ArticleLifetime data analysis2019
A class of semiparametric cure models with current status data.
Article in Lifetime data analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Enhanced doubly robust estimation with concave link functions for estimands in clinical trials.Journal of nonparametric statistics · 2024Article
- The analysis of COVID-19 in-hospital mortality: A competing risk approach or a cure model?Statistical methods in medical research · 2022Article
- Exposure assessment for Cox proportional hazards cure models with interval-censored survival data.Biometrical journal. Biometrische Zeitschrift · 2022Article
- Regression analysis of current status data with latent variables.Lifetime data analysis · 2021Article
- Semiparametric frailty models for zero-inflated event count data in the presence of informative dropout.Biometrics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Current status data occur in many biomedical studies where we only know whether the event of interest occurs before or after a particular time point. In practice, some subjects may never experience the event of interest, i.e., a certain fraction of the population is cured or is not susceptible to the event of interest. We consider a class of semiparametric transformation cure models for current status data with a survival fraction. This class includes both the proportional hazards and the proportional odds cure models as two special cases. We develop efficient likelihood-based estimation and inference procedures. We show that the maximum likelihood estimators for the regression coefficients are consistent, asymptotically normal, and asymptotically efficient. Simulation studies demonstrate that the proposed methods perform well in finite samples. For illustration, we provide an application of the models to a study on the calcification of the hydrogel intraocular lenses.
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Registered trials
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