Journal articles: 'Data censoring'

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Moradian, Hoora, Denis Larocque, and François Bellavance. "Survival forests for data with dependent censoring." Statistical Methods in Medical Research 28, no. 2 (August 24, 2017): 445–61. http://dx.doi.org/10.1177/0962280217727314.

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Tree-based methods are very powerful and popular tools for analysing survival data with right-censoring. The existing methods assume that the true time-to-event and the censoring times are independent given the covariates. We propose different ways to build survival forests when dependent censoring is suspected, by using an appropriate estimator of the survival function when aggregating the individual trees and/or by modifying the splitting rule. The appropriate estimator used in this paper is the copula-graphic estimator. We also propose a new method for building survival forests, called p-forest, that may be used not only when dependent censoring is suspected, but also as a new survival forest method in general. The results from a simulation study indicate that these modifications improve greatly the estimation of the survival function in situations of dependent censoring. A real data example illustrates how the proposed methods can be used to perform a sensitivity analysis.

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Willems, SJW, A. Schat, MS van Noorden, and M. Fiocco. "Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator." Statistical Methods in Medical Research 27, no. 2 (March 17, 2016): 323–35. http://dx.doi.org/10.1177/0962280216628900.

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Censored data make survival analysis more complicated because exact event times are not observed. Statistical methodology developed to account for censored observations assumes that patients’ withdrawal from a study is independent of the event of interest. However, in practice, some covariates might be associated to both lifetime and censoring mechanism, inducing dependent censoring. In this case, standard survival techniques, like Kaplan–Meier estimator, give biased results. The inverse probability censoring weighted estimator was developed to correct for bias due to dependent censoring. In this article, we explore the use of inverse probability censoring weighting methodology and describe why it is effective in removing the bias. Since implementing this method is highly time consuming and requires programming and mathematical skills, we propose a user friendly algorithm in R. Applications to a toy example and to a medical data set illustrate how the algorithm works. A simulation study was carried out to investigate the performance of the inverse probability censoring weighted estimators in situations where dependent censoring is present in the data. In the simulation process, different sample sizes, strengths of the censoring model, and percentages of censored individuals were chosen. Results show that in each scenario inverse probability censoring weighting reduces the bias induced in the traditional Kaplan–Meier approach where dependent censoring is ignored.

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Emura, Takeshi, and Yi-Hau Chen. "Gene selection for survival data under dependent censoring: A copula-based approach." Statistical Methods in Medical Research 25, no. 6 (July 11, 2016): 2840–57. http://dx.doi.org/10.1177/0962280214533378.

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Dependent censoring arises in biomedical studies when the survival outcome of interest is censored by competing risks. In survival data with microarray gene expressions, gene selection based on the univariate Cox regression analyses has been used extensively in medical research, which however, is only valid under the independent censoring assumption. In this paper, we first consider a copula-based framework to investigate the bias caused by dependent censoring on gene selection. Then, we utilize the copula-based dependence model to develop an alternative gene selection procedure. Simulations show that the proposed procedure adjusts for the effect of dependent censoring and thus outperforms the existing method when dependent censoring is indeed present. The non-small-cell lung cancer data are analyzed to demonstrate the usefulness of our proposal. We implemented the proposed method in an R “compound.Cox” package.

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Sparks, R. S., G. Sutton, P. Toscas, and J. T. Ormerod. "Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC." Advances in Decision Sciences 2011 (July 5, 2011): 1–8. http://dx.doi.org/10.1155/2011/571768.

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Low detection limits are common in measure environmental variables. Building models using data containing low or high detection limits without adjusting for the censoring produces biased models. This paper offers approaches to estimate an inverse Gaussian distribution when some of the data used are censored because of low or high detection limits. Adjustments for the censoring can be made if there is between 2% and 20% censoring using either the EM algorithm or MCMC. This paper compares these approaches.

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Sun, Yanqing, and Jimin Lee. "Testing independent censoring for longitudinal data." Statistica Sinica 21, no. 3 (June 1, 2011): 1315–39. http://dx.doi.org/10.5705/ss.2009.251.

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D’Arrigo, Graziella, Daniela Leonardis, Samar Abd ElHafeez, Maria Fusaro, Giovanni Tripepi, and Stefanos Roumeliotis. "Methods to Analyse Time-to-Event Data: The Kaplan-Meier Survival Curve." Oxidative Medicine and Cellular Longevity 2021 (September 20, 2021): 1–7. http://dx.doi.org/10.1155/2021/2290120.

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Studies performed in the field of oxidative medicine and cellular longevity frequently focus on the association between biomarkers of cellular and molecular mechanisms of oxidative stress as well as of aging, immune function, and vascular biology with specific time to event data, such as mortality and organ failure. Indeed, time-to-event analysis is one of the most important methodologies used in clinical and epidemiological research to address etiological and prognostic hypotheses. Survival data require adequate methods of analyses. Among these, the Kaplan-Meier analysis is the most used one in both observational and interventional studies. In this paper, we describe the mathematical background of this technique and the concept of censoring (right censoring, interval censoring, and left censoring) and report some examples demonstrating how to construct a Kaplan-Meier survival curve and how to apply this method to provide an answer to specific research questions.

