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Rubio FJ, Drikvandi R. MEGH: A parametric class of general hazard models for clustered survival data. Stat Methods Med Res 2022; 31:1603-1616. [PMID: 35668699 PMCID: PMC9315191 DOI: 10.1177/09622802221102620] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In many applications of survival data analysis, the individuals are treated in different medical centres or belong to different clusters defined by geographical or administrative regions. The analysis of such data requires accounting for between-cluster variability. Ignoring such variability would impose unrealistic assumptions in the analysis and could affect the inference on the statistical models. We develop a novel parametric mixed-effects general hazard (MEGH) model that is particularly suitable for the analysis of clustered survival data. The proposed structure generalises the mixed-effects proportional hazards and mixed-effects accelerated failure time structures, among other structures, which are obtained as special cases of the MEGH structure. We develop a likelihood-based algorithm for parameter estimation in general subclasses of the MEGH model, which is implemented in our R package MEGH. We propose diagnostic tools for assessing the random effects and their distributional assumption in the proposed MEGH model. We investigate the performance of the MEGH model using theoretical and simulation studies, as well as a real data application on leukaemia.
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Affiliation(s)
| | - Reza Drikvandi
- Department of Mathematical Sciences, 3057Durham University, Durham, UK
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2
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Caraka RE, Chen RC, Huang SW, Chiou SY, Gio PU, Pardamean B. Big data ordination towards intensive care event count cases using fast computing GLLVMS. BMC Med Res Methodol 2022; 22:77. [PMID: 35313816 PMCID: PMC8939086 DOI: 10.1186/s12874-022-01538-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 02/04/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND In heart data mining and machine learning, dimension reduction is needed to remove multicollinearity. Meanwhile, it has been proven to improve the interpretation of the parameter model. In addition, dimension reduction can also increase the time of computing in high dimensional data. METHODS In this paper, we perform high dimensional ordination towards event counts in intensive care hospital for Emergency Department (ED 1), First Intensive Care Unit (ICU1), Second Intensive Care Unit (ICU2), Respiratory Care Intensive Care Unit (RICU), Surgical Intensive Care Unit (SICU), Subacute Respiratory Care Unit (RCC), Trauma and Neurosurgery Intensive Care Unit (TNCU), Neonatal Intensive Care Unit (NICU) which use the Generalized Linear Latent Variable Models (GLLVM's). RESULTS During the analysis, we measure the performance and calculate the time computing of GLLVM by employing variational approximation and Laplace approximation, and compare the different distributions, including Negative Binomial, Poisson, Gaussian, ZIP, and Tweedie, respectively. GLLVMs (Generalized Linear Latent Variable Models), an extended version of GLMs (Generalized Linear Models) with latent variables, have fast computing time. The major challenge in latent variable modelling is that the function [Formula: see text] is not trivial to solve since the marginal likelihood involves integration over the latent variable u. CONCLUSIONS In a nutshell, GLLVMs lead as the best performance reaching the variance of 98% comparing other methods. We get the best model negative binomial and Variational approximation, which provides the best accuracy by accuracy value of AIC, AICc, and BIC. In a nutshell, our best model is GLLVM-VA Negative Binomial with AIC 7144.07 and GLLVM-LA Negative Binomial with AIC 6955.922.
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Affiliation(s)
- Rezzy Eko Caraka
- Executive Secretariat, National Research and Innovation Agency (BRIN), DKI Jakarta, 10340, Indonesia
- Department of Information Management, College of Informatics, Chaoyang University of Technology, Taichung City, 41349, Taiwan
| | - Rung-Ching Chen
- Department of Information Management, College of Informatics, Chaoyang University of Technology, Taichung City, 41349, Taiwan.
| | - Su-Wen Huang
- Department of Information Management, College of Informatics, Chaoyang University of Technology, Taichung City, 41349, Taiwan.
- Taichung Veterans General Hospital, Taichung City, 40705, Taiwan.
| | - Shyue-Yow Chiou
- Taichung Veterans General Hospital, Taichung City, 40705, Taiwan
| | - Prana Ugiana Gio
- Department of Mathematics, Universitas Sumatera Utara, Medan, 20155, Indonesia
| | - Bens Pardamean
- Bioinformatics and Data Science Research Center, Bina Nusantara University, DKI Jakarta, 11480, Indonesia
- Computer Science Department, Bina Nusantara University, DKI Jakarta, 11480, Indonesia
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3
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Ha ID, Lee Y. A review of h-likelihood for survival analysis. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE 2021. [DOI: 10.1007/s42081-021-00125-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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4
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Weeks DM, Parris MJ. A Bacillus thuringiensis kurstaki Biopesticide Does Not Reduce Hatching Success or Tadpole Survival at Environmentally Relevant Concentrations in Southern Leopard Frogs (Lithobates sphenocephalus). ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2020; 39:155-161. [PMID: 31499575 DOI: 10.1002/etc.4588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 08/21/2019] [Accepted: 08/30/2019] [Indexed: 06/10/2023]
Abstract
Amphibians are in global decline, and anthropogenic activities are known leading causes of their demise. Thus the interaction between agriculture and amphibian health has been examined for decades. Many facets of amphibian physiology and ecology place them at high risk among the nontarget organisms affected by agricultural byproducts. Research has shown that many chemicals and fertilizers affect amphibian growth, reproduction, and survival. The impacts differ based on the type of agricultural byproduct (e.g., chemical pesticide or nutrient-heavy fertilizer) and amphibian species, but the effects are usually negative. However, minimal research exists on how organic biopesticides interact with amphibian populations. Biopesticides utilize insecticidal bacteria as the active ingredient in lieu of synthetic chemicals. The inert ingredients present in biopesticide commercial products are considered safe to nontarget organisms. The present study tested the impacts of a commercial biopesticide on the survival of amphibian embryