2,293 results
Search Results
2. Investigation of risk factors for tunneled hemodialysis catheters dysfunction: competing risk analysis of a tertiary center data.
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Mohazzab, Arash, Khavanin Zadeh, Morteza, Dehesh, Paria, Abdolvand, Neda, Rahimi, Zhaleh, and Rahmani, Sahar
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DIALYSIS catheters ,COMPETING risks ,RISK assessment ,ELECTRONIC paper ,CATHETER-related infections ,INFERENTIAL statistics - Abstract
Background: Hemodialysis tunneled catheters are prone to failure due to infection or thrombosis. Prediction of catheter dysfunction chance and finding the predisposing risk factors might help clinicians to prolong proper catheter function. The multidimensional mechanism of failures following infection or thrombosis needs a multivariable and comprehensive analytic approach.Methods: A longitudinal cross-sectional study was implemented on 1048 patients admitted for the first hemodialysis tunneled catheterization attempt between 2013 and 2019 in Shahid Hasheminejdad hospital, Tehran, Iran. Patients' information was extracted from digital and also paper records. Based on their criteria, single and multiple variable analyses were done separately in patients with catheter dysfunction due to thrombosis and infection. T-test and Chi-square test were performed in quantitative and categorical variables, respectively. Competing risk regression was performed under the assumption of proportionality for infection and thrombosis, and the sub-distributional hazard ratios (SHR) were calculated. All statistical inferences were made with a significance level of 0.05.Results: Four hundred sixty-six patients were enrolled in the analysis based on study criteria. Samples' mean (SD) age was 54(15.54), and 322 (69.1%) patients were female. Three hundred sixty-five catheter dysfunction cases were observed due to thrombosis 123(26.4%) and infection 242(52%). The Median (range) time to catheter dysfunction event was 243(36-1131) days. Single variable analysis showed a statistically significant higher proportion of thrombosis in females (OR = 2.66, 95% CI: 1.77-4.00) and younger patients, respectively. Multivariate competing risk regression showed a statistically significant higher risk of thrombosis in females (Sub-distributional hazard (SHR) = 1.81), hypertensive (SHR = 1.82), and more obese patients (BMI SHR = 1.037). A higher risk of infection was calculated in younger (Age SHR = 0.98) and diabetic (SHR = 1.63) patients using the same method.Conclusion: Female and hypertensive patients are considerably at higher risk of catheter thrombosis, whereas diabetes is the most critical risk factor for infectious catheter dysfunction. Competing risk regression analysis showed a comprehensive result in the assessment of risk factors of catheter dysfunction. [ABSTRACT FROM AUTHOR]- Published
- 2022
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3. An improved failure model of oil-paper insulation at pulsating DC voltages
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Chenmeng Xiang, Jian Li, Lianwei Bao, Peng Sun, and Yan Wang
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Materials science ,business.industry ,Electric breakdown ,Electrical breakdown ,Electronic engineering ,Structural engineering ,business ,Competing risks ,Weibull distribution ,Voltage - Abstract
This paper presented the improved failure model by experimental and analysis results on electrical breakdown characteristics of oil-paper insulation at pulsating DC voltages. The time-to-breakdown of oil-paper specimens were measured by using constant-stress tests. The improved failure model was established and calculated by the parameters of Weibull model and the improved competing risk algorithm. It optimizes the faults of traditional two-parameter Weibull distribution when both time and voltage are considered as variables in estimating the lifetime of oil-impregnated paper.
- Published
- 2013
4. Discussion of the Paper of Ghosh, Taylor, and Sargent.
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Korn, Edward L.
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FAILURE time data analysis , *COMPETING risks , *MATHEMATICAL models , *META-analysis , *ONCOLOGY , *CLINICAL trials - Abstract
The author discusses the article "Meta-Analysis for Surrogacy: Accelerated Failure Time (AFT) Models and Semi-Competing Risks Modeling," by D. Ghosh et al. He focuses on some issues raised by the methodology for trial-level surrogacy used in the paper. He restricts attention to oncology trials as their illustrative application is in oncology. He also argues on the recommendation by the authors not to use composite time-to-event endpoints in the trial.
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- 2012
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5. Modeling duration of FSA operating and farm ownership loan guarantees
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Long, Deng, Ahrendsen, Bruce L., L. Dixon, Bruce, and B. Dodson, Charles
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- 2016
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6. Multivariate Failure Time Analysis: A Discussion of Papers by Oakes; Pons, Kaddour and De Turckheim; and Prentice and Cai
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David Oakes and Bruce W. Turnbull
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Cognitive science ,Multivariate survival analysis ,Multivariate statistics ,Disease onset ,Actuarial science ,Randomized controlled trial ,law ,Health insurance ,Disease ,Competing risks ,Psychology ,Rodent carcinogenicity ,law.invention - Abstract
Many important and interesting problems involving multivariate failure analysis occur in investigations in the physical, biomedical and social sciences. Examples of different types of such problems have been given by Lawless (1982, Chapter 10) and by Hougaard (1987). Applications of multivariate survival analysis usually fall into one of two categories, let’s call them “parallel” and “serial”. In “parallel” systems, several failure (e.g., disease) processes are acting concurrently and, at any time, each unit can experience a failure of a particular type, unless that type of failure has already occurred in that unit and it has not been censored. (We are excluding the competing risks situation where failure of one type censors failure times of other types in that unit This is a different problem.) Examples in this category would be the times to first puncture of the front and rear tires of my bicycle, or times to disease onset in a litter matched rodent carcinogenicity assay. Other examples are numerous: twin studies, group randomized trials, studies in humans of disease of the lung, eye, ear, kidney, breast, hip joint, etc. In the “serial” category, I consider applications with repeated, successive or recurrent events. An example would be times of successive flat tires on the front wheel of my bicycle. Generally, reliability analyses of repairable systems would fall in this category. As an example of an application in biomedicine, Oakes (this volume) uses times of multiple mammary tumors in rats. Other applications have included studies in humans of asthma attacks, epileptic attacks, bladder, skin and colon cancer. In the social sciences, the events studied may be timings of major purchases, of initiation of new employment, of health insurance claims, etc.
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- 1992
7. An Overview of Ross Prentice's Contributions to Statistical Science.
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Lin, D.
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Ross Prentice's work has had the most profound impact on the theory and practice of statistics. His research interests range from survival analysis, longitudinal data analysis, epidemiologic designs and analysis, to genomic studies. His contributions are so broad and so deep that it would be impossible to provide a comprehensive review in any limited amount of space. In this commentary, I will attempt to give a brief tour of some of his statistical work, focusing on ten of my favorite papers of his. I will describe the main ideas in those papers and their influence on the directions of statistical research and on the designs and analysis of medical studies. I will mention a few stories along the way. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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8. The Proportional Hazard Model for Purchase Timing: A Comparison of Alternative Specifications.
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Seetharaman, P. B. and Chintagunta, Pradeep K.
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CONSUMER behavior ,CONSUMPTION (Economics) ,RISK assessment ,DETERGENTS ,PAPER towels ,CONSUMERS ,HOUSEHOLDS ,MARKETING ,INDUSTRIAL psychology - Abstract
We use the proportional hazard model (PHM) to study purchase-timing behavior of households in two product categories: laundry detergents and paper towels. The PHM decomposes a household's instantaneous probability of buying the product at a point of time into two components: the baseline hazard that captures the household's intrinsic purchase pattern over time and the covariate function that captures the effects of marketing variables on the household's purchase timing decision. We compare the continuous-time and discrete-time PHMs, where the latter explicitly accounts for households' shopping trips that do not involve purchase of the product. We find that the discrete-time PHM empirically outperforms the continuous-time PHM in terms of explaining the observed purchase outcomes. We compare five different parametric specifications of the baseline hazard, and find that the three-parameter expo-power specification outperforms the exponential, Erlang-2, Weibull, and log-logistic specifications. We use a cause-specific, competing-risks PHM to distinguish between two types of purchase events that differ in terms of whether or not they were preceded by a shopping trip that involved purchase of the product. Such a cause-specific, competing-risks PHM is shown to outperform the traditional discrete-time PHM. We then estimate a nonparametric version of the PHM and find that it does not offer any additional insights compared to the parsimonious parametric PHM. Finally, we accommodate unobserved heterogeneity across households by allowing all of the parameters of the PHM to follow a discrete distribution across households whose locations and supports are nonparametrically estimated from the data. We find evidence for substantial unobserved heterogeneity in the data, both in the parameters of marketing variables and in the baseline hazards. This study will be a useful reference to researchers hoping to use the PHM to study event times. [ABSTRACT FROM AUTHOR]
- Published
- 2003
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9. A Bayesian destructive generalized Waring regression cure model with a variance decomposition and application in colorectal cancer data.