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Gómez, Guadalupe, M. Luz Calle, Ramon Oller, and Klaus Langohr. "Tutorial on methods for interval-censored data and their implementation in R." Statistical Modelling 9, no. 4 (December 2009): 259–97. http://dx.doi.org/10.1177/1471082x0900900402.

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Interval censoring is encountered in many practical situations when the event of interest cannot be observed and it is only known to have occurred within a time window. The theory for the analysis of interval-censored data has been developed over the past three decades and several reviews have been written. However, it is still a common practice in medical and reliability studies to simplify the interval censoring structure of the data into a more standard right censoring situation by, for instance, imputing the midpoint of the censoring interval. The availability of software for right censoring might well be the main reason for this simplifying practice. In contrast, several methods have been developed to deal with interval-censored data and the corresponding algorithms to make the procedures feasible are scattered across the statistical software or remain behind the personal computers of many researchers. The purpose of this tutorial is to present, in a pedagogical and unified manner, the methodology and the available software for analyzing interval-censored data. The paper covers frequentist non-parametric, parametric and semiparametric estimating approaches, non-parametric tests for comparing survival curves and a section on simulation of interval-censored data. The methods and the software are described using the data from a dental study.

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Nagy, M., and Adel Fahad Alrasheedi. "Estimations of Generalized Exponential Distribution Parameters Based on Type I Generalized Progressive Hybrid Censored Data." Computational and Mathematical Methods in Medicine 2022 (March 29, 2022): 1–15. http://dx.doi.org/10.1155/2022/8058473.

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Type I generalized progressive hybrid censoring scheme is a combination of Type I and Type II progressive hybrid censoring schemes, and it is one of the most recent advancements in data censoring. In this article, based on Type I generalized progressive hybrid censoring data from generalized exponential distribution, the maximum likelihood and Bayesian estimators of distribution’s parameters as well as the reliability and hazard functions are approximately calculated. Also, the credible interval estimators of these quantities are obtained. Since these quantities cannot be obtained in closed form, so simulation and analysis using a Monte Carlo simulation study with Gibbs sampling are taken. Finally, an illustrative example using real data set is presented to compare the proposed procedures presented and developed here.

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Belviso, Nicholas, Yichi Zhang, Herbert D. Aronow, Richard Wyss, Marilyn Barbour, Stephen Kogut, Oluwadolapo D. Lawal, Si Y. Zhan, Prabhani Kuruppumullage Don, and Xuerong Wen. "Addressing Posttreatment Selection Bias in Comparative Effectiveness Research, Using Real-World Data and Simulation." American Journal of Epidemiology 191, no. 2 (October 6, 2021): 331–40. http://dx.doi.org/10.1093/aje/kwab242.

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Abstract To examine methodologies that address imbalanced treatment switching and censoring, 6 different analytical approaches were evaluated under a comparative effectiveness framework: intention-to-treat, as-treated, intention-to-treat with censor-weighting, as-treated with censor-weighting, time-varying exposure, and time-varying exposure with censor-weighting. Marginal structural models were employed to address time-varying exposure, confounding, and possibly informative censoring in an administrative data set of adult patients who were hospitalized with acute coronary syndrome and treated with either clopidogrel or ticagrelor. The effectiveness endpoint included first occurrence of death, myocardial infarction, or stroke. These methodologies were then applied across simulated data sets with varying frequencies of treatment switching and censoring to compare the effect estimate of each analysis. The findings suggest that implementing different analytical approaches has an impact on the point estimate and interpretation of analyses, especially when censoring is highly unbalanced.

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Belviso, Nicholas, Yichi Zhang, Herbert D. Aronow, Richard Wyss, Marilyn Barbour, Stephen Kogut, Oluwadolapo D. Lawal, Si Y. Zhan, Prabhani Kuruppumullage Don, and Xuerong Wen. "Addressing Posttreatment Selection Bias in Comparative Effectiveness Research, Using Real-World Data and Simulation." American Journal of Epidemiology 191, no. 2 (October 6, 2021): 331–40. http://dx.doi.org/10.1093/aje/kwab242.

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Abstract To examine methodologies that address imbalanced treatment switching and censoring, 6 different analytical approaches were evaluated under a comparative effectiveness framework: intention-to-treat, as-treated, intention-to-treat with censor-weighting, as-treated with censor-weighting, time-varying exposure, and time-varying exposure with censor-weighting. Marginal structural models were employed to address time-varying exposure, confounding, and possibly informative censoring in an administrative data set of adult patients who were hospitalized with acute coronary syndrome and treated with either clopidogrel or ticagrelor. The effectiveness endpoint included first occurrence of death, myocardial infarction, or stroke. These methodologies were then applied across simulated data sets with varying frequencies of treatment switching and censoring to compare the effect estimate of each analysis. The findings suggest that implementing different analytical approaches has an impact on the point estimate and interpretation of analyses, especially when censoring is highly unbalanced.

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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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Hyun, Seunggeun, Jimin Lee, and Robert Yearout. "Parameter Estimation of Type-I and Type-II Hybrid Censored Data from the Log-Logistic Distribution." Industrial and Systems Engineering Review 4, no. 1 (July 16, 2016): 37–44. http://dx.doi.org/10.37266/iser.2016v4i1.pp37-44.

