ArticleStatistics in medicine2013
A semi-Markov model for stroke with piecewise-constant hazards in the presence of left, right and interval censoring.
Article in Statistics in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- A Comparison of Methods for Modeling Multistate Cancer Progression Using Screening Data with Censoring after Intervention.Medical decision making : an international journal of the Society for Medical Decision Making · 2026Article
- Risk estimation and dynamic prediction using discrete-time joint models for longitudinal and multistate data with interval and state censoring.Biostatistics (Oxford, England) · 2026Article
- A progressive three-state model to estimate time to cancer: a likelihood-based approach.BMC medical research methodology · 2022Article
- Stroke to Dementia Associated with Environmental Risks-A Semi-Markov Model.International journal of environmental research and public health · 2020Article
- Comparing Strategies to Prevent Stroke and Ischemic Heart Disease in the Tunisian Population: Markov Modeling Approach Using a Comprehensive Sensitivity Analysis Algorithm.Computational and mathematical methods in medicine · 2019Article
- Semi-Markov models for interval censored transient cognitive states with back transitions and a competing risk.Statistical methods in medical research · 2016Article
- Using semi-Markov processes to study timeliness and tests used in the diagnostic evaluation of suspected breast cancer.Statistics in medicine · 2016Article
- Measuring concurrency using a joint multistate and point process model for retrospective sexual history data.Statistics in medicine · 2016Article
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
This paper presents a parametric method of fitting semi-Markov models with piecewise-constant hazards in the presence of left, right and interval censoring. We investigate transition intensities in a three-state illness-death model with no recovery. We relax the Markov assumption by adjusting the intensity for the transition from state 2 (illness) to state 3 (death) for the time spent in state 2 through a time-varying covariate. This involves the exact time of the transition from state 1 (healthy) to state 2. When the data are subject to left or interval censoring, this time is unknown. In the estimation of the likelihood, we take into account interval censoring by integrating out all possible times for the transition from state 1 to state 2. For left censoring, we use an Expectation-Maximisation inspired algorithm. A simulation study reflects the performance of the method. The proposed combination of statistical procedures provides great flexibility. We illustrate the method in an application by using data on stroke onset for the older population from the UK Medical Research Council Cognitive Function and Ageing Study.
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.