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Rodrigues, Josemar, Cancho, Vicente G., Balakrishnan, N., and Suzuki, Adriano K.
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COLORECTAL cancer ,COMPETING risks ,REGRESSION analysis ,FRAILTY ,IMMUNE system - Abstract
In this paper, we develop a Bayesian two-stage cure rate model whose biological destructive mechanism (immune system) of the competing risk factors of death is suitable for detecting the impact on the long-term survival function of three sources of variance well-known in accident theory: randomness, liability and proneness. From a survival analysis viewpoint, proneness means individual effect or destructive mechanism and liability corresponds to external effects or covariates. The flexibility of the generalized Waring frailty distribution in capturing these variance components separately enables one to understand the nature of overdispersion of the risk factors involved in studying risk of death after a long-term treatment of the patient. A new cure rate, involving covariate and destructive mechanism, is developed here under a competing cause scenario. A simulation study and an application to colorectal cancer data set are finally presented to demonstrate the usefulness of the proposed model and the inferential results developed here. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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10. Extreme Behavior of Competing Risks with Random Sample Size.
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Bai, Long, Hu, Kaihao, Wen, Conghua, Tan, Zhongquan, and Ling, Chengxiu
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EXTREME value theory ,COMPETING risks ,SAMPLE size (Statistics) ,AT-risk behavior ,DATA modeling - Abstract
The advances in science and technology have led to vast amounts of complex and heterogeneous data from multiple sources of random sample length. This paper aims to investigate the extreme behavior of competing risks with random sample sizes. Two accelerated mixed types of stable distributions are obtained as the extreme limit laws of random sampling competing risks under linear and power normalizations, respectively. The theoretical findings are well illustrated by typical examples and numerical studies. The developed methodology and models provide new insights into modeling complex data across numerous fields. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Chen-Burr XII Model as a Competing Risks Model with Applications to Real-Life Data Sets.
- Author
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Kalantan, Zakiah I., Binhimd, Sulafah M. S., Salem, Heba N., AL-Dayian, Gannat R., EL-Helbawy, Abeer A., and Elaal, Mervat K. Abd
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PROBABILITY density function ,MAXIMUM likelihood statistics ,HAZARD function (Statistics) ,COMPETING risks ,PARAMETER estimation - Abstract
In this paper Chen-Burr XII distribution is constructed and graphical description of the probability density function, hazard rate and reversed hazard rate functions of the proposed model is obtained. Also, some statistical characteristics of the Chen-Burr XII distribution are discussed and some new models as sub-models from the Chen-Burr XII distribution are introduced. Moreover, maximum likelihood estimation of the parameters, reliability, hazard rate and reversed hazard rate functions of the Chen-Burr XII distribution are considered. Also, the asymptotic confidence intervals of the distribution parameters, reliability, hazard rate and reversed hazard rate functions are presented. Finally, three real life data sets are applied to prove how the Chen-Burr XII distribution can be applied in real life and to confirm its superiority over some existing distributions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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12. Validation of discrete time‐to‐event prediction models in the presence of competing risks
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Jean-François Timsit, Rachel Heyard, Leonhard Held, University of Zurich, and Heyard, Rachel
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Statistics and Probability ,Biometry ,area under the curve ,Computer science ,Calibration (statistics) ,Mean squared prediction error ,610 Medicine & health ,Machine learning ,computer.software_genre ,Competing risks ,Risk Assessment ,competing events ,Intensive care ,Humans ,1804 Statistics, Probability and Uncertainty ,2613 Statistics and Probability ,discrete time‐to‐event model ,Event (probability theory) ,validation ,prediction error ,Models, Statistical ,business.industry ,Statistics ,calibration slope ,Pneumonia, Ventilator-Associated ,Probability and statistics ,10060 Epidemiology, Biostatistics and Prevention Institute (EBPI) ,General Medicine ,Research Papers ,Intensive Care Units ,Discrete time and continuous time ,dynamic prediction models ,Calibration ,Pseudomonas aeruginosa ,Probability and Uncertainty ,Artificial intelligence ,Statistics, Probability and Uncertainty ,business ,computer ,Predictive modelling ,Research Paper - Abstract
Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time-to-event outcomes, but the literature on validation methods for discrete time-to-event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time-to-event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator-associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data. Keywords: area under the curve; calibration slope; competing events; discrete time-to-event model; dynamic prediction models; prediction error; validation.
- Published
- 2019
13. Life expectancy improvement for multiple cure distributions
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Shanoja R. Naik and Peter Adamic
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Statistics and Probability ,Economics and Econometrics ,Life expectancy ,media_common.quotation_subject ,Hazard function ,Competing risks ,03 medical and health sciences ,0302 clinical medicine ,Multiple decrement ,Economics ,030212 general & internal medicine ,Financial services ,media_common ,Mixture model ,Actuarial science ,business.industry ,Mathematical finance ,Certainty ,Original Research Paper ,030220 oncology & carcinogenesis ,Probability distribution ,Statistics, Probability and Uncertainty ,Cure distribution ,business - Abstract
In many circumstances, the increase in life expectancy when certain causes of death are eliminated is sought. These calculations are typically based on the assumption that the causes in question are simply omitted, which is equivalent to the causes being taken out of consideration, from the outset, with certainty. In this paper, we propose models whereby probability distributions for the cures of specific causes of death over time can be incorporated so as to more accurately predict the increase in life expectancy that would ensue. The theoretical results are applied to a real data set involving Diabetes and HIV-related deaths from Denver, Colorado, United States of America, between the years 1990 and 2015 inclusive.
- Published
- 2020
14. On the relation between the cause‐specific hazard and the subdistribution rate for competing risks data: The Fine–Gray model revisited
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Hans C. van Houwelingen, Martin Schumacher, and Hein Putter
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Statistics and Probability ,Biometry ,cause-specific hazard ,media_common.quotation_subject ,Inference ,Competing risks ,Risk Assessment ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Covariate ,cause‐specific hazard ,Econometrics ,030212 general & internal medicine ,0101 mathematics ,cumulative incidence ,Reduction factor ,competing risks ,media_common ,Analysis of Variance ,Models, Statistical ,subdistribution hazard ,General Medicine ,proportional hazards ,Research Papers ,Feeling ,Subdistribution hazard ,Statistics, Probability and Uncertainty ,Psychology ,Gray (horse) ,Cause specific hazard ,Research Paper - Abstract
The Fine–Gray proportional subdistribution hazards model has been puzzling many people since its introduction. The main reason for the uneasy feeling is that the approach considers individuals still at risk for an event of cause 1 after they fell victim to the competing risk of cause 2. The subdistribution hazard and the extended risk sets, where subjects who failed of the competing risk remain in the risk set, are generally perceived as unnatural . One could say it is somewhat of a riddle why the Fine–Gray approach yields valid inference. To take away these uneasy feelings, we explore the link between the Fine–Gray and cause‐specific approaches in more detail. We introduce the reduction factor as representing the proportion of subjects in the Fine–Gray risk set that has not yet experienced a competing event. In the presence of covariates, the dependence of the reduction factor on a covariate gives information on how the effect of the covariate on the cause‐specific hazard and the subdistribution hazard relate. We discuss estimation and modeling of the reduction factor, and show how they can be used in various ways to estimate cumulative incidences, given the covariates. Methods are illustrated on data of the European Society for Blood and Marrow Transplantation.
- Published
- 2020
15. Life Assessment for Motorized Spindle with Zero Traumatic Failure Data Based on Subdistribution Competing Risk Model.
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Zhang, Yingzhi, Zhou, Yutong, Chen, Bingkun, and Zhang, Han
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COMPETING risks ,WEIBULL distribution ,CONFIDENCE intervals - Abstract
Considering the influence of performance degradation on a product's traumatic failure, under the condition that only degradation data are observed and no traumatic failure data are observed, this paper proposes a subdistribution competing risk model to achieve a motorized spindle life assessment. This paper assumes that the failure rate ratio of the tested products does not change with time under different stress levels. Basic reliability with zero traumatic failure data is modeled by a unilateral confidence limit method under a two-parameter Weibull distribution. Performance degradation data are taken as covariates. The regression coefficients of the covariates are calculated by SPSS software. Then, a subdistribution competing risk model is constructed, which reflects the dependency relationship between reliability and performance degradation, and the product's reliability life can be evaluated accordingly. The correctness and advantages of the model built in this paper are verified by a case analysis combined with the performance degradation information of a motorized spindle. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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16. A Bayes Analysis of a Dependent Competing Risk Model Based on Marshall–Olkin Bivariate Weibull Distribution.
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Gupta, Ankita, Ranjan, Rakesh, Gupta, Akanksha, and Upadhyay, Satyanshu K.
