The Experts below are selected from a list of 22059 Experts worldwide ranked by ideXlab platform
Helene Jacqmingadda - One of the best experts on this subject based on the ideXlab platform.
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joint Latent Class Model for longitudinal data and interval censored semi competing events application to dementia
Biometrics, 2016Co-Authors: Anais Rouanet, Pierre Joly, Jeanfrancois Dartigues, Cecile Proustlima, Helene JacqmingaddaAbstract:Joint Models are used in ageing studies to investigate the association between longitudinal markers and a time-to-event, and have been extended to multiple markers and/or competing risks. The competing risk of death must be considered in the elderly because death and dementia have common risk factors. Moreover, in cohort studies, time-to-dementia is interval-censored since dementia is assessed intermittently. So subjects can develop dementia and die between two visits without being diagnosed. To study predementia cognitive decline, we propose a joint Latent Class Model combining a (possibly multivariate) mixed Model and an illness-death Model handling both interval censoring (by accounting for a possible unobserved transition to dementia) and semi-competing risks. Parameters are estimated by maximum-likelihood handling interval censoring. The correlation between the marker and the times-to-events is captured by Latent Classes, homogeneous sub-groups with specific risks of death, dementia, and profiles of cognitive decline. We propose Markovian and semi-Markovian versions. Both approaches are compared to a joint Latent-Class Model for competing risks through a simulation study, and applied in a prospective cohort study of cerebral and functional ageing to distinguish different profiles of cognitive decline associated with risks of dementia and death. The comparison highlights that among subjects with dementia, mortality depends more on age than on duration of dementia. This Model distinguishes the so-called terminal predeath decline (among healthy subjects) from the predementia decline.
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joint Latent Class Model for longitudinal data and interval censored semi competing events application to dementia
arXiv: Methodology, 2015Co-Authors: Anais Rouanet, Pierre Joly, Jeanfrancois Dartigues, Cecile Proustlima, Helene JacqmingaddaAbstract:Joint Models are used in ageing studies to investigate the association between longitudinal markers and a time-to-event, and have been extended to multiple markers and/or competing risks. The competing risk of death must be considered in the elderly because death and dementia have common risk factors. Moreover, in cohort studies, time-to-dementia is interval-censored because dementia is only assessed intermittently. So subjects can become demented and die between two follow-up visits without being diagnosed. To study pre-dementia cognitive decline, we propose a joint Latent Class Model combining a (possibly multivariate) mixed Model and an illness-death Model handling both interval censoring (by accounting for a possible unobserved transition to dementia) and semi-competing risks. Parameters are estimated by maximum likelihood handling interval censoring. The correlation between the marker and the times-to-events is captured by Latent Classes, homogeneous groups with specific risks of death and dementia and profiles of cognitive decline. We propose markovian and semi-markovian versions. Both approaches are compared to a joint Latent Class Model for standard competing risks through a simulation study, and then applied in a prospective cohort study of cerebral and functional ageing to distinguish different profiles of cognitive decline associated with risks of dementia and death. The comparison highlights that among demented subjects, mortality depends more on age than duration of dementia. This Model distinguishes the so-called terminal pre-death decline (among non-demented subjects) from the pre-dementia decline.
Mohammad H. Rahbar - One of the best experts on this subject based on the ideXlab platform.
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a joint Latent Class Model for Classifying severely hemorrhaging trauma patients
BMC Research Notes, 2015Co-Authors: Mohammad H. Rahbar, Deborah J. Del Junco, Hanwen Huang, Jing Ning, Erin E. Fox, Sangbum Choi, Jin Piao, Chuan Hong, Elaheh RahbarAbstract:In trauma research, “massive transfusion” (MT), historically defined as receiving ≥10 units of red blood cells (RBCs) within 24 h of admission, has been routinely used as a “gold standard” for quantifying bleeding severity. Due to early in-hospital mortality, however, MT is subject to survivor bias and thus a poorly defined criterion to Classify bleeding trauma patients. Using the data from a retrospective trauma transfusion study, we applied a Latent-Class (LC) mixture Model to identify severely hemorrhaging (SH) patients. Based on the joint distribution of cumulative units of RBCs and binary survival outcome at 24 h of admission, we applied an expectation-maximization (EM) algorithm to obtain Model parameters. Estimated posterior probabilities were used for patients’ Classification and compared with the MT rule. To evaluate predictive performance of the LC-based Classification, we examined the role of six clinical variables as predictors using two separate logistic regression Models. Out of 471 trauma patients, 211 (45 %) were MT, while our Latent SH Classifier identified only 127 (27 %) of patients as SH. The agreement between the two Classification methods was 73 %. A non-ignorable portion of patients (17 out of 68, 25 %) who died within 24 h were not Classified as MT but the SH group included 62 patients (91 %) who died during the same period. Our comparison of the predictive Models based on MT and SH revealed significant differences between the coefficients of potential predictors of patients who may be in need of activation of the massive transfusion protocol. The traditional MT Classification does not adequately reflect transfusion practices and outcomes during the trauma reception and initial resuscitation phase. Although we have demonstrated that joint Latent Class Modeling could be used to correct for potential bias caused by misClassification of severely bleeding patients, improvement in this approach could be made in the presence of time to event data from prospective studies.