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In experiments on product lifetime and reliability testing, there are many practical situations in which researchers terminate the experiment and report the results before all items of the experiment fail because of time or cost consideration. The most common and popular censoring schemes are type-I and type-II censoring. In type-I censoring scheme, the termination time is pre-fixed, but the number of observed failures is a random variable. However, if the mean lifetime of experimental units is somewhat larger than the pre-fixed termination time, then far fewer failures would be observed and this is a significant disadvantage on the efficiency of inferential procedures. In type-II censoring scheme, however, the number of observed failures is pre-fixed, but the experiment time is a random variable. In this case, at least pre-specified number of failure are obtained, but the termination time is clearly a disadvantage from the experimenter’s point of view. To overcome some of the drawbacks in those schemes, the hybrid censoring scheme, which is a mixture of the conventional type-I and type-II censoring schemes, has received much attention in recent years. In this paper, we consider the analysis of type-I and type-II hybrid censored data where the lifetimes of items follow two-parameter log-logistic distribution. We present the maximum likelihood estimators of unknown parameters and asymptotic confidence intervals, and a simulation study is conducted to evaluate the proposed methods.

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Leurgans, Sue. "Linear Models, Random Censoring and Synthetic Data." Biometrika 74, no. 2 (June 1987): 301. http://dx.doi.org/10.2307/2336144.

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Helsel, Dennis R. "Insider Censoring: Distortion of Data with Nondetects." Human and Ecological Risk Assessment: An International Journal 11, no. 6 (December 2005): 1127–37. http://dx.doi.org/10.1080/10807030500278586.

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Wang, Mei-Cheng, Jing Qin, and Chin-Tsang Chiang. "Analyzing Recurrent Event Data With Informative Censoring." Journal of the American Statistical Association 96, no. 455 (September 2001): 1057–65. http://dx.doi.org/10.1198/016214501753209031.

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Zhu, Hongbin, Hua Qian, Xiliang Luo, and Yang Yang. "Adaptive Queuing Censoring for Big Data Processing." IEEE Signal Processing Letters 25, no. 5 (May 2018): 610–14. http://dx.doi.org/10.1109/lsp.2018.2815006.

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Jammalamadaka, S. Rao, and Vasudevan Mangalam. "A general censoring scheme for circular data." Statistical Methodology 6, no. 3 (May 2009): 280–89. http://dx.doi.org/10.1016/j.stamet.2008.10.002.

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Rabinowitz, Daniel. "Testing current status data for dependent censoring." Statistics & Probability Letters 48, no. 3 (July 2000): 213–16. http://dx.doi.org/10.1016/s0167-7152(99)00202-3.

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Fennie, Kristopher P., Carol A. Bova, and Ann B. Williams. "Adjusting and Censoring Electronic Monitoring Device Data." JAIDS Journal of Acquired Immune Deficiency Syndromes 43, Supplement 1 (December 2006): S88—S95. http://dx.doi.org/10.1097/01.qai.0000248336.97814.2f.

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Kim, Jin-Heum, and Youn-Nam Kim. "Analysis of Tumorigenicity Data with Informative Censoring." Korean Journal of Applied Statistics 23, no. 5 (October 31, 2010): 871–82. http://dx.doi.org/10.5351/kjas.2010.23.5.871.

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SUE, LEURGANS. "Linear models, random censoring and synthetic data." Biometrika 74, no. 2 (1987): 301–9. http://dx.doi.org/10.1093/biomet/74.2.301.

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Santos, Jesús Basulto, and Francisco Javier Ortega Irizo. "Modelling citation age data with right censoring." Scientometrics 62, no. 3 (March 2005): 329–42. http://dx.doi.org/10.1007/s11192-005-0025-5.

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Abdushukurov, A. A. "Extension of Relative-Risk Power Estimator under Dependent Random Censored Data." Contemporary Mathematics. Fundamental Directions 68, no. 1 (April 20, 2022): 1–13. http://dx.doi.org/10.22363/2413-3639-2022-68-1-1-13.

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In this paper, the considered problem consists in estimation of conditional survival function by right random censoring model in the presence of a covariate. We propose a new estimator of conditional survival function which is extension of relative-risk power estimator of independent censoring and study its large sample properties. We present result of asymptotic normality with the same limiting Gaussian process as for copula-graphic estimator.

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Jayalath, Kalanka P. "Fiducial Inference on the Right Censored Birnbaum–Saunders Data via Gibbs Sampler." Stats 4, no. 2 (May 21, 2021): 385–99. http://dx.doi.org/10.3390/stats4020025.

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In this article, we implement a flexible Gibbs sampler to make inferences for two-parameter Birnbaum–Saunders (BS) distribution in the presence of right-censored data. The Gibbs sampler is applied on the fiducial distributions of the BS parameters derived using the maximum likelihood, methods of moments, and their bias-reduced estimates. A Monte-Carlo study is conducted to make comparisons between these estimates for Type-II right censoring with various parameter settings, sample sizes, and censoring percentages. It is concluded that the bias-reduced estimates outperform the rest with increasing precision. Higher sample sizes improve the overall accuracy of all the estimates while the amount of censoring shows a negative effect. Further comparisons are made with existing methods using two real-world examples.