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WEIBULL distribution ,COMPETING risks ,BAYESIAN analysis ,GIBBS sampling ,ACCELERATED life testing ,MODELS & modelmaking ,BIVARIATE analysis - Abstract
This paper considers a competing risk model defined on the basis of minimum of two dependent failures where the two failures are assumed to jointly follow Marshall–Olkin bivariate Weibull distribution. This paper explores some important features of corresponding likelihood functions and performs a full Bayesian analysis of the model for data resulting from normal as well as accelerated life tests. The accelerated model is described by regressing the scale parameters of the model through inverse power-law relationship. Posterior-based inferences are drawn using the Gibbs sampler algorithm after specifying proper but vague priors for the model parameters. The numerical illustration is provided using real datasets. The performance of the model is assured by Bayesian tools of model compatibility and then the entertained model is compared with the competing risk model based on Marshall–Olkin bivariate exponential assumption. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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17. The Joint-Specific BACH classification: A predictor of outcome in prosthetic joint infection
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Martin A. McNally, Martina Galea Wismayer, Eve Robertson-Waters, Abtin Alvand, Ben Kendrick, Stephen M. McDonnell, Andrew J Hotchen, and Adrian Taylor
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medicine.medical_specialty ,education.field_of_study ,Research paper ,Referral ,business.industry ,Population ,MEDLINE ,Prosthetic joint infection ,General Medicine ,Competing risks ,Index score ,Quality of life ,Internal medicine ,medicine ,Outcome data ,education ,business - Abstract
Background There is currently no commonly accepted method of stratifying complexity of prosthetic joint infection (PJI). This study assesses a new classification, the Joint-Specific, Bone involvement, Anti-microbial options, Coverage of the soft tissues, Host status (JS-BACH) classification, for predicting clinical and patient reported outcomes in PJI. Methods Patients who received surgery for PJI at two centres in the UK between 2010 and 2015 were classified using JS-BACH as 'uncomplicated', 'complex' or 'limited treatment options'. Patient reported outcomes were recorded at 365-days following the index operation and included the EuroQol EQ-5D-3L index score and the EQ-visual analogue score (VAS). Clinical outcome data were obtained from the most recent follow-up appointment. Findings 220 patients met the inclusion criteria. At 365-days following the index operation, patients with 'uncomplicated' PJI reported similar EQ-index scores (0.730, SD:0.326) and EQ-VAS (79.4, SD:20.9) compared to the age-matched population. Scores for 'uncomplicated' PJI were significantly higher than patients classified as having 'complex' (EQ-index:0.515 SD:0.323, p = 0.012; EQ-VAS:68.4 SD:19.4, p = 0.042) and 'limited treatment options' PJI (EQ-index:0.333 SD:0.383, p
- Published
- 2021
18. Estimation for partially observed left truncation and right censored competing risks data from a generalized inverted exponential distribution with illustrations.
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Wang, Liang, Zhang, Chunfang, Wu, Shuo-Jye, Dey, Sanku, and Lio, Yuhlong
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DISTRIBUTION (Probability theory) ,COMPETING risks ,MAXIMUM likelihood statistics ,GIBBS sampling ,FISHER information - Abstract
In this paper, studies of competing risks model are considered when the observations are left-truncated and right-censored data. When the failure times of the competing risks are distributed by a generalized inverted exponential model with same scale but different shape parameters with partially observed failure causes, statistical inference for the unknown model parameters is discussed from classical and Bayesian approaches, respectively. Maximum likelihood estimators of the unknown parameters, along with associated existence and uniqueness, are established, and the asymptotic likelihood theory is also used to construct the confidence interval via the observed Fisher information matrix. Moreover, Bayesian estimates and the corresponding highest posterior density credible intervals are also obtained based a flexible Gamma-Beta prior, and a Gibbs sampling technique is constructed to compute associated estimates. Further, under a general practical assumption with order-restriction parameter case, classical and Bayesian estimations are also established under order restriction situations, respectively. Extensive Monte-Carlo simulations are carried out to investigate the performances of our results and two real-life examples are analyzed to show the applicability of the proposed methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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19. Joint AFT random-effect modeling approach for clustered competing-risks data.
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Hao, Lin, Ha, Il Do, Jeong, Jong-Hyeon, and Lee, Youngjo
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COMPETING risks ,GAUSSIAN distribution ,SURVIVAL analysis (Biometry) ,MEDICAL research ,RANDOM effects model - Abstract
Competing risks data arise when occurrence of an event hinders observation of other types of events, and they are encountered in various research areas including biomedical research. These data have been usually analyzed using the hazard-based models, not survival times themselves. In this paper, we propose a joint accelerated failure time (AFT) modeling approach to model clustered competing risks data. Times to competing events are assumed to be log-linear with normal errors and correlated through a scaled random effect that follows a zero-mean normal distribution. Inference on the model parameters is based on the h-likelihood. Performance of the proposed method is evaluated through extensive simulation studies. The simulation results show that the estimated regression parameters are robust against the violation of the assumed parametric distributions. The proposed method is illustrated with three real competing risks data sets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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20. Parameter Estimation of Birnbaum-Saunders Distribution under Competing Risks Using the Quantile Variant of the Expectation-Maximization Algorithm.
- Author
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Park, Chanseok and Wang, Min
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EXPECTATION-maximization algorithms ,COMPETING risks ,PARAMETER estimation ,CENSORING (Statistics) ,AUTOPSY ,FRACTURE mechanics ,FAILURE mode & effects analysis - Abstract
Competing risks models, also known as weakest-link models, are utilized to analyze diverse strength distributions exhibiting multi-modality, often attributed to various types of defects within the material. The weakest-link theory posits that a material's fracture is dictated by its most severe defect. However, multimodal problems can become intricate due to potential censoring, a common constraint stemming from time and cost limitations during experiments. Additionally, determining the mode of failure can be challenging due to factors like the absence of suitable diagnostic tools, costly autopsy procedures, and other obstacles, collectively referred to as the masking problem. In this paper, we investigate the distribution of strength for multimodal failures with censored data. We consider both full and partial maskings and present an EM-type parameter estimate for the Birnbaum-Saunders distribution under competing risks. We compare the results with those obtained from other distributions, such as lognormal, Weibull, and Wald (inverse-Gaussian) distributions. The effectiveness of the proposed method is demonstrated through two illustrative examples, as well as an analysis of the sensitivity of parameter estimates to variations in starting values. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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21. The Additive Xgamma-Burr XII Distribution: Properties, Estimation and Applications.
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Mohammad, Hebatalla H., Alamri, Faten S., Salem, Heba N., and EL-Helbawy, Abeer A.
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HAZARD function (Statistics) ,PROBABILITY density function ,MAXIMUM likelihood statistics ,COMPETING risks ,CONFIDENCE intervals - Abstract
This paper introduces a new four-parameter additive model, named xgamma-Burr XII distribution, by considering two competing risks: the former has the xgamma distribution and the latter has the Burr XII distribution. A graphical description of the xgamma-Burr XII distribution is presented, including plots of the probability density function, hazard rate and reversed hazard rate functions. The xgamma-Burr XII density has different shapes such as decreasing, unimodal, approximately symmetric and decreasing-unimodal. The main statistical properties of the proposed model are studied. The unknown model parameters, reliability, hazard rate and reversed hazard rate functions are estimated via the maximum likelihood method. The asymptotic confidence intervals of the parameters, reliability function, hazard rate function and reversed hazard rate function are also obtained. A simulation study is carried out to evaluate the performance of the maximum likelihood estimates. In addition, three real data are applied to show the superiority of the xgamma-Burr XII distribution over some known distributions in real-life applications. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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22. Control charts for monitoring relative risk rate in the presence of Weibull competing risks with censored and masked data.
- Author
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Ahmadi Nadi, Adel, Afshari, Robab, and Sadeghpour Gildeh, Bahram
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QUALITY control charts ,COMPETING risks ,CENSORING (Statistics) ,MONTE Carlo method ,MAXIMUM likelihood statistics - Abstract
Competing risks data frequently appear in real-world operations like quality inspections, survival analysis, reliability tests, and clinical trials. From the quality point of view, relative risk rate can be considered an interesting quality indicator in analyzing the competing risks data for statistical process monitoring purposes. The relative risk rate measures the proportion of failures caused by the primary risk among a set of competing risks. This paper introduces two Shewhart-type control charts for monitoring the relative risk rate when the lifetimes of competing risks are independent Weibull random variables. The former chart is constructed based on the maximum likelihood estimation method, while the latter is developed based on the Bayesian approach. The proposed control charts can be applied in Phase II. The calculation of the Bayesian control charts and the evaluation of both process monitoring techniques have been done based on Monte Carlo simulations. The performance of the proposed control charts has been examined based on the average run length metric. The illustrative example is also discussed in detail to demonstrate the applicability of the proposed methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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23. Phase‐type models involving restarting and instantaneous transitions, with applications to degradation and maintenance.
