direct Adjustment
• Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments
• Estimating medical costs with censored data
• A Generalization of Sampling Without Replacement From a Finite Universe

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