ArticleScandinavian journal of statistics, theory and applications2017
A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data.
Article in Scandinavian journal of statistics, theory and applications, 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
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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
9 citing papers in PubMed.
- H-Likelihood Approach on the Joint Frailty Model for Clustered Bivariate Survival Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- DEEP LEARNING OF SEMI-COMPETING RISK DATA VIA A NEW NEURAL EXPECTATION-MAXIMIZATION ALGORITHM.The annals of applied statistics · 2026Article
- An Overview and Recent Developments in the Analysis of Multistate Processes.Statistics in medicine · 2026Review
- SEMIPARAMETRIC ANALYSIS OF INTERVAL-CENSORED DATA SUBJECT TO INACCURATE DIAGNOSES WITH A TERMINAL EVENT.The annals of applied statistics · 2026Article
- Non-Parametric Estimation for Semi-Competing Risks Data With Event Misascertainment.Statistics in medicine · 2025Article
- The quasi-xgamma frailty model with survival analysis under heterogeneity problem, validation testing, and risk analysis for emergency care data.Scientific reports · 2024Article
- Simulating time-to-event data subject to competing risks and clustering: A review and synthesis.Statistical methods in medical research · 2023Review
- Estimation and inference for semi-competing risks based on data from a nested case-control study.Statistical methods in medical research · 2020Article
- Accelerated failure time models for semi-competing risks data in the presence of complex censoring.Biometrics · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
In the analysis of semi-competing risks data interest lies in estimation and inference with respect to a so-called non-terminal event, the observation of which is subject to a terminal event. Multi-state models are commonly used to analyse such data, with covariate effects on the transition/intensity functions typically specified via the Cox model and dependence between the non-terminal and terminal events specified, in part, by a unit-specific shared frailty term. To ensure identifiability, the frailties are typically assumed to arise from a parametric distribution, specifically a Gamma distribution with mean 1.0 and variance, say, σ
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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.