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A joint Latent Class Model for Classifying severely hemorrhaging trauma patients
BMC Research Notes, 2015Co-Authors: Mohammad H. Rahbar, Hanwen Huang, Jing Ning, Erin E. Fox, Sangbum Choi, Jin Piao, Chuan Hong, Deborah J. Junco, Elaheh Rahbar, John B. HolcombAbstract:Background In trauma research, “massive transfusion” (MT), historically defined as receiving ≥10 units of red blood cells (RBCs) within 24 h of admission, has been routinely used as a “gold standard” for quantifying bleeding severity. Due to early in-hospital mortality, however, MT is subject to survivor bias and thus a poorly defined criterion to Classify bleeding trauma patients. Methods Using the data from a retrospective trauma transfusion study, we applied a Latent-Class (LC) mixture Model to identify severely hemorrhaging (SH) patients. Based on the joint distribution of cumulative units of RBCs and binary survival outcome at 24 h of admission, we applied an expectation-maximization (EM) algorithm to obtain Model parameters. Estimated posterior probabilities were used for patients’ Classification and compared with the MT rule. To evaluate predictive performance of the LC-based Classification, we examined the role of six clinical variables as predictors using two separate logistic regression Models. Results Out of 471 trauma patients, 211 (45 %) were MT, while our Latent SH Classifier identified only 127 (27 %) of patients as SH. The agreement between the two Classification methods was 73 %. A non-ignorable portion of patients (17 out of 68, 25 %) who died within 24 h were not Classified as MT but the SH group included 62 patients (91 %) who died during the same period. Our comparison of the predictive Models based on MT and SH revealed significant differences between the coefficients of potential predictors of patients who may be in need of activation of the massive transfusion protocol. Conclusions The traditional MT Classification does not adequately reflect transfusion practices and outcomes during the trauma reception and initial resuscitation phase. Although we have demonstrated that joint Latent Class Modeling could be used to correct for potential bias caused by misClassification of severely bleeding patients, improvement in this approach could be made in the presence of time to event data from prospective studies.
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a Latent Class Model for defining severe hemorrhage experience from the prommtt study
Journal of Trauma-injury Infection and Critical Care, 2013Co-Authors: Mohammad H. Rahbar, Deborah J. Del Junco, Hanwen Huang, Jing Ning, Erin E. Fox, Xuan Zhang, Martin A. Schreiber, Karen J. Brasel, Eileen M. Bulger, Charles E. WadeAbstract:BACKGROUNDSeveral predictive Models have been developed to identify trauma patients who have had severe hemorrhage (SH) and may need a massive transfusion (MT) protocol. However, almost all these Models define SH as the transfusion of 10 or more units of red blood cells (RBCs) within 24 hours of eme
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A Latent Class Model for defining severe hemorrhage
Journal of Trauma and Acute Care Surgery, 2013Co-Authors: Mohammad H. Rahbar, Deborah J. Del Junco, Hanwen Huang, Jing Ning, Erin E. Fox, Xuan Zhang, Martin A. Schreiber, Karen J. Brasel, Eileen M. Bulger, Charles E. WadeAbstract:Background Several predictive Models have been developed to identify trauma patients who have had severe hemorrhage (SH) and may need a massive transfusion protocol (MTP). However, almost all these Models define SH as the transfusion of ≥10 units of red blood cells (RBCs) within 24 hours of ED admission (aka massive transfusion, MT). This definition excludes some patients with SH, especially those who die before a 10th unit of RBCs could be transfused, which calls the validity of these prediction Models into question. We show how a Latent Class Model could improve the accuracy of identifying the SH patients.
Anais Rouanet - One of the best experts on this subject based on the ideXlab platform.
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joint Latent Class Model for longitudinal data and interval censored semi competing events application to dementia
Biometrics, 2016Co-Authors: Anais Rouanet, Pierre Joly, Jeanfrancois Dartigues, Cecile Proustlima, Helene JacqmingaddaAbstract:Joint Models are used in ageing studies to investigate the association between longitudinal markers and a time-to-event, and have been extended to multiple markers and/or competing risks. The competing risk of death must be considered in the elderly because death and dementia have common risk factors. Moreover, in cohort studies, time-to-dementia is interval-censored since dementia is assessed intermittently. So subjects can develop dementia and die between two visits without being diagnosed. To study predementia cognitive decline, we propose a joint Latent Class Model combining a (possibly multivariate) mixed Model and an illness-death Model handling both interval censoring (by accounting for a possible unobserved transition to dementia) and semi-competing risks. Parameters are estimated by maximum-likelihood handling interval censoring. The correlation between the marker and the times-to-events is captured by Latent Classes, homogeneous sub-groups with specific risks of death, dementia, and profiles of cognitive decline. We propose Markovian and semi-Markovian versions. Both approaches are compared to a joint Latent-Class Model for competing risks through a simulation study, and applied in a prospective cohort study of cerebral and functional ageing to distinguish different profiles of cognitive decline associated with risks of dementia and death. The comparison highlights that among subjects with dementia, mortality depends more on age than on duration of dementia. This Model distinguishes the so-called terminal predeath decline (among healthy subjects) from the predementia decline.
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joint Latent Class Model for longitudinal data and interval censored semi competing events application to dementia
arXiv: Methodology, 2015Co-Authors: Anais Rouanet, Pierre Joly, Jeanfrancois Dartigues, Cecile Proustlima, Helene JacqmingaddaAbstract:Joint Models are used in ageing studies to investigate the association between longitudinal markers and a time-to-event, and have been extended to multiple markers and/or competing risks. The competing risk of death must be considered in the elderly because death and dementia have common risk factors. Moreover, in cohort studies, time-to-dementia is interval-censored because dementia is only assessed intermittently. So subjects can become demented and die between two follow-up visits without being diagnosed. To study pre-dementia cognitive decline, we propose a joint Latent Class Model combining a (possibly multivariate) mixed Model and an illness-death Model handling both interval censoring (by accounting for a possible unobserved transition to dementia) and semi-competing risks. Parameters are estimated by maximum likelihood handling interval censoring. The correlation between the marker and the times-to-events is captured by Latent Classes, homogeneous groups with specific risks of death and dementia and profiles of cognitive decline. We propose markovian and semi-markovian versions. Both approaches are compared to a joint Latent Class Model for standard competing risks through a simulation study, and then applied in a prospective cohort study of cerebral and functional ageing to distinguish different profiles of cognitive decline associated with risks of dementia and death. The comparison highlights that among demented subjects, mortality depends more on age than duration of dementia. This Model distinguishes the so-called terminal pre-death decline (among non-demented subjects) from the pre-dementia decline.
Lars Holmberg - One of the best experts on this subject based on the ideXlab platform.
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a Latent Class Model for competing risks
Statistics in Medicine, 2017Co-Authors: Mark Rowley, Hans Garmo, M Van Hemelrijck, Wahyu Wulaningsih, Birgitta Grundmark, Bjorn Zethelius, Niklas Hammar, Goran Walldius, Masato Inoue, Lars HolmbergAbstract:Survival data analysis becomes complex when the proportional hazards assumption is violated at population level or when crude hazard rates are no longer estimators of marginal ones. We develop a Bayesian survival analysis method to deal with these situations, on the basis of assuming that the complexities are induced by Latent cohort or disease heterogeneity that is not captured by covariates and that proportional hazards hold at the level of individuals. This leads to a description from which risk-specific marginal hazard rates and survival functions are fully accessible, 'decontaminated' of the effects of informative censoring, and which includes Cox, random effects and Latent Class Models as special cases. Simulated data confirm that our approach can map a cohort's substructure and remove heterogeneity-induced informative censoring effects. Application to data from the Uppsala Longitudinal Study of Adult Men cohort leads to plausible alternative explanations for previous counter-intuitive inferences on prostate cancer. The importance of managing cardiovascular disease as a comorbidity in women diagnosed with breast cancer is suggested on application to data from the Swedish Apolipoprotein Mortality Risk Study. Copyright © 2017 John Wiley & Sons, Ltd.
Jiming Liu - One of the best experts on this subject based on the ideXlab platform.
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Extended Latent Class Models for collaborative recommendation
IEEE Transactions on Systems Man and Cybernetics - Part A: Systems and Humans, 2004Co-Authors: Kwok-wai Cheung, Kwok-ching Tsui, Jiming LiuAbstract:With the advent of the World Wide Web, providing just-in-time personalized product recommendations to customers now becomes possible. Collaborative recommender systems utilize correlation between customer preference ratings to identify "like-minded" customers and predict their product preference. One factor determining the success of the recommender systems is the prediction accuracy, which in many cases is limited by lacking adequate ratings (the sparsity problem). Recently, the use of Latent Class Model (LCM) has been proposed to alleviate this problem. In this paper, we first study how the LCM can be extended to handle customers and products outside the training set. In addition, we propose the use of a pair of LCMs (called dual Latent Class Model-DLCM), instead of a single LCM, to Model customers' likes and dislikes separately for enhancing the prediction accuracy. Experimental results based on the EachMovie dataset show that DLCM outperforms both LCM and the conventional correlation-based method when the available ratings are sparse.