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Bakoban, R. A., and G. A. Abd-Elmougod. "MCMC in Analysis of Progressively First Failure Censored Competing Risks Data for Gompertz Model." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 6662–70. http://dx.doi.org/10.1166/jctn.2016.5612.

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In medical studies or in reliability analysis, it is quite common that the failure of any individual or any item may be attributable to more than one cause. So in this paper, we consider the competing risks model with very general censoring scheme, namely progressive first-failure censored scheme under the Gompertz life time distribution. The results in each of first-failure censoring, progressive Type II censoring, Type II censoring and complete sample are a special cases. We provide different methods for the analysis of the model under the assumption of independent causes of failure and Gompertz distribution lifetimes. The maximum likelihood estimators (MLE’s) of the different parameters as well as approximate confidence intervals are presented. Bayesian estimation using MCMC method under the joint prior density as a product of a conditional gamma density and inverted gamma density for unknown Gompertz parameters are presented. The analysis of a real data set to assess the performance of all these estimators, confidence intervals are developed using asymptotic distributions and Bayesian credible intervals for the parameters. The different methods are compared through a simulation study.

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Jaffa, Miran A., Mulugeta Gebregziabher, and Ayad A. Jaffa. "Shared parameter and copula models for analysis of semicontinuous longitudinal data with nonrandom dropout and informative censoring." Statistical Methods in Medical Research 31, no. 3 (November 22, 2021): 451–74. http://dx.doi.org/10.1177/09622802211060519.

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Analysis of longitudinal semicontinuous data characterized by subjects’ attrition triggered by nonrandom dropout is complex and requires accounting for the within-subject correlation, and modeling of the dropout process. While methods that address the within-subject correlation and missing data are available, approaches that incorporate the nonrandom dropout, also referred to informative right censoring, in the modeling step are scarce due to the computational intensity and possible intractable integration needed for its implementation. Appreciating the complexity of this problem and the need for a new methodology that is feasible for implementation, we propose to extend a framework of likelihood-based marginalized two-part models to account for informative right censoring. The censoring process is modeled using two approaches: (1) Poisson censoring for the count of visits before dropout and (2) survival time to dropout. Novel consideration was given to the proposed joint modeling approaches for the semicontinuous and censoring components of the likelihood function which included (1) shared parameter, and (2) Clayton copula. The cross-part and within-part correlations were accounted for through a complex random effect structure that models correlated random intercepts and slopes. Feasibility of implementation, and accuracy of these approaches were investigated using extensive simulation studies and clinical application.

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Prakash, Gyan. "Bound lengths for type-I progressive hybrid burr type-XII censored data under SS-PALT." Biometrics & Biostatistics International Journal 10, no. 1 (February 23, 2021): 4–22. http://dx.doi.org/10.15406/bbij.2021.10.00325.

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Our main focus on combining two different approaches, Step-Stress Partially Accelerated Life Test and Type-I Progressive Hybrid censoring criteria in the present article. The fruitfulness of this combination has been investigated by bound lengths for unknown parameters of the Burr Type-XII distribution. Approximate confidence intervals, Bootstrap confidence intervals and One-Sample Bayes prediction bound lengths have been obtained under the above scenario. Particular cases of Type-I Progressive Hybrid censoring (Type-I and Progressive Type-II censoring) has also evaluated under SS-PALT. Optimal stress change time also measured by minimizing the asymptotic variance of ML Estimation. A simulation study based on Metropolis-Hastings algorithm have carried out along with a real data set example.

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Sigelman, Lee, and Langche Zeng. "Analyzing Censored and Sample-Selected Data with Tobit and Heckit Models." Political Analysis 8, no. 2 (December 16, 1999): 167–82. http://dx.doi.org/10.1093/oxfordjournals.pan.a029811.

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Political scientists are making increasing use of the Tobit and Heckit models. This paper addresses some common problems in the application and interpretation of these models. Through numerical experiments and reanalysis of data from a study by Romer and Snyder (1994), we illustrate the consequences of using the standard Tobit model, which assumes a censoring point at zero, when the zeros are not due to censoring mechanisms or when actual censoring is not at zero. In the latter case, we also show that Greene's (1981) well-known results on the direction and size of the bias of the OLS estimator in the standard Tobit model do not necessarily hold. Because the Heckit model is often used as an alternative to Tobit, we examine its assumptions and discuss the proper interpretation of the Heckit/Tobit estimation results using Grier and co-workers' (1994) Heckit model of campaign contribution data. Sensitivity analyses of the Heckit estimation results suggest some conclusions rather different from those reached by Grier et al.

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Khoolenjani, N. B., and O. Chatrabgoun. "Estimating the parameters of lifetime distributions under progressively Type-II censoring from fuzzy data." Journal of Applied Mathematics, Statistics and Informatics 12, no. 1 (May 1, 2016): 41–53. http://dx.doi.org/10.1515/jamsi-2016-0004.