- Author
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Lindqvist, Bo Henry
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MARKOV processes ,COMPETING risks ,ABSORPTION - Abstract
A phase‐type distribution is the distribution of time to absorption for an absorbing continuous‐time finite state Markov chain. The paper first reviews the extension of the phase‐type setting to modeling of competing risks by introducing multiple absorbing states. The main study of the paper is the further extension to introducing instantaneous transitions at certain stages of the original models. The motivation is from applications to repair and maintenance, bringing failed systems into working ones by instantaneous repair actions. Two slightly different approaches are studied. The first one is based on restarting the original Markov chain upon absorption, leading to the consideration of a Markov renewal process. The second approach involves periodically inspected systems, where maintenance actions are modeled by instantaneous transitions made at regular inspection times. For both approaches are suggested measures of reliability and maintenance based on long run properties. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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24. A simple and robust parametric shared frailty model for recurrent events with the competing risk of death: An application to the Carvedilol Prospective Randomized Cumulative Survival trial.
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Sun, Jiren and Cook, Thomas
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COMPETING risks ,CARVEDILOL ,TREATMENT effectiveness ,RESEARCH personnel ,PARAMETRIC modeling - Abstract
Many non-fatal events can be considered recurrent in that they can occur repeatedly over time, and some researchers may be interested in the trajectory and relative risk of non-fatal events. With the competing risk of death, the treatment effect on the mean number of recurrent events is non-identifiable since the observed mean is a function of both the recurrent event and terminal event processes. In this paper, we assume independence between the non-fatal and the terminal event process, conditional on the shared frailty, to fit a parametric model that recovers the trajectory of, and identifies the effect of treatment on, the non-fatal event process in the presence of the competing risk of death. Simulation studies are conducted to verify the reliability of our estimators. We illustrate the method and perform model diagnostics using the Carvedilol Prospective Randomized Cumulative Survival trial which involves heart-failure events. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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25. Inference of improved adaptive progressively censored competing risks data for Weibull lifetime models.
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Elshahhat, Ahmed and Nassar, Mazen
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MONTE Carlo method ,MARKOV chain Monte Carlo ,CENSORING (Statistics) ,COMPETING risks ,WEIBULL distribution ,FISHER information ,LATENT variables - Abstract
Recently, an improved adaptive Type-II progressive censoring scheme is proposed to ensure that the experimental time will not pass a pre-fixed time and ends the test after recording a pre-fixed number of failures. This paper studies the inference of the competing risks model from Weibull distribution under the improved adaptive progressive Type-II censoring. For this goal, we used the latent failure time model with Weibull lifetime distributions with common shape parameters. The point and interval estimation problems of parameters, reliability and hazard rate functions using the maximum likelihood and Bayesian estimation methods are considered. Moreover, making use of the asymptotic normality of classical estimators and delta method, approximate intervals are constructed via the observed Fisher information matrix. Following the assumption of independent gamma priors, the Bayes estimates of the scale parameters have closed expressions, but when the common shape parameter is unknown, the Bayes estimates cannot be formed explicitly. To solve this difficulty, we recommend using Markov chain Monte Carlo routine to compute the Bayes estimates and to construct credible intervals. A comprehensive Monte Carlo simulation is conducted to judge the behavior of the offered methods. Ultimately, analysis of electrodes data from the life-test of high-stress voltage endurance is provided to illustrate all proposed inferential procedures. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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26. The collapse of credit booms: a competing risks analysis.
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Castro, Vítor and Martins, Rodrigo
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COMPETING risks ,RISK assessment ,ECONOMIC expansion ,CREDIT ,CENTRAL banking industry - Abstract
Purpose: This paper analyses the collapse of credit booms into soft landings or systemic banking crises. Design/methodology/approach: A discrete-time competing risks duration model is employed to disentangle the factors behind the length of benign and harmful credit booms. Findings: The results show that economic growth and monetary authorities play the major role in explaining the differences in the length and outcome of credit booms. Moreover, both types of credit expansions display positive duration dependence, i.e. both are more likely to end as they grow older, but hard landing credit booms have proven to be longer than those that land softly. Originality/value: This paper contributes to our understanding of what affects the length of credit booms and why some end up creating havoc and others do not. In particular, it calls the attention to the important role that Central Bank independence plays regarding credit booms length and outcome. [ABSTRACT FROM AUTHOR]
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- 2020
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27. Bayesian Analysis for Dependent Progressively Censored Weibull Competing Risks Using Copulas.
- Author
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Salem, Maram Magdy
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COMPETING risks ,BAYESIAN analysis ,FAILURE time data analysis ,CENSORING (Statistics) ,CENSORSHIP ,COPULA functions ,DEPENDENCE (Statistics) - Abstract
In many reliability studies, the experimental units may fail due to one of several causes of failure. It is usually assumed that the competing risks of failure are independent. In many practical situations, however, the interpretation of the failure modes makes the assumption of independence unreasonable. Copulas are considered an effective tool for modeling the dependence structure among the multiple competing risks. This paper presents Bayesian analysis of progressively Type-II censored dependent competing risks data using copulas. The analysis is performed under the assumption of binomial progressive random removals and Weibull failure times, where unit failure occurs due to only one of the competing risks. Bayesian point and interval estimates of the unknown parameters are derived using different Archimedean copulas with non-conjugate prior distributions. A simulation study is carried out to assess the performance of the proposed techniques under different dependence structures. A real data set is analyzed for illustrative purposes. [ABSTRACT FROM AUTHOR]
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- 2023
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- View/download PDF
28. Joint inference for competing risks data using multiple endpoints.
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Wen, Jiyang, Hu, Chen, and Wang, Mei‐Cheng
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COMPETING risks ,HOSPITAL admission & discharge ,COVID-19 treatment ,CLINICAL trials - Abstract
Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID‐19 in‐patient treatment clinical trial, where the outcomes of COVID‐19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications. [ABSTRACT FROM AUTHOR]
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- 2023
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29. Cumulative Incidence Functions for Competing Risks Survival Data from Subjects with COVID-19.
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Haque, Mohammad Anamul and Cortese, Giuliana
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COMPETING risks ,COVID-19 ,COVID-19 pandemic ,LUNGS ,STATISTICAL learning ,SURVIVAL analysis (Biometry) ,INFLUENZA vaccines - Abstract
Competing risks survival analysis is used to answer questions about the time to occurrence of events with the extension of multiple causes of failure. Studies that investigate how clinical features and risk factors of COVID-19 are associated with the survival of patients in the presence of competing risks (CRs) are limited. The main objective of this paper is, under a CRs setting, to estimate the Cumulative Incidence Function (CIF) of COVID-19 death, the CIF of other-causes death, and the probability of being cured in subjects with COVID-19, who have been under observation from the date of symptoms to the date of death or exit from the study because they are cured. In particular, we compared the non-parametric estimator of the CIF based on the naive technique of Kaplan–Meier (K–M) with the Aalen–Johansen estimator based on the cause-specific approach. Moreover, we compared two of the most popular regression approaches for CRs data: the cause-specific hazard (CSH) and the sub-distribution hazard (SDH) approaches. A clear overestimation of the CIF function over time was observed under the K–M estimation technique. Moreover, exposure to asthma, diabetes, obesity, older age, male sex, black and indigenous races, absence of flu vaccine, admission to the ICU, and the presence of other risk factors, such as immunosuppression and chronic kidney, neurological, liver, and lung diseases, significantly increased the probability of COVID-19 death. The highest hazard ratio of 2.03 was observed for subjects with an age greater than 70 years compared with subjects aged 50–60 years. The SDH approach showed slightly higher survival probabilities compared with the CSH approach. An important foundation for producing precise individualized predictions was provided by the competing risks regression models discussed in this paper. This foundation allowed us, in general, to more realistically model complex data, such as the COVID-19 data, and can be used, for instance, by many modern statistical learning and personalized medicine techniques to obtain more accurate conclusions. [ABSTRACT FROM AUTHOR]
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- 2023
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30. Ceramic-on-ceramic articulation in press-fit total hip arthroplasty as a potential reason for early failure, what about the survivors: a ten year follow-up
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D. C. Baas, Gino M. M. J. Kerkhoffs, D. Haverkamp, J. T. van Loon, Kim T. M. Opdam, A. M. J. S. Vervest, H. M. van der Vis, Inger N. Sierevelt, AMS - Ageing & Vitality, AMS - Musculoskeletal Health, Graduate School, Amsterdam Movement Sciences, Orthopedic Surgery and Sports Medicine, APH - Personalized Medicine, APH - Quality of Care, and AMS - Sports
- Subjects
Reoperation ,musculoskeletal diseases ,medicine.medical_specialty ,Ceramics ,Arthroplasty, Replacement, Hip ,Aseptic loosening ,Competing risks ,Prosthesis Design ,03 medical and health sciences ,0302 clinical medicine ,Primary outcome ,medicine ,Humans ,Orthopedics and Sports Medicine ,Cumulative incidence ,Survivors ,Early failure ,Retrospective Studies ,030222 orthopedics ,Original Paper ,business.industry ,Primary stability ,Incidence (epidemiology) ,Ceramic-on-ceramic ,equipment and supplies ,Surgery ,Prosthesis Failure ,Treatment Outcome ,surgical procedures, operative ,030220 oncology & carcinogenesis ,Orthopedic surgery ,Total hip arthroplasty ,Hip Prosthesis ,business ,Follow-Up Studies - Abstract
Purpose In press-fit total hip arthroplasty (THA), primary stability is needed to avoid micromotion and hereby aseptic loosening, the main reason for early revision. High aseptic loosening revision rates of the seleXys TH+ cup (Mathys Medical) with Ceramys ceramic-on-ceramic (CoC) bearing are seen in literature. Since CoC is presumed to overcome long-term wear-related revisions, the reason for early failure of this cup is important to clarify. The aim is to investigate its ten year outcomes and differentiate between potential causes and identify risk factors for aseptic loosening. Methods Retrospective screening of a prospectively documented series of 315 THAs was performed. Primary outcome was cumulative incidence of cup revision due to aseptic loosening. Secondary outcomes were component revision and reoperation. Additionally, potential predictive factors for aseptic loosening were evaluated. Results At the median follow-up of 9.7 years [IQR 4.4; 10.3], 48 TH+ (15.2%) were revised due to aseptic loosening. Competing risk analysis showed a ten year cumulative incidence of cup revision due to aseptic loosening of 15.6% (95% CI 12.0–20.2). Stabilization of early revision rates was observed, following a high rate of respectively 81.3% (n = 39) and 95.8% (n = 46) within the first two and three years. No significant predictive factors for aseptic loosening were found. Conclusion The ten year results of seleXys TH+ cup with Ceramys CoC bearing showed an unacceptable high aseptic loosening rate, which stabilized over time after a high early failure incidence. This could be attributed to a problem with osseointegration during the transition of primary to definitive stability.
