ArticleLifetime data analysis2018
Survival models and health sequences.
Article in Lifetime data analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Backward joint model for the joint dynamic prediction of time-to-event and longitudinal data: basic formulation and new developments.Lifetime data analysis · 2026Article
- A Comparison of Testing Procedures for Detecting Short- and Long-Term Effects of Biomarkers for Cancer Screening.Statistics in medicine · 2026Article
- SPARCC: Semi-Parametric Robust Estimation in a Right-Censored Covariate Model.Journal of the American Statistical Association · 2026Article
- Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks.Statistics in medicine · 2024Article
- Joint modeling in presence of informative censoring on the retrospective time scale with application to palliative care research.Biostatistics (Oxford, England) · 2024Article
- Kernel Estimation of Bivariate Time-varying Coefficient Model for Longitudinal Data with Terminal Event.Journal of the American Statistical Association · 2024Article
- Landmarking 2.0: Bridging the gap between joint models and landmarking.Statistics in medicine · 2022Article
- Risk factors associated with readmissions of patients with severe mental disorders under treatment with antipsychotics.BMC psychiatry · 2022Article
- Exchangeable Markov multi-state survival processes.Statistica Sinica · 2021Article
- Commentary: Alignment of time scales and joint models.Lifetime data analysis · 2018Article
- A unified framework of demographic time.Genus · 2017Article
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Authors and funding
2 authors.
Funding
Abstract
Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Robust health is ordinarily associated with longer survival, so the two parts of a survival process cannot be assumed independent. This paper is concerned with a general technique-reverse alignment-for constructing statistical models for survival processes, here termed revival models. A revival model is a regression model in the sense that it incorporates covariate and treatment effects into both the distribution of survival times and the joint distribution of health outcomes. The revival model also determines a conditional survival distribution given the observed history, which describes how the subsequent survival distribution is determined by the observed progression of health outcomes.
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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.