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Abstract The problem of estimating lifetime distribution parameters under progressively Type-II censoring originated in the context of reliability. But traditionally it is assumed that the available data from this censoring scheme are performed in exact numbers. However, some collected lifetime data might be imprecise and are represented in the form of fuzzy numbers. Thus, it is necessary to generalize classical statistical estimation methods for real numbers to fuzzy numbers. This paper deals with the estimation of lifetime distribution parameters under progressively Type-II censoring scheme when the lifetime observations are reported by means of fuzzy numbers. A new method is proposed to determine the maximum likelihood estimates of the parameters of interest. The methodology is illustrated with two popular models in lifetime analysis, the Rayleigh and Lognormal lifetime distributions.

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Lone, Showkat Ahmad, Ahmadur Rahman, and Tanveer A. Tarray. "Inference for Step-Stress Partially Accelerated Life Test Model with an Adaptive Type-I Progressively Hybrid Censored Data." Journal of Modern Applied Statistical Methods 19, no. 1 (June 8, 2021): 2–20. http://dx.doi.org/10.22237/jmasm/1608552180.

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Consider estimating data of failure times under step-stress partially accelerated life tests based on adaptive Type-I hybrid censoring. The mathematical model related to the lifetime of the test units is assumed to follow Rayleigh distribution. The point and interval maximum-likelihood estimations are obtained for distribution parameter and tampering coefficient. Also, the work is conducted under a traditional Type-I hybrid censoring plan (scheme). A Monte Carlo simulation algorithm is used to evaluate and compare the performances of the estimators of the tempering coefficient and model parameters under both progressively hybrid censoring plans. The comparison is carried out on the basis of mean squared errors and bias.

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Sadler, William A. "Methods comparison biases due to differing uncertainties and data censoring." Annals of Clinical Biochemistry: International Journal of Laboratory Medicine 56, no. 5 (June 26, 2019): 608–12. http://dx.doi.org/10.1177/0004563219859920.

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Background Measurements on clinical specimens that contain no analyte, or very low amounts of analyte, unavoidably generate assay response (signal) measurements that fall on the ‘negative’ side of the fitted zero response. It is virtually universal practice to left-censor such measurements to zero and this is frequently extended by left-censoring to the assay limit of detection (LoD) value for reporting purposes. This study considers the effect of censoring on methods comparison analysis. Methods Paired results were randomly generated from two hypothetical assays with zero bias, firstly assuming equal uncertainty near zero and secondly with uncertainties that differed by a moderate 50% near zero. In both cases results were left-censored to zero and to LoD and further subsets were extracted representing partial and complete removal of censored results. All data sets were subjected to overall bias evaluation and Bland–Altman and Deming regression analyses. Results The combination of differing uncertainties and data censoring produced spurious biases by both Bland–Altman and regression analysis, regardless of whether censored results were retained or discarded. Biases were small for data left-censored to zero but were non-trivial with LoD-censoring. Imposing a lower limit aimed at eliminating the influence of censored results did not resolve the problem. Conclusions When high proportion of clinical results are located near zero, caution is required when using censored data (and especially LoD-censored data) in methods comparison studies. Optional access to negative results would rectify the problem, but requires the cooperation of manufacturers.

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Rogula, Basia, Greta Lozano-Ortega, and Karissa M. Johnston. "A Method for Reconstructing Individual Patient Data From Kaplan-Meier Survival Curves That Incorporate Marked Censoring Times." MDM Policy & Practice 7, no. 1 (January 2022): 238146832210776. http://dx.doi.org/10.1177/23814683221077643.

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Introduction. Access to individual patient data (IPD) can be advantageous when conducting cost-effectiveness analyses or indirect treatment comparisons. While exact times of censoring are often marked on published Kaplan-Meier (KM) curves, an algorithm for reconstructing IPD from such curves that allows for their incorporation is presently unavailable. Methods. An algorithm capable of incorporating marked censoring times was developed to reconstruct IPD from KM curves, taking as additional inputs the total patient count and coordinates of the drops in survival. The reliability of the algorithm was evaluated via a simulation exercise, in which survival curves were simulated, digitized, and then reconstructed. To assess the reliability of the reconstructed curves, hazard ratios (HRs) and quantiles of survival were compared between the original and reconstructed curves, and the reconstructed curves were visually inspected. Results. No systematic differences were found in HRs and quantiles in the original versus reconstructed curves. Upon visual inspection, the reconstructed IPD provided a close fit to the digitized data from the published KM curves. Inherent to the algorithm, censoring times were incorporated into the reconstructed data exactly as specified. Conclusion. This new algorithm can reliably be used to reconstruct IPD from reported KM survival curves in the presence of extractable censoring times. Use of the algorithm will allow health researchers to reconstruct IPD more closely by incorporating censoring times exactly as marked, requiring as additional inputs the total patient count and coordinates of the drops in survival.

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Dagne, Getachew A., and Yangxin Huang. "Mixed-Effects Tobit Joint Models for Longitudinal Data with Skewness, Detection Limits, and Measurement Errors." Journal of Probability and Statistics 2012 (2012): 1–19. http://dx.doi.org/10.1155/2012/614102.