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- 2021
31. The Efficacy of Upfront Intracranial Radiation with TKI Compared to TKI Alone in the NSCLC Patients Harboring EGFR Mutation and Brain Metastases
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Luhua Wang, Jingbo Wang, Qinfu Feng, Zongmei Zhou, Chunyu Wang, Wenqing Wang, Xiaotong Lu, Zhouguang Hui, Junling Li, Jun Liang, Jianping Xiao, Zefen Xiao, Jima Lv, Tao Zhang, Dongfu Chen, Lei Deng, Xiaozhen Wang, Xin Wang, and Nan Bi
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Oncology ,medicine.medical_specialty ,medicine.medical_treatment ,Competing risks ,03 medical and health sciences ,0302 clinical medicine ,Internal medicine ,brain metastases ,tyrosine kinase inhibitors ,Overall survival ,Retrospective analysis ,Medicine ,030212 general & internal medicine ,non-small cell lung cancer ,business.industry ,Similar time ,respiratory tract diseases ,Radiation therapy ,radiation ,Egfr mutation ,Median time ,030220 oncology & carcinogenesis ,Non small cell ,EGFR mutation ,business ,Research Paper - Abstract
Introduction: The high intracranial efficacy of EGFR-TKI challenges the role of upfront intracranial radiation therapy (RT) in non-small cell lung cancer (NSCLC) patients with EGFR mutation and brain metastases (BM). Therefore, we conducted a retrospective analysis to demonstrate the role of upfront RT in these patients. Materials and Methods: Patients that had histologically confirmed NSCLC with EGFR mutation, brain metastases, and received TKI or upfront RT with TKI were included in this study. Intracranial progression was estimated using the Fine-Gray competing risks model. Kaplan-Meier analysis and Log-rank test were used to evaluate and compare intracranial progression-free survival (iPFS), systemic PFS (sPFS), time to second-line systematic therapy (SST) and overall survival (OS). Results: Among the 93 patients included, 53 patients received upfront RT and TKI, and 40 patients received TKI only. Upfront RT group showed lower intracranial progression risk with adjusted SHR 0.38 (95% CI, 0.19 to 0.75, P= 0.006) and longer median time to sPFS (15.6 vs 8.9 months, P= 0.009). There were 9 out of 36 (25%) and 16 out of 34 (47.1%) patients who had oligo-progression received salvage RT in the RT group and TKI group, respectively. After the salvage RT, upfront RT did not prolong the median time to SST (23.6 vs 18.9 months, P=0.862) and OS (median time, 35.4 vs 35.8 months, P=0.695) compared to TKI alone. Conclusion: Compared to upfront intracranial RT, the salvage RT to oligo-progressive disease allowed patients getting TKI to have similar time on initial TKI and OS despite worse iPFS. The best timing of intracranial RT remains to be further verified.
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- 2019
32. Role of Adjuvant Chemotherapy in Advanced Stage Upper Urinary Tract Urothelial Carcinoma after Radical Nephroureterectomy: Competing Risk Analysis after Propensity Score-Matching
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Hyun Moo Lee, Kyunga Kim, Minyong Kang, Byong Chang Jeong, Han Yong Choi, Se Hoon Park, Heejin Yoo, Seong Soo Jeon, Si Hyun Sung, Seong Il Seo, and Hwang Gyun Jeon
- Subjects
medicine.medical_specialty ,Multivariate analysis ,Locally-advanced ,Adjuvant chemotherapy ,business.industry ,Hazard ratio ,030232 urology & nephrology ,Urology ,Competing risks ,Propensity score-matching ,Upper urinary tract cancer ,03 medical and health sciences ,Competing risk analysis ,0302 clinical medicine ,Oncology ,030220 oncology & carcinogenesis ,Propensity score matching ,medicine ,Clinical endpoint ,Positive Surgical Margin ,business ,Research Paper ,Upper urinary tract - Abstract
Objective: To determine whether adjuvant chemotherapy (ACH) influences cancer-specific mortality, bladder cancer-specific mortality, and other-cause mortality in patients with locally advanced upper tract urothelial carcinoma (UTUC) following radical nephroureterectomy (RNU) through the use of competing risk analysis. Methods: Among 785 patients with UTUC who underwent RNU from 1994 through 2015, we analyzed 338 individuals with locally advanced UTUC (pathologic T3-T4 and/or positive lymph nodes) without distant metastases. Patients were classified into two groups according to receipt of ACH. We performed a 1:1 propensity score-matching analysis between the ACH and no ACH group. The study endpoints were UTUC- and other cause-specific survivals. The association of potential risk factors with outcome was tested with the Fine and Gray regression model. Results: During a median follow-up duration of 31.5 months, rates of UTUC- and other cause-mortalities were 32.9% (n = 79) and 8.7% (n = 21), respectively. Of note, there were no significant differences in overall survival between the observation and ACH groups according to the competing risks of death (UTUC and other causes of death). Multivariate analysis showed that only older age at surgery (≥ 65 years; hazard ratio [HR] = 1.73), multifocality (HR = 1.74), and tumor size (HR = 1.92) remained as poor predictors of UTUC-specific survival. Additionally, positive surgical margin was only identified as independent predictor of other causes of death (HR = 4.23). Conclusion: In summary, postoperative chemotherapy failed to improve UTUC- and other cause-specific survival rates, based on competing risk analysis after propensity score-matching.
