ArticleJournal of the American Statistical Association2011
Maximum Likelihood Estimations and EM Algorithms with Length-biased Data.
Article in Journal of the American Statistical Association, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
29 citing papers in PubMed.
- Saddlepoint approximations for linear rank tests with left-truncated, right-censored, and cross-sectional data under randomized block design.Scientific reports · 2026Article
- Clinical, pathological, and prognostic features of invasive pyelocaliceal urothelial carcinoma with venous thrombus: A propensity score-matched retrospective cohort study.Asian journal of urology · 2026Article
- Pseudo-Observation Approach for Length-Biased Cox Proportional Hazards Model.Biometrical journal. Biometrische Zeitschrift · 2025Article
- A pairwise pseudo-likelihood approach for regression analysis of left-truncated failure time data with various types of censoring.BMC medical research methodology · 2023Article
- Survival Modelling For Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis using Electronic Health Records.International statistical review = Revue internationale de statistique · 2023Article
- Cox regression model under dependent truncation.Biometrics · 2022Article
- Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction.Lifetime data analysis · 2022Article
- A risk set adjustment for proportional hazards modeling of combined cohort data.Journal of applied statistics · 2022Article
- Incorporating survival data into case-control studies with incident and prevalent cases.Statistics in medicine · 2021Article
- Augmented likelihood for incorporating auxiliary information into left-truncated data.Lifetime data analysis · 2021Article
- A Bayesian semiparametric method for analyzing length-biased data.Journal of applied statistics · 2021Article
- Nonparametric and semiparametric estimators of restricted mean survival time under length-biased sampling.Lifetime data analysis · 2020Article
- CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model.The R journal · 2020Article
- Semiparametric Model for Bivariate Survival Data Subject to Biased Sampling.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2019Article
- Article
- Model diagnostics for the proportional hazards model with length-biased data.Lifetime data analysis · 2019Review
- Platinum-based concurrent chemotherapy remains the optimal regimen for nasopharyngeal carcinoma: a large institutional-based cohort study from an endemic area.Journal of cancer research and clinical oncology · 2018Article
- A nonparametric maximum likelihood approach for survival data with observed cured subjects, left truncation and right-censoring.Lifetime data analysis · 2018Article
- Analysis of restricted mean survival time for length-biased data.Biometrics · 2018Article
- Missing Information Principle: A Unified Approach for General Truncated and Censored Survival Data Problems.Statistical science : a review journal of the Institute of Mathematical Statistics · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Length-biased sampling has been well recognized in economics, industrial reliability, etiology applications, epidemiological, genetic and cancer screening studies. Length-biased right-censored data have a unique data structure different from traditional survival data. The nonparametric and semiparametric estimations and inference methods for traditional survival data are not directly applicable for length-biased right-censored data. We propose new expectation-maximization algorithms for estimations based on full likelihoods involving infinite dimensional parameters under three settings for length-biased data: estimating nonparametric distribution function, estimating nonparametric hazard function under an increasing failure rate constraint, and jointly estimating baseline hazards function and the covariate coefficients under the Cox proportional hazards model. Extensive empirical simulation studies show that the maximum likelihood estimators perform well with moderate sample sizes and lead to more efficient estimators compared to the estimating equation approaches. The proposed estimates are also more robust to various right-censoring mechanisms. We prove the strong consistency properties of the estimators, and establish the asymptotic normality of the semi-parametric maximum likelihood estimators under the Cox model using modern empirical processes theory. We apply the proposed methods to a prevalent cohort medical study. Supplemental materials are available online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.