Double Inverse-Weighted Estimation of Cumulative Treatment Effects Under Nonproportional Hazards and Dependent Censoring

DOI10.1111/j.1541-0420.2010.01449.xzbMath1216.62179OpenAlexW2016144426WikidataQ36022734 ScholiaQ36022734MaRDI QIDQ3008854

Guanghui Wei, Douglas E. Schaubel

Publication date: 22 June 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3372067

zbMATH Keywords

survival analysisrelative risktreatment effectinverse weightingcumulative hazardrestricted mean lifetime

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Medical applications (general) (92C50) Estimation in survival analysis and censored data (62N02)

Related Items (14)

Regression analysis of restricted mean survival time based on pseudo-observations for competing risks data ⋮ Estimating cumulative treatment effect under an additive hazards model ⋮ Restricted mean survival time as a function of restriction time ⋮ Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective ⋮ Weighted estimators of the complier average causal effect on restricted mean survival time with observed instrument–outcome confounders ⋮ Fully nonparametric survival analysis in the presence of time-dependent covariates and dependent censoring ⋮ Modeling restricted mean survival time under general censoring mechanisms ⋮ Adjusted log-rank test with double inverse weighting under dependent censoring ⋮ Semiparametric Analysis of Treatment Effect via Failure Probability Ratio and the Ratio of Cumulative Hazards ⋮ Two-sample tests for survival data from observational studies ⋮ Semiparametric inference on the absolute risk reduction and the restricted mean survival difference ⋮ On the restricted mean survival time curve in survival analysis ⋮ Modeling potential time to event data with competing risks ⋮ Semiparametric Inference on the Absolute Risk Reduction and the Restricted Mean Survival Difference

Cites Work

• Unnamed Item
• Unnamed Item
• Sensitivity analysis for unmeasured confounding in a marginal structural Cox proportional hazards model
• Addendum to ``A new approach to causal inference in mortality studies with a sustained exposure period. Application to control of the healthy worker survivor effect
• Nonparametric inference for a family of counting processes
• Towards a general asymptotic theory for Cox model with staggered entry
• Survival analysis. Techniques for censored and truncated data.
• Causal Inference on the Difference of the Restricted Mean Lifetime Between Two Groups
• Nonparametric Estimation from Incomplete Observations
• Multiple-Bias Modelling for Analysis of Observational Data
• Estimating Cumulative Treatment Effects in the Presence of Nonproportional Hazards
• Partial likelihood
• Restricted Mean Life with Covariates: Modification and Extension of a Useful Survival Analysis Method
• Estimation of Regression Coefficients When Some Regressors Are Not Always Observed
• Marginal Structural Models to Estimate the Joint Causal Effect of Nonrandomized Treatments
• Assessing the Sensitivity of Regression Results to Unmeasured Confounders in Observational Studies
• Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted (IPCW) Log‐Rank Tests
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
• Sensitivity Analysis of Unmeasured Confounding for the Causal Risk Ratio by Applying Marginal Structural Models
• Statistical models based on counting processes

This page was built for publication: Double Inverse-Weighted Estimation of Cumulative Treatment Effects Under Nonproportional Hazards and Dependent Censoring