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- 2019
33. Nomogram Predicting Cause-Specific Mortality in Nonmetastatic Male Breast Cancer: A Competing Risk Analysis
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Minghua Cheng, Zeting Qiu, Huaqiang Zhou, Wenqi Huang, and Wei Sun
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Other cause-specific death ,0301 basic medicine ,Oncology ,medicine.medical_specialty ,Breast cancer-specific death ,Ajcc stage ,Competing risks ,Fine and Gray model ,03 medical and health sciences ,Predictive nomogram ,0302 clinical medicine ,Internal medicine ,Epidemiology ,medicine ,Nonmetastatic male breast cancer ,business.industry ,Cause specific mortality ,Competing risk nomogram ,Nomogram ,medicine.disease ,SEER database ,030104 developmental biology ,030220 oncology & carcinogenesis ,Male breast cancer ,Inclusion and exclusion criteria ,business ,Research Paper - Abstract
Introduction: Male breast cancer (MBC) is a rare tumor with few cases for research. Using the Surveillance, Epidemiology, and End Results program database, we carried out a competing risk analysis in patients with primary nonmetastatic MBC and built a predictive nomogram. Materials and Methods: We extracted primary nonmetastatic MBC patients according to the inclusion and exclusion criteria. Cumulative incidence function (CIF) and proportional subdistribution hazard model were adopted to explore risk factors for breast cancer-specific death (BCSD) and other cause-specific death (OCSD). Then we built a nomogram to predict the 3-year, 5-year and 8-year probabilities of BCSD and OCSD. C-indexes, Brier scores and calibration curves were chosen for validation. Results: We identified 1,978 nonmetastatic MBC patients finally. CIF analysis showed that the 3-year, 5-year and 8-year mortalities were 5.2%, 10.6% and 16.5% for BCSD, and 6.1%, 9.6% and 14.4% for OCSD. After adjustment of Fine and Gray models, black race, PR (-), advanced T/N/grade and no surgery were independently associated with BCSD. Meanwhile, elderly, unmarried status, advanced AJCC stage and no chemotherapy resulted in OCSD more possibly. A graphic nomogram was developed according to the coefficients from the Fine and Gray models. The calibration curves displayed exceptionally, with C-indexes nearly larger than 0.700 and Brier scores nearly smaller than 0.100. Conclusion: The competing risk nomogram showed good accuracy for predictive prognosis in nonmetastatic MBC patients. It was a useful implement to evaluate crude mortalities of BCSD and OCSD, and help clinicians to choose appropriate therapeutic plans.
- Published
- 2019
34. Step‐stress life‐testing under tampered random variable modeling for Weibull distribution in presence of competing risk data.
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Sultana, Farha, Çetinkaya, Çağatay, and Kundu, Debasis
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COMPETING risks , *MAXIMUM likelihood statistics , *CENSORING (Statistics) , *WEIBULL distribution , *BAYESIAN field theory , *BAYESIAN analysis , *RANDOM variables - Abstract
In this paper, we have considered the classical and Bayesian inference of the unknown parameters of the lifetime distribution based on the observations obtained from a simple step‐stress life‐testing (SSLT) experiment and when more than one cause of failures are observed. We have used the Tampered Random Variable (TRV) approach. The main advantage of the TRV approach is that it can be easily extended to a multiple step‐stress model as well as for different lifetime distributions. In this paper, it is assumed that the lifetime of the experimental units at each stress level follows Weibull distribution with the same shape parameter and different scale parameters. Further, we have introduced different tempering co‐efficient for different causes of failures. The maximum likelihood estimators and the associated asymptotic confidence intervals are obtained based on Type‐II censored observations. Further, we have considered the Bayesian inference of the unknown model parameters based on a fairly general class prior distributions. An extensive simulation study is performed to examine the performances of the proposed method, and the analysis of a real data set has been provided to show how the method can be used in practice. We have compared the TRV model with some of the other existing models, and the TRV model provides a better fit in terms of information theoretic criteria. We have also provided some optimality criteria, to determine the optimal stress change time and some sensitivity analyses have been performed. Most of the methods can be extended for other lifetime distributions also. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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35. Including individual customer lifetime value and competing risks in tree-based lapse management strategies.
- Author
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Valla, Mathias, Milhaud, Xavier, and Olympio, Anani
- Abstract
A retention strategy based on an enlightened lapse model is a powerful profitability lever for a life insurer. Some machine learning models are excellent at predicting lapse, but from the insurer's perspective, predicting which policyholder is likely to lapse is not enough to design a retention strategy. In our paper, we define a lapse management framework with an appropriate validation metric based on Customer Lifetime Value and profitability. We include the risk of death in the study through competing risks considerations in parametric and tree-based models and show that further individualization of the existing approaches leads to increased performance. We show that survival tree-based models outperform parametric approaches and that the actuarial literature can significantly benefit from them. Then, we compare, on real data, how this framework leads to increased predicted gains for a life insurer and discuss the benefits of our model in terms of commercial and strategic decision-making. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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36. The analysis of semi‐competing risks data using Archimedean copula models.
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Wang, Antai, Guo, Ziyan, Zhang, Yilong, and Wu, Jihua
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RISK assessment , *PARAMETER estimation , *COMPETING risks , *DATA analysis - Abstract
In this paper, we derive the copula‐graphic estimator (Zheng and Klein) for marginal survival functions using Archimedean copula models based on competing risks data subject to univariate right censoring and prove its uniform consistency and asymptotic properties. We then propose a novel parameter estimation method based on the semi‐competing risks data using Archimedean copula models. Based on our estimation strategy, we propose a new model selection procedure. We also describe an easy way to accommodate possible covariates in data analysis using our strategies. Simulation studies have shown that our parameter estimate outperforms the estimator proposed by Lakhal, Rivest and Abdous for the Hougaard model and the model selection procedure works quite well. We fit a leukemia dataset using our model and end our paper with some discussion. [ABSTRACT FROM AUTHOR]
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- 2024
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37. Comparison between inverse-probability weighting and multiple imputation in Cox model with missing failure subtype.
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Guo, Fuyu, Langworthy, Benjamin, Ogino, Shuji, and Wang, Molin
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DISEASE risk factors ,PROCEDURE manuals ,MISSING data (Statistics) ,COMPETING risks ,ONCOLOGY nursing ,COLORECTAL cancer - Abstract
Identifying and distinguishing risk factors for heterogeneous disease subtypes has been of great interest. However, missingness in disease subtypes is a common problem in those data analyses. Several methods have been proposed to deal with the missing data, including complete-case analysis, inverse-probability weighting, and multiple imputation. Although extant literature has compared these methods in missing problems, none has focused on the competing risk setting. In this paper, we discuss the assumptions required when complete-case analysis, inverse-probability weighting, and multiple imputation are used to deal with the missing failure subtype problem, focusing on how to implement these methods under various realistic scenarios in competing risk settings. Besides, we compare these three methods regarding their biases, efficiency, and robustness to model misspecifications using simulation studies. Our results show that complete-case analysis can be seriously biased when the missing completely at random assumption does not hold. Inverse-probability weighting and multiple imputation estimators are valid when we correctly specify the corresponding models for missingness and for imputation, and multiple imputation typically shows higher efficiency than inverse-probability weighting. However, in real-world studies, building imputation models for the missing subtypes can be more challenging than building missingness models. In that case, inverse-probability weighting could be preferred for its easy usage. We also propose two automated model selection procedures and demonstrate their usage in a study of the association between smoking and colorectal cancer subtypes in the Nurses' Health Study and Health Professional Follow-Up Study. [ABSTRACT FROM AUTHOR]
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- 2024
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38. The role of chemotherapy in patients with T1bN0M0 triple-negative breast cancer: a real-world competing risk analysis
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Yunyan Lu, Ouou Yang, Junling He, Tian Lan, Zujian Hu, Haibin Xu, and Hua Luo
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Oncology ,Chemotherapy ,medicine.medical_specialty ,business.industry ,medicine.medical_treatment ,chemotherapy ,Competing risks ,survival analysis ,Internal medicine ,Cohort ,Epidemiology ,Propensity score matching ,triple-negative breast cancer ,Medicine ,T1bN0M0 breast cancer ,In patient ,business ,Survival analysis ,Triple-negative breast cancer ,Research Paper - Abstract
The objective of the present study was to implement Kaplan-Meier analysis, competing risk analysis, and propensity score matching to evaluate whether the patients with T1bN0M0 triple-negative breast (TNBC) could benefit from adjuvant chemotherapy. A total of 1849 patients were identified in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. All eligible patients were divided into two cohorts, the chemotherapy (1155 patients) and the no-chemotherapy (694 patients) cohorts. Similar 5-year breast cancer-specific survival (BCSS) was observed in the chemotherapy and no-chemotherapy cohorts (96.1% vs. 96.0%, p=0.820). The results of the competing risk analysis showed a comparable 5-year breast cancer-specific death (BCSD) in both groups (chemotherapy 3.6% vs. no-chemotherapy 3.4%, p=0.778). Also, a higher 5-year other causes death (OCD) was observed in the no-chemotherapy cohort (0.7% vs. 5.4%, p
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- 2020
39. Nomogram to Predict Cancer-Specific Survival in Patients with Pancreatic Acinar Cell Carcinoma: A Competing Risk Analysis
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Xiaojun Lin, Fangting Duan, Zhiyuan Cai, Yu Zhang, Chaobin He, and Shengping Li
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Oncology ,medicine.medical_specialty ,genetic structures ,overall survival ,Competing risks ,nomogram ,03 medical and health sciences ,cancer-specific survival ,0302 clinical medicine ,Internal medicine ,Medicine ,In patient ,Stage (cooking) ,acinar cell carcinoma ,Receiver operating characteristic ,business.industry ,Proportional hazards model ,Nomogram ,030220 oncology & carcinogenesis ,Cohort ,030211 gastroenterology & hepatology ,prognosis ,business ,Research Paper ,Pancreatic Acinar Cell Carcinoma - Abstract
Background: The objective of this study was to evaluate the probability of cancer-specific death of patients with acinar cell carcinoma (ACC) and build nomograms to predict overall survival (OS) and cancer-specific survival (CSS) of these patients. Methods: Data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed with ACC between 2004 and 2014 were retrospectively collected. Cancer-specific mortality and competing risk mortality were evaluated. Nomograms for estimating 1-, 2- and 3-year OS and CSS were established based on Cox regression model and Fine and Grey's model. The precision of the 1-, 2- and 3-year survival of the nomograms was evaluated and compared using the area under receiver operating characteristic (ROC) curve (AUC). Results: The study cohort included 227 patients with ACC. The established nomograms were well calibrated, and had good discriminative ability, with a concordance index (C-index) of 0.742 for OS prediction and 0.766 for CSS prediction. The nomograms displayed better discrimination power than 7th or 8th edition Tumor-Node-Metastasis (TNM) stage systems in training set and validation set for predicting both OS and CSS. The AUC values of the nomogram predicting 1-, 2-, and 3-year OS rates were 0.784, 0.797 and 0.805, respectively, which were higher than those of 7th or 8th edition TNM stage systems. Regard to the prediction of CSS rates, the AUC values of the nomogram were also higher than those of 7th or 8th edition TNM stage systems. Conclusion: We evaluated the 1-, 2- and 3-year OS and CSS in patients with ACC for the first time. Our nomograms showed relatively good performance and could be considered as convenient individualized predictive tools for prognosis.
