Relevant bibliographies by topics / Non-informative censoring

Academic literature on the topic 'Non-informative censoring'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Non-informative censoring"

1

Arboretti, Rosa, Roberto Fontana, Fortunato Pesarin, and Luigi Salmaso. "Nonparametric combination tests for comparing two survival curves with informative and non-informative censoring." Statistical Methods in Medical Research 27, no. 12 (June 28, 2017): 3739–69. http://dx.doi.org/10.1177/0962280217710836.

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This paper looks at permutation methods used to deal with hypothesis testing within the survival analysis framework. In the literature, several attempts have been made to deal with the comparison of survival curves and, depending on the survival and hazard functions of two groups, they can be more or less efficient in detecting differences. Furthermore, in some situations, censoring can be informative in that it depends on treatment effect. Our proposal is based on the nonparametric combination approach and has proven to be very effective under different configurations of survival and hazard functions. It allows the practitioner to test jointly on primary and censoring events and, by using multiple testing methods, to assess the significance of the treatment effect separately on the survival and the censoring process.
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2

Nishikawa, Masako, Toshiro Tango, and Makiko Ogawa. "Non-parametric inference of adverse events under informative censoring." Statistics in Medicine 25, no. 23 (March 8, 2006): 3981–4003. http://dx.doi.org/10.1002/sim.2511.

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3

López-López, José A., Jonathan A. C. Sterne, and Julian P. T. Higgins. "Selection bias introduced by informative censoring in studies examining effects of vaccination in infancy." International Journal of Epidemiology 48, no. 6 (May 9, 2019): 2001–9. http://dx.doi.org/10.1093/ije/dyz092.

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Abstract Background Many studies have examined ‘non-specific’ vaccine effects on infant mortality: attention has been particularly drawn to diphtheria-tetanus-pertussis (DTP) vaccine, which has been proposed to be associated with an increased mortality risk. Both right and left censoring are common in such studies. Methods We conducted simulation studies examining right censoring (at measles vaccination) and left censoring (by excluding early follow-up) in a variety of scenarios in which confounding was and was not present. We estimated both unadjusted and adjusted hazard ratios (HRs), averaged across simulations. Results We identified scenarios in which right-censoring at measles vaccination was informative and so introduced bias in the direction of a detrimental effect of DTP vaccine. In some, but not all, situations, adjusting for confounding by health status removed the bias caused by censoring. However, such adjustment will not always remove bias due to informative censoring: inverse probability weighting was required in one scenario. Bias due to left censoring arose when both health status and DTP vaccination were associated with mortality during the censored early follow-up and was in the direction of attenuating a beneficial effect of DTP on mortality. Such bias was more severe when the effect of DTP changed over time. Conclusions Estimates of non-specific effects of vaccines may be biased by informative right or left censoring. Authors of studies estimating such effects should consider the potential for such bias and use appropriate statistical approaches to control for it. Such approaches require measurement of prognostic factors that predict censoring.
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Ungolo, Francesco, and Edwin R. van den Heuvel. "Inference on latent factor models for informative censoring." Statistical Methods in Medical Research 31, no. 5 (January 25, 2022): 801–20. http://dx.doi.org/10.1177/09622802211057290.

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This work discusses the problem of informative censoring in survival studies. A joint model for the time to event and the time to censoring is presented. Their hazard functions include a latent factor in order to identify this joint model without sacrificing the flexibility of the parametric specification. Furthermore, a fully Bayesian formulation with a semi-parametric proportional hazard function is provided. Similar latent variable models have been described in literature, but here the emphasis is on the performance of the inferential task of the resulting mixture model with unknown number of components. The posterior distribution of the parameters is estimated using Hamiltonian Monte Carlo methods implemented in Stan. Simulation studies are provided to study its performance and the methodology is implemented for the analysis of the ACTG175 clinical trial dataset yielding a better fit. The results are also compared to the non-informative censoring case to show that ignoring informative censoring may lead to serious biases.
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Yu, Mengzhu, Yanqin Feng, Ran Duan, and Jianguo Sun. "Regression analysis of multivariate interval-censored failure time data with informative censoring." Statistical Methods in Medical Research 31, no. 3 (December 8, 2021): 391–403. http://dx.doi.org/10.1177/09622802211061668.

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Regression analysis of multivariate interval-censored failure time data has been discussed by many authors1–6. For most of the existing methods, however, one limitation is that they only apply to the situation where the censoring is non-informative or the failure time of interest is independent of the censoring mechanism. It is apparent that this may not be true sometimes and as pointed out by some authors, the analysis that does not take the dependent censoring into account could lead to biased or misleading results7,8. In this study, we consider regression analysis of multivariate interval-censored data arising from the additive hazards model and propose an estimating equation-based approach that allows for the informative censoring. The method can be easily implemented and the asymptotic properties of the proposed estimator of regression parameters are established. Also we perform a simulation study for the evaluation of the proposed method and it suggests that the method works well for practical situations. Finally, the proposed approach is applied to a set of real data.
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Bambey Guure, Chris, and Noor Akma Ibrahim. "Generalized Bayesian non-informative prior estimation of Weibull parameter with interval censoring." ScienceAsia 39S, no. 1 (2013): 75. http://dx.doi.org/10.2306/scienceasia1513-1874.2013.39s.075.