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Complex longitudinal data are commonly analyzed using nonlinear mixed-effects (NLME) models with a normal distribution. However, a departure from normality may lead to invalid inference and unreasonable parameter estimates. Some covariates may be measured with substantial errors, and the response observations may also be subjected to left-censoring due to a detection limit. Inferential procedures can be complicated dramatically when such data with asymmetric characteristics, left censoring, and measurement errors are analyzed. There is relatively little work concerning all of the three features simultaneously. In this paper, we jointly investigate a skew-tNLME Tobit model for response (with left censoring) process and a skew-tnonparametric mixed-effects model for covariate (with measurement errors) process under a Bayesian framework. A real data example is used to illustrate the proposed methods.

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Bakoyannis, Giorgos, and Giota Touloumi. "Practical methods for competing risks data: A review." Statistical Methods in Medical Research 21, no. 3 (January 7, 2011): 257–72. http://dx.doi.org/10.1177/0962280210394479.

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Competing risks data arise naturally in medical research, when subjects under study are at risk of more than one mutually exclusive event such as death from different causes. The competing risks framework also includes settings where different possible events are not mutually exclusive but the interest lies on the first occurring event. For example, in HIV studies where seropositive subjects are receiving highly active antiretroviral therapy (HAART), treatment interruption and switching to a new HAART regimen act as competing risks for the first major change in HAART. This article introduces competing risks data and critically reviews the widely used statistical methods for estimation and modelling of the basic (estimable) quantities of interest. We discuss the increasingly popular Fine and Gray model for subdistribution hazard of interest, which can be readily fitted using standard software under the assumption of administrative censoring. We present a simulation study, which explores the robustness of inference for the subdistribution hazard to the assumption of administrative censoring. This shows a range of scenarios within which the strictly incorrect assumption of administrative censoring has a relatively small effect on parameter estimates and confidence interval coverage. The methods are illustrated using data from HIV-1 seropositive patients from the collaborative multicentre study CASCADE (Concerted Action on SeroConversion to AIDS and Death in Europe).

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Chen, Xiaotian, Yu Cheng, Ellen Frank, and David J. Kupfer. "Association analysis of successive events data in the presence of competing risks." Statistical Methods in Medical Research 27, no. 6 (September 19, 2016): 1661–82. http://dx.doi.org/10.1177/0962280216667645.

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We aim to close a methodological gap in analyzing durations of successive events that are subject to induced dependent censoring as well as competing-risk censoring. In the Bipolar Disorder Center for Pennsylvanians study, some patients who managed to recover from their symptomatic entry later developed a new depressive or manic episode. It is of great clinical interest to quantify the association between time to recovery and time to recurrence in patients with bipolar disorder. The estimation of the bivariate distribution of the gap times with independent censoring has been well studied. However, the existing methods cannot be applied to failure times that are censored by competing causes such as in the Bipolar Disorder Center for Pennsylvanians study. Bivariate cumulative incidence function has been used to describe the joint distribution of parallel event times that involve multiple causes. To the best of our knowledge, however, there is no method available for successive events with competing-risk censoring. Therefore, we extend the bivariate cumulative incidence function to successive events data, and propose non-parametric estimators of the bivariate cumulative incidence function and the related conditional cumulative incidence function. Moreover, an odds ratio measure is proposed to describe the cause-specific dependence, leading to the development of a formal test for independence of successive events. Simulation studies demonstrate that the estimators and tests perform well for realistic sample sizes, and our methods can be readily applied to the Bipolar Disorder Center for Pennsylvanians study.

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Seong, Yeongjae, and Kyeongjun Lee. "Exact Likelihood Inference for Parameter of Exponential Distribution under Combined Generalized Progressive Hybrid Censoring Scheme." Symmetry 14, no. 9 (August 24, 2022): 1764. http://dx.doi.org/10.3390/sym14091764.

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Recently, the generalized type I progressive hybrid censoring scheme (GenT1PrHyCS) and generalized type II progressive hybrid censoring scheme (GenT2PrHyCS) have become quite popular in reliability studies. These two type censoring schemes are very complex due to the large number of parameters used to specify the censoring procedure. Therefore, in this paper, we consider a more general and more complex new censoring scheme. Also, we consider the exponential distribution(ExpD) and derive an expression for the density function of the MLE. We prove the exact distribution of the maximum likelihood estimator (MLE) and conditional moment generating function (CondMGF) of the MLE for the mean of the ExpD under a new censoring scheme. We then derive the exact confidence intervals (ConfItv) for the mean of the ExpD under a new censoring scheme. Finally, we present an example to explain the methods of inference derived for this paper. From the example data, it can be seen that PDF of MLE for ExpD under new censoring scheme is almost symmetrical.

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Dey, Tanujit, Stuart R. Lipsitz, Zara Cooper, Quoc-Dien Trinh, Martin Krzywinski, and Naomi Altman. "Survival analysis—time-to-event data and censoring." Nature Methods 19, no. 8 (August 2022): 906–8. http://dx.doi.org/10.1038/s41592-022-01563-7.

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Drobeniuc, Ana-Maria, and Anne Spaulding. "Censoring of HIV Viremia Data of Reincarcerated Individuals." JAIDS Journal of Acquired Immune Deficiency Syndromes 76, no. 1 (September 2017): e22-e23. http://dx.doi.org/10.1097/qai.0000000000001465.

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Jiang, Thomas J., and James M. Dickey. "Bayesian methods for categorical data under informative censoring." Bayesian Analysis 3, no. 3 (September 2008): 541–53. http://dx.doi.org/10.1214/08-ba321.