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- 2018
40. Frailty index as a predictor of all-cause and cause-specific mortality in a Swedish population-based cohort
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Nancy L. Pedersen, Miao Jiang, Ralf Kuja-Halkola, Ida K. Karlsson, Andrea D. Foebel, Juulia Jylhävä, and Sara Hägg
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Adult ,Male ,medicine.medical_specialty ,Aging ,Health Status ,Frailty Index ,Disease ,030204 cardiovascular system & hematology ,Competing risks ,early risk marker ,Cohort Studies ,03 medical and health sciences ,0302 clinical medicine ,Swedish population ,Epidemiology ,cancer mortality ,medicine ,cardiovascular disease mortality ,Humans ,Dementia ,030212 general & internal medicine ,competing risks ,Aged ,Aged, 80 and over ,Sweden ,Frailty ,business.industry ,Construct validity ,Cause specific mortality ,Cell Biology ,Middle Aged ,medicine.disease ,Twin study ,3. Good health ,030220 oncology & carcinogenesis ,Cohort ,Female ,business ,030217 neurology & neurosurgery ,Research Paper ,Demography - Abstract
BackgroundFrailty is a complex manifestation of aging and associated with increased risk of mortality and poor health outcomes. Younger individuals (under 65 years) typically have low levels of frailty and are less-studied in this respect. Also, the relationship between the Rockwood frailty index (FI) and cause-specific mortality in community settings is understudied.MethodsWe created and validated a 42-item Rockwood-based FI in The Swedish Adoption/Twin Study of Aging (n=1477; 623 men, 854 women; aged 29-95 years) and analyzed its association with all-cause and cause-specific mortality in up to 30-years of follow-up. Deaths due to cardiovascular disease (CVD), cancer, dementia and other causes were considered as competing risks.ResultsOur FI demonstrated construct validity as its associations with age, sex and mortality were similar to the existing literature. The FI was independently associated with increased risk for all-cause mortality in younger (ConclusionsThe FI showed good predictive value for all-cause mortality especially in the younger group. The FI predicted CVD mortality risk in women, whereas in men it captured vulnerability to death from various causes.
- Published
- 2017
41. Estimation of the Modified Weibull Additive Hazards Regression Model under Competing Risks.
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Rehman, Habbiburr, Chandra, Navin, Emura, Takeshi, and Pandey, Manju
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REGRESSION analysis ,COMPETING risks ,MONTE Carlo method ,PROPORTIONAL hazards models ,WEIBULL distribution ,CENSORING (Statistics) - Abstract
The additive hazard regression model plays an important role when the excess risk is the quantity of interest compared to the relative risks, where the proportional hazard model is better. This paper discusses parametric regression analysis of survival data using the additive hazards model with competing risks in the presence of independent right censoring. In this paper, the baseline hazard function is parameterized using a modified Weibull distribution as a lifetime model. The model parameters are estimated using maximum likelihood and Bayesian estimation methods. We also derive the asymptotic confidence interval and the Bayes credible interval of the unknown parameters. The finite sample behaviour of the proposed estimators is investigated through a Monte Carlo simulation study. The proposed model is applied to liver transplant data. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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- View/download PDF
42. 288 Note: A Note on Kimball's Paper 'Models for the Estimation of Competing Risks from Grouped Data'
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M. C. Pike
- Subjects
Statistics and Probability ,Estimation ,General Immunology and Microbiology ,Applied Mathematics ,Statistics ,Econometrics ,Economics ,General Medicine ,General Agricultural and Biological Sciences ,Competing risks ,General Biochemistry, Genetics and Molecular Biology ,Grouped data - Published
- 1970
43. Whole of population-based cohort study of recovery time from COVID-19 in New South Wales Australia
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John M. Kaldor, Victoria Pye, Timothy Dobbins, Gregory J. Dore, Duleepa Jayasundara, Bette Liu, Gail V. Matthews, and Paula J. Spokes
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2019-20 coronavirus outbreak ,Pediatrics ,medicine.medical_specialty ,Coronavirus disease 2019 (COVID-19) ,Proportional hazards model ,business.industry ,Health Policy ,Public Health, Environmental and Occupational Health ,COVID-19 ,Obstetrics and Gynecology ,Competing risks ,Age and gender ,Psychiatry and Mental health ,Population based cohort ,Infectious Diseases ,Emergency response ,Recovery ,Pediatrics, Perinatology and Child Health ,Internal Medicine ,Medicine ,Geriatrics and Gerontology ,Cohort study ,business ,Research Paper - Abstract
Background COVID-19 results in persisting symptoms but there is little systematically collected data estimating recovery time following infection. Methods We followed 94% of all COVID-19 cases diagnosed in the Australian state of New South Wales between January and May 2020 using 3-4 weekly telephone interviews and linkage to hospitalisation and death data to determine if they had recovered from COVID-19 based on symptom resolution. Proportional hazards models with competing risks were used to estimate time to recovery adjusted for age and gender. Findings In analyses 2904 cases were followed for recovery (median follow-up time 16 days, range 1-122, IQR 11-24).There were 2572 (88.6%) who reported resolution of symptoms (262/2572 were also hospitalised), 224 (7.8%) had not recovered at last contact (28/224 were also hospitalised), 51 (1.8%) died of COVID-19, and 57 (2.0%) were hospitalised without a documented recovery date. Of those followed, 20% recovered by 10 days, 60% at 20, 80% at 30, 91% at 60, 93% at 90 and 96% at 120 days. Compared to those aged 30-49 years, those 0-29 years were more likely to recover (aHR 1.22, 95%CI 1.10-1.34) while those aged 50-69 and 70+ years were less likely to recover (aHR respectively 0.74, 95%CI 0.67-0.81 and 0.63, 95%CI 0.56-0.71). Men were faster to recover than women (aHR 1.20, 95%CI 1.11-1.29) and those with pre-existing co-morbidities took longer to recover than those without (aHR 0.90, 95%CI 0.83-0.98). Interpretation In a setting where most cases of COVID-19 were ascertained and followed, 80% of those with COVID-19 recover within a month, but about 5% will continue to experience symptoms 3 months later. Funding NSW Health Emergency Response Priority Research Projects
- Published
- 2021
44. The influence of antibiotic-loaded cement spacers on the risk of reinfection after septic two-stage hip revision surgery
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Christoph Böhler, Alexandra Kaider, Reinhard Windhager, Kevin Staats, Stephan E. Puchner, Johannes Holinka, Irene K. Sigmund, and Florian Sevelda
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Adult ,Male ,Risk ,Microbiology (medical) ,medicine.medical_specialty ,Prosthesis-Related Infections ,Spacer ,Arthroplasty, Replacement, Hip ,Kaplan-Meier Estimate ,Competing risks ,Cohort Studies ,Young Adult ,03 medical and health sciences ,0302 clinical medicine ,Hip revision ,Vancomycin ,Periprosthetic joint infection ,medicine ,Humans ,Cumulative incidence ,030212 general & internal medicine ,Stage (cooking) ,Antibiotic loaded cement ,Survival analysis ,Aged ,Aged, 80 and over ,Original Paper ,030222 orthopedics ,Hip ,business.industry ,Significant difference ,Bone Cements ,Cumulative incidence function ,General Medicine ,Middle Aged ,Anti-Bacterial Agents ,Surgery ,Treatment Outcome ,Infectious Diseases ,Female ,Gentamicins ,business - Abstract
Purpose The aim of this study was the evaluation of possible outcome differences of patients undergoing two-stage hip exchange with antibiotic-loaded spacers, compared to patients without an interim spacer implantation. Methods We evaluated 46 patients undergoing two-stage hip revision surgery. Twenty-five patients received an interim ALS. Additional to a Kaplan–Meier survival analysis, a competing risk analysis was performed to estimate the cumulative incidence function for re-revisions due to infection accounting for death as a competing event. Results Nine patients (seven non-ALS vs. two ALS) had to undergo re-revision surgery due to reinfection of the hip joint. The non-ALS group showed a risk of re-revision of 19% (95% CI 5–38%) at 12 and 24 months and 30% (95% CI 12–51%) at 36 months. The group with ALS implantation displayed a 0% risk of re-revision surgery in the first 36 months. The Gray test revealed a significant difference in the cumulative incidence between both observed groups (p = 0.026). Conclusion Our findings suggest that ALS implantation significantly reduces the risk of reinfection after two-stage hip revision surgery.