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7

Ejoku, Jonathan, Collins Odhiambo, and Linda Chaba. "Analysis of Recurrent Events with Associated Informative Censoring: Application to HIV Data." International Journal of Statistics in Medical Research 9 (November 16, 2021): 20–27. http://dx.doi.org/10.6000/1929-6029.2020.09.03.

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In this study, we adapt a Cox-based model for recurrent events; the Prentice, Williams and Peterson Total -Time (PWP-TT) that has largely, been used under the assumption of non-informative censoring and evaluate it under an informative censoring setting. Empirical evaluation was undertaken with the aid of the semi-parametric framework for recurrent events suggested by Huang [1] and implemented in R Studio software. For validation we used data from a typical HIV care setting in Kenya. Of the three models under consideration; the standard Cox Model had gender hazard ratio (HR) of 0.66 (p-value=0.165), Andersen-Gill had HR 0.46 (with borderline p-value=0.054) and extended PWP TT had HR 0.22 (p-value=0.006). The PWP-TT model performed better as compared to other models under informative setting. In terms of risk factors under informative setting, LTFU due to stigma; gender [base=Male] had HR 0.544 (p-value =0.002), age [base is < 37] had HR 0.772 (p-value=0.008), ART regimen [base= First line] had HR 0.518 (p-value= 0.233) and differentiated care model (Base=not on DCM) had HR 0.77(p-value=0.036). In conclusion, in spite of the multiple interventions designed to address incidences of LTFU among HIV patients, within-person cases of LTFU are usually common and recurrent in nature, with the present likelihood of a person getting LTFU influenced by previous occurrences and therefore informative censoring should be checked.
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8

Nagy, M., M. E. Bakr, and Adel Fahad Alrasheedi. "Analysis with Applications of the Generalized Type-II Progressive Hybrid Censoring Sample from Burr Type-XII Model." Mathematical Problems in Engineering 2022 (February 18, 2022): 1–21. http://dx.doi.org/10.1155/2022/1241303.

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In this article, based on the generalized Type-II progressive hybrid censoring sample from the Burr Type-XII distribution, maximum likelihood and Bayesian inference are discussed. Point and interval estimates of unknown parameters, reliability, and hazard functions are developed. We employed several loss functions, such as squared error, LINEX, and general entropy, as symmetric and asymmetric loss functions and various prior distributions as informative and non-informative priors for Bayesian inference of unknown parameters. Under a generalized Type-II progressive hybrid censoring sample, we also propose a Bayesian one-sample prediction for unobserved failures. We conduct simulation study using the MCMC algorithm for the Bayesian approach based on several prior distributions. Finally, we apply the results of the theoretical research to real data.
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9

Wax, Yohanan, Stuart G. Baker, and Blossom H. Patterson. "A Score Test for Non-Informative Censoring Using Doubly Sampled Grouped Survival Data." Applied Statistics 42, no. 1 (1993): 159. http://dx.doi.org/10.2307/2347418.

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10

Alotaibi, Refah, Hoda Rezk, Sanku Dey, and Hassan Okasha. "Bayesian estimation for Dagum distribution based on progressive type I interval censoring." PLOS ONE 16, no. 6 (June 2, 2021): e0252556. http://dx.doi.org/10.1371/journal.pone.0252556.

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In this paper, we consider Dagum distribution which is capable of modeling various shapes of failure rates and aging criteria. Based on progressively type-I interval censoring data, we first obtain the maximum likelihood estimators and the approximate confidence intervals of the unknown parameters of the Dagum distribution. Next, we obtain the Bayes estimators of the parameters of Dagum distribution under the squared error loss (SEL) and balanced squared error loss (BSEL) functions using independent informative gamma and non informative uniform priors for both scale and two shape parameters. A Monte Carlo simulation study is performed to assess the performance of the proposed Bayes estimators with the maximum likelihood estimators. We also compute credible intervals and symmetric 100(1 − τ)% two-sided Bayes probability intervals under the respective approaches. Besides, based on observed samples, Bayes predictive estimates and intervals are obtained using one-and two-sample schemes. Simulation results reveal that the Bayes estimates based on SEL and BSEL performs better than maximum likelihood estimates in terms of bias and MSEs. Besides, credible intervals have smaller interval lengths than confidence interval. Further, predictive estimates based on SEL with informative prior performs better than non-informative prior for both one and two sample schemes. Further, the optimal censoring scheme has been suggested using a optimality criteria. Finally, we analyze a data set to illustrate the results derived.
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