ReviewLifetime data analysis2017
Nonparametric and semiparametric regression estimation for length-biased survival data.
Review in Lifetime data analysis, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- 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
- Conditional Independence Test of Failure and Truncation Times: Essential Tool for Method Selection.Computational statistics & data analysis · 2022Article
- A risk set adjustment for proportional hazards modeling of combined cohort data.Journal of applied statistics · 2022Article
- Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction.Lifetime data analysis · 2022Article
- CoxPhLb: An R Package for Analyzing Length Biased Data under Cox Model.The R journal · 2020Article
- 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
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
For the past several decades, nonparametric and semiparametric modeling for conventional right-censored survival data has been investigated intensively under a noninformative censoring mechanism. However, these methods may not be applicable for analyzing right-censored survival data that arise from prevalent cohorts when the failure times are subject to length-biased sampling. This review article is intended to provide a summary of some newly developed methods as well as established methods for analyzing length-biased data.
Indexed as
Identifiers
What OpenQuestion holds
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.