ArticleBiometrics2021
Variance estimation in inverse probability weighted Cox models.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it.
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- Gender-specific outcomes in immune checkpoint inhibitor therapy for advanced or metastatic urothelial cancer: a systematic review and meta-analysis.Journal of cancer research and clinical oncology · 2023Pooled it
- Marginal structural models for multilevel clustered data.Biostatistics (Oxford, England) · 2022Pooled it
- Nocturnal Oxygen Therapy for Central Sleep Apnea in Patients with Heart Failure: A Multisite, Double-Blind, Sham-controlled Randomized Clinical Trial (LOFT-HF).Annals of the American Thoracic Society · 2025Trial
- A Frequentist Approach Using Weighted Estimating Equations for Survival Analysis of Randomized Controlled Trial With External Control Arm.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Post-acute organ complications within one year following COVID-19 hospitalization and related socioeconomic inequalities.Nature communications · 2026Article
- Comparative Effectiveness of Current Glucose-lowering Medications in Type 2 Diabetes Mellitus: Emulation of a Modified GRADE Trial.Journal of general internal medicine · 2026Article
- When to Start, How to Classify, and Whom to Include: A Methodological Assessment and Illustrative Example of the Clone-Censor-Weight Approach for Evaluating Intervention Timing in Observational Studies.Clinical epidemiology · 2026Article
- Plasma metabolic landscape unveils key regulators of leukemia subtype progression.Future science OA · 2025Article
- Inference procedures in sequential trial emulation with survival outcomes: Comparing confidence intervals based on the sandwich variance estimator, bootstrap and jackknife.Statistical methods in medical research · 2025Article
- Longterm efficacy of a patient focused intervention in patients with asplenia- a three year follow-up of the PrePPS trial.Infection · 2025Article
- The Cox-Pólya-Gamma algorithm for flexible Bayesian inference of multilevel survival models.Biometrics · 2025Article
- Comparison of the Risk of Pneumonia Between Fluticasone Furoate/Umeclidinium/Vilanterol and Multiple-Inhaler Triple Therapy in Patients with COPD Using Health Insurance Claims Data: Final Analysis of Post-Marketing Database Surveillance in Japan.Journal of clinical medicine · 2025Article
- Efficacy of cabozantinib and sunitinib for the treatment of intermediate/poor risk renal cell carcinoma based upon UK real-world data.ESMO real world data and digital oncology · 2024Article
- How estimating nuisance parameters can reduce the variance (with consistent variance estimation).Statistics in medicine · 2024Article
- Air pollution and serious bleeding events in high-risk older adults.Environmental research · 2024Article
- Higher methadone dose at time of release from prison predicts linkage to maintenance treatment for people with HIV and opioid use disorder transitioning to the community in Malaysia.The International journal on drug policy · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Inverse probability weighted Cox models can be used to estimate marginal hazard ratios under different point treatments in observational studies. To obtain variance estimates, the robust sandwich variance estimator is often recommended to account for the induced correlation among weighted observations. However, this estimator does not incorporate the uncertainty in estimating the weights and tends to overestimate the variance, leading to inefficient inference. Here we propose a new variance estimator that combines the estimation procedures for the hazard ratio and weights using stacked estimating equations, with additional adjustments for the sum of terms that are not independently and identically distributed in a Cox partial likelihood score equation. We prove analytically that the robust sandwich variance estimator is conservative and establish the asymptotic equivalence between the proposed variance estimator and one obtained through linearization by Hajage et al. in 2018. In addition, we extend our proposed variance estimator to accommodate clustered data. We compare the finite sample performance of the proposed method with alternative methods through simulation studies. We illustrate these different variance methods in both independent and clustered data settings, using a bariatric surgery dataset and a multiple readmission dataset, respectively. To facilitate implementation of the proposed method, we have developed an
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