Estimating treatment effects on the marginal recurrent event mean in the presence of a terminating event

DOI10.1007/s10985-009-9149-xzbMath1322.62234OpenAlexW2098545663WikidataQ35998855 ScholiaQ35998855MaRDI QIDQ746073

Min Zhang, Douglas E. Schaubel

Publication date: 15 October 2015

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3364315

zbMATH Keywords

censoringimputationsemiparametric methodsmultivariate survival analysisinverse weightingmarginal mean

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of statistics to environmental and related topics (62P12) Censored data models (62N01)

Related Items (8)

Time-varying coefficients in a multivariate frailty model: application to breast cancer recurrences of several types and death ⋮ Computationally Efficient Marginal Models for Clustered Recurrent Event Data ⋮ Nonparametric Inference of General While-Alive Estimands for Recurrent Events ⋮ The Cox–Aalen model for recurrent‐event data with a dependent terminal event ⋮ Survival models and health sequences ⋮ Semiparametric temporal process regression of survival-out-of-hospital ⋮ Joint analysis of recurrent event data with additive-multiplicative hazards model for the terminal event time ⋮ Time-dependent prognostic score matching for recurrent event analysis to evaluate a treatment assigned during follow-up

Cites Work

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• Cox's regression model for counting processes: A large sample study
• A semiparametric additive rates model for recurrent event data
• Bayesian inference for causal effects: The role of randomization
• Towards a general asymptotic theory for Cox model with staggered entry
• Survival estimation and testing via multiple imputation.
• Weak convergence and empirical processes. With applications to statistics
• Multiple Imputation Methods for Estimating Regression Coefficients in the Competing Risks Model with Missing Cause of Failure
• Utilizing Propensity Scores to Estimate Causal Treatment Effects with Censored Time-Lagged Data
• Some Graphical Displays and Marginal Regression Analyses for Recurrent Failure Times and Time Dependent Covariates
• Flexible Estimation of Differences in Treatment-Specific Recurrent Event Means in the Presence of a Terminating Event
• Shared Frailty Models for Recurrent Events and a Terminal Event
• The central role of the propensity score in observational studies for causal effects
• On the regression analysis of multivariate failure time data
• Large-sample theory for parametric multiple imputation procedures
• Semiparametric Regression for the Mean and Rate Functions of Recurrent Events
• Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data
• Nonparametric Analysis of Recurrent Events and Death
• Some Simple Robust Methods for the Analysis of Recurrent Events
• Inference for imputation estimators
• Robust Estimation of Mean Functions and Treatment Effects for Recurrent Events Under Event-Dependent Censoring and Termination: Application to Skeletal Complications in Cancer Metastatic to Bone
• Multiple imputation methods for recurrent event data with missing event category
• Semiparametric Analysis of Correlated Recurrent and Terminal Events
• Statistical models based on counting processes

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