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- 2017
45. Association between HIV infection and outcomes of care among medicare enrollees with breast cancer
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Sumedha Chhatre, Marilyn M. Schapira, Ravishankar Jayadevappa, and David S. Metzger
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medicine.medical_specialty ,Phase-specific cost of care ,Human immunodeficiency virus (HIV) ,medicine.disease_cause ,Competing risks ,Medicare ,01 natural sciences ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,Disabled ,Interim ,Internal medicine ,Survivorship curve ,Epidemiology ,medicine ,030212 general & internal medicine ,0101 mathematics ,Mortality ,lcsh:R5-920 ,business.industry ,Proportional hazards model ,010102 general mathematics ,virus diseases ,HIV ,General Medicine ,Competing risk ,medicine.disease ,3. Good health ,Concomitant ,lcsh:Medicine (General) ,business ,Research Paper - Abstract
Background: To assess the interaction of breast cancer, HIV infection, Medicare disability status, cancer stage and its implications for outcomes, after accounting for competing risks among female, fee-for-service Medicare enrollees. Methods: We used data from Surveillance, Epidemiology and End Results (SEER) -Medicare (2000–2013). From primary female breast cancer cases diagnosed between 2001 and 2011, we identified those with HIV infection. We used Generalized Linear Model for phase-specific incremental cost of HIV, Cox regression for association between HIV and all-cause mortality, and Fine and Gray competing risk models to assess hazard of breast cancer-specific mortality by HIV status. We also studied this association for subgroups of cancer stage and disability status. Findings: Of 164,080 eligible cases of breast cancer, 176 had HIV infection. Compared to HIV-uninfected patients, HIV infected patients had 16% higher cost in initial phase, and 80% higher cost in interim stage of care, and at least two times higher mortality (all-cause and breast cancer-specific), after accounting for competing risk. Among disabled enrollees, HIV-infected patients had higher risk of all-cause and breast cancer-specific mortality, compared to HIV-uninfected patients. Interpretation: Female fee-for-service Medicare enrollees with breast cancer experience higher initial and interim phase cost and worse survival in the presence of HIV. This association was also significant among disabled Medicare enrollees. Medicare is the single largest source of federal financing for HIV care. Burden on Medicare will grow exponentially due to higher proportion of disabled among HIV-infected enrollees, longer survival among HIV- infected persons, increased HIV incidence in older adults, and increased age related risk of breast cancer. Future research can identify the pathways via which HIV infection affects cost and mortality, and develop integrated strategies for effective management of concomitant breast cancer and HIV and inform survivorship guidelines. Funding: National Institute on Aging, National Institutes of Health, Grant # R21AG34870-1A1 Keywords: Breast cancer, HIV, Medicare, Disabled, Mortality, Competing risk, Phase-specific cost of care
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- 2019
46. Gaussian Copula Regression Modeling for Marker Classification Metrics with Competing Risk Outcomes.
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Vásquez, Alejandro Román, Escarela, Gabriel, Reyes-Cervantes, Hortensia Josefina, and Núñez-Antonio, Gabriel
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REGRESSION analysis ,COMPETING risks ,PROPORTIONAL hazards models ,RECEIVER operating characteristic curves ,SKEWNESS (Probability theory) - Abstract
Decisions regarding competing risks are usually based on a continuous-valued marker toward predicting a cause-specific outcome. The classification power of a marker can be summarized using the time-dependent receiver operating characteristic curve and the corresponding area under the curve (AUC). This paper proposes a Gaussian copula-based model to represent the joint distribution of the continuous-valued marker, the overall survival time, and the cause-specific outcome. Then, it is used to characterize the time-varying ROC curve in the context of competing risks. Covariate effects are incorporated by linking linear components to the skewed normal distribution for the margin of the marker, a parametric proportional hazards model for the survival time, and a logit model for the cause of failure. Estimation is carried out using maximum likelihood, and a bootstrap technique is implemented to obtain confidence intervals for the AUC. Information-criteria strategies are employed to find a parsimonious model. The performance of the proposed model is evaluated in simulation studies, considering different sample sizes and censoring distributions. The methods are illustrated with the reanalysis of a prostate cancer clinical trial. The joint regression strategy produces a straightforward and flexible model of the time-dependent ROC curve in the presence of competing risks, enhancing the understanding of how covariates may affect the discrimination of a marker. [ABSTRACT FROM AUTHOR]
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- 2024
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47. Targeted maximum likelihood estimation for causal inference in survival and competing risks analysis.
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Rytgaard, Helene C. W. and van der Laan, Mark J.
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MAXIMUM likelihood statistics ,CAUSAL inference ,COMPETING risks ,RISK assessment ,CAUSAL models ,INFERENTIAL statistics ,SURVIVAL analysis (Biometry) - Abstract
Targeted maximum likelihood estimation (TMLE) provides a general methodology for estimation of causal parameters in presence of high-dimensional nuisance parameters. Generally, TMLE consists of a two-step procedure that combines data-adaptive nuisance parameter estimation with semiparametric efficiency and rigorous statistical inference obtained via a targeted update step. In this paper, we demonstrate the practical applicability of TMLE based causal inference in survival and competing risks settings where event times are not confined to take place on a discrete and finite grid. We focus on estimation of causal effects of time-fixed treatment decisions on survival and absolute risk probabilities, considering different univariate and multidimensional parameters. Besides providing a general guidance to using TMLE for survival and competing risks analysis, we further describe how the previous work can be extended with the use of loss-based cross-validated estimation, also known as super learning, of the conditional hazards. We illustrate the usage of the considered methods using publicly available data from a trial on adjuvant chemotherapy for colon cancer. R software code to implement all considered algorithms and to reproduce all analyses is available in an accompanying online appendix on Github. [ABSTRACT FROM AUTHOR]
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- 2024
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48. ASSESSMENT OF CRUDE PROBABILITIES OF COMPETING RISKS FOR TIME TO EVENT IN SHOCK MODEL.
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Munoli, S. B. and Jadhav, Abhijeet
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The aim of this paper is to study the shock model with two kinds of shocks from competing risks outlook. A component or system having two failure modes is considered. The two crude probabilities of failures are modelled and assessed. For assessment of the crude probabilities, Type I (time) censoring life test is considered. Monte-Carlo simulation experiments are used to validate the results derived. [ABSTRACT FROM AUTHOR]
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- 2023
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49. COMPARATIVE ANALYSIS OF COMPETING RISKS MODELS USING COVARIATES.
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Talawar, A. S. and Rangoli, A. M.
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In the present paper, we have considered prostate cancer data to study the competing risks. We considered various covariates, their impact on survival for different causes and also the impact of covariates on causes in absence and presence of competing risks. We conclude that for a cause cancer, except for weight index (Wt), history of cardiovascular disease (hx) and serum haemoglobin (HG), all the covariates are statistically significant both in absence and presence of competing risks. In case of Cardio Vascular Disease (CVD), except for history of cardiovascular disease (hx), all are statistically insignificant. Further, we have considered subjects having failure time greater than or equal to 11 months and observed significant changes in the covariates for different causes. [ABSTRACT FROM AUTHOR]
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- 2023
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50. Long-term outcomes of 5-year survivors without recurrence after the complete resection of non-small cell lung cancer after lobectomy: a landmark analysis in consideration of competing risks
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Koike, Yutaro, Aokage, Keiju, Wakabayashi, Masashi, Ikeno, Takashi, Onodera, Ken, Samejima, Joji, Miyoshi, Tomohiro, Tane, Kenta, Suzuki, Kenji, and Tsuboi, Masahiro
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- 2024
- Full Text
- View/download PDF
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