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Datta, Somnath. "Robust Regression Analysis of Longitudinal Data under Censoring." Austrian Journal of Statistics 46, no. 3-4 (April 12, 2017): 3–11. http://dx.doi.org/10.17713/ajs.v46i3-4.666.

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We consider regression analysis of longitudinal data when the temporal correlation is modeled by an autoregressive process. Robust R estimators of regression and autoregressive parameters are obtained. Our estimators are valid under censoring caused by detection limits. Efficient computation of the estimators is discussed. Theoretical and simulation studies of the estimators are presented. We analyze a real data set on air pollution using our methodology.

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de Oliveira, Guilherme Lopes, Rosangela Helena Loschi, and Renato Martins Assunção. "A random-censoring Poisson model for underreported data." Statistics in Medicine 36, no. 30 (October 24, 2017): 4873–92. http://dx.doi.org/10.1002/sim.7456.

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Khan, Shakeeb, Maria Ponomareva, and Elie Tamer. "Identification of panel data models with endogenous censoring." Journal of Econometrics 194, no. 1 (September 2016): 57–75. http://dx.doi.org/10.1016/j.jeconom.2016.01.010.

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Nur Niswah Naslina, Azid Maarof, Arasan Jayanthi, Zulkafli Hani Syahida, and Adam Mohd Bakri. "Assessing the Goodness of Fit of the Gompertz Model in the Presence of Right and Interval Censored Data with Covariate." Austrian Journal of Statistics 49, no. 3 (February 20, 2020): 57–71. http://dx.doi.org/10.17713/ajs.v49i3.1085.

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This research focuses on assessing the goodness of fit for the Gompertz model in the presence of right and interval censored data with covariate. The performance of the maximum likelihood estimates was evaluated via a simulation study at various censoring proportions and sample sizes. The conclusions were drawn based on the results of bias, standard error and root mean square error at different settings. Following that, another simulation study was carried out to compare the performance of the proposed modifications to the Cox-Snell residuals for both censored and uncensored observations at different combinations of sample sizes and censoring levels. The results show that standard error and root mean square error values of the parameter estimates increase with the increase in censoring proportions and decrease in the number of sample size. This indicates that the estimates perform better when sample sizes are larger and censoring proportions are lower. The performance of the proposed modifications of the Cox-Snell residuals showed that they perform slightly better than existing method.

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Lin, Hung-Mo. "Informative Censoring—A Cause of Bias in Estimating COVID-19 Mortality Using Hospital Data." Life 13, no. 1 (January 11, 2023): 210. http://dx.doi.org/10.3390/life13010210.

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(1) Background: Several retrospective observational analyzed treatment outcomes for COVID-19; (2) Methods: Inverse probability of censoring weighting (IPCW) was applied to correct for bias due to informative censoring in database of hospitalized patients who did and did not receive convalescent plasma; (3) Results: When compared with an IPCW analysis, overall mortality was overestimated using an unadjusted Kaplan–Meier curve, and hazard ratios for the older age group compared to the youngest were underestimated using the Cox proportional hazard models and 30-day mortality; (4) Conclusions: An IPCW analysis provided stabilizing weights by hospital admission.

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Jackson, John W. "Diagnosing Covariate Balance Across Levels of Right-Censoring Before and After Application of Inverse-Probability-of-Censoring Weights." American Journal of Epidemiology 188, no. 12 (May 30, 2019): 2213–21. http://dx.doi.org/10.1093/aje/kwz136.

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Abstract Covariate balance is a central concept in the potential outcomes literature. With selected populations or missing data, balance across treatment groups can be insufficient for estimating marginal treatment effects. Recently, a framework for using covariate balance to describe measured confounding and selection bias for time-varying and other multivariate exposures in the presence of right-censoring has been proposed. Here, we revisit this framework to consider balance across levels of right-censoring over time in more depth. Specifically, we develop measures of covariate balance that can describe what is known as “dependent censoring” in the literature, along with its associated selection bias, under multiple mechanisms for right censoring. Such measures are interesting because they substantively describe the evolution of dependent censoring mechanisms. Furthermore, we provide weighted versions that can depict how well such dependent censoring has been eliminated when inverse-probability-of-censoring weights are applied. These results provide a conceptually grounded way to inspect covariate balance across levels of right-censoring as a validity check. As a motivating example, we applied these measures to a study of hypothetical “static” and “dynamic” treatment protocols in a sequential multiple-assignment randomized trial of antipsychotics with high dropout rates.

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Bagust, Adrian, and Sophie J. Beale. "Exploring the Effects of Early Censoring and Analysis of Clinical Trial Survival Data on Effectiveness and Cost-effectiveness Estimation through a Case Study in Advanced Breast Cancer." Medical Decision Making 38, no. 7 (August 20, 2018): 789–96. http://dx.doi.org/10.1177/0272989x18790966.

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Interim analyses of clinical trial data are frequently used to provide evidence to obtain marketing authorization for new drugs. However, results from such analyses may not reflect true estimates of relative effectiveness when trial follow-up is complete. Survival results, available at 2 time points from a breast cancer clinical trial, were compared to test the hypothesis that using immature data and a widely used right-censoring rule leads to biased survival estimates. Kaplan-Meier progression-free and overall survival data from 2 published CLEOPATRA trial reports (2012 and 2014) were digitized. Overlaying these results highlighted divergent trends. Parametric functions were fitted to both data sets but did not indicate consistent patterns that could be used as a basis for long-term extrapolation. Heavy censoring of patients in the early data cut coincides with sudden changes in hazard trends and survival patterns, supporting the hypothesis of censoring bias. This challenges the validity of estimates of clinical benefit (progression-free survival and overall survival) based on extrapolation of results from interim analyses of trial data, using a commonly employed censoring rule.

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Ragab, Mahmoud, Aisha Fayomi, Ali Algarni, G. A. Abd-Elmougod, Neveen Sayed-Ahmed, S. M. Abo-Dahab, and S. Abdel-Khalek. "Statistical Analysis of Joint Type-I Generalized Hybrid Censoring Data from Burr XII Lifetime Distributions." Complexity 2021 (May 21, 2021): 1–15. http://dx.doi.org/10.1155/2021/5543187.

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The quality of the products coming from different lines of production requires some tests called comparative life tests. For lines having the same facility, the lifetime of the product is distributed by Burr XII, the lifetime distribution, and units are tested under type-I generalized hybrid censoring scheme. The observed censoring data are used under maximum likelihood and the Bayes method to estimate the model parameters. The theoretical results are discussed and assessed through data analysis and Monte Carlo simulation study. Finally, we reported some brief comments obtained from numerical computation.

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Guure, Chris B., and Samuel Bosomprah. "Bayesian Perspective on Random Censored Survival Data." International Scholarly Research Notices 2014 (October 29, 2014): 1–9. http://dx.doi.org/10.1155/2014/430357.

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A unit is said to be randomly censored when the information on time occurrence of an event is not available due to either loss to followup, withdrawal, or nonoccurrence of the outcome event before the end of the study. It is assumed in independent random/noninformative censoring that each individual has his/her own failure time T and censoring time C; however, one can only observe the random vector, say, (X;δ). The classical approach is considered for analysing the generalised exponential distribution with random or noninformative censored samples which occur most often in biological or medical studies. The Bayes methods are also considered via a numerical approximation suggested by Lindley in 1980 and that of the Laplace approximation procedure developed by Tierney and Kadane in 1986 with assumed informative priors alongside linear exponential loss function and squared error loss function. A simulation study is carried out to compare the estimators proposed in this paper. Two datasets have also been illustrated.

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Pretorius, Carel J., and Jacobus P. J. Ungerer. "Improved Diagnostic Performance of High-Sensitivity Cardiac Troponin Assays Is an Artifact of Censored Data." Clinical Chemistry 62, no. 12 (December 1, 2016): 1654–57. http://dx.doi.org/10.1373/clinchem.2016.262634.

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Abstract BACKGROUND High-sensitivity cardiac troponin assays with improved analytical performance at low concentrations are credited with increased diagnostic sensitivity in acute coronary syndrome patients. We investigated the relationship between analytical sensitivity (detection capability) and diagnostic accuracy and tested the effect of censoring data with a software model. METHOD We generated 4 sets of results with decreasing detection capability and compared the ROC curves with and without censored data. RESULTS There was no relationship between diagnostic performance and detection capability. When data were censored the diagnostic accuracy decreased progressively with an increase in the threshold concentration for censoring. The ROC curves constructed with censored data have a characteristic appearance with a straight line between the censoring point and the top right hand corner. CONCLUSIONS There is not a direct relationship between the diagnostic accuracy and the detection capability of cardiac troponin assays. The artifactual decrease in diagnostic accuracy can be added to the list of reasons why data should not be censored and this practice should be disclosed in studies on diagnostic accuracy.

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Elbatal, Ibrahim, Naif Alotaibi, Salem A. Alyami, Mohammed Elgarhy, and Ahmed R. El-Saeed. "Bayesian and Non-Bayesian Estimation of the Nadaraj ah–Haghighi Distribution: Using Progressive Type-1 Censoring Scheme." Mathematics 10, no. 5 (February 27, 2022): 760. http://dx.doi.org/10.3390/math10050760.

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This work will address the problem of estimating the parameters for the Nadaraj ah–Haghighi (NH) distribution using progressive Type-1 censoring (PT1C) utilizing Bayesian and non-Bayesian approaches. To apply PT1C, censoring times for each stage of censoring needed to be known before the experiment started. To solve this issue of censoring time selection, qauntiles from the NH lifetime distribution will be used as PT1C censoring time points. Maximum likelihood (ML) estimators (MLEs) and asymptotic confidence intervals (ACoIs) are produced with a focus on the censoring technique. Bayes estimates (BEs) and accompanying maximum posterior density (PD) credible interval estimations are also created via the squared error (SEr) loss function. The BEs are evaluated using the Markov Chain Monte Carlo (MCMC) technique and the Metropolis–Hasting (MH) algorithm. An analysis of an actual data set demonstrates the theoretical implications of MLEs and BEs for defined schemes of PT1C samples. Finally, simulation results will be used to compare the performance of the various recommended estimators.

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Journal articles on the topic 'Data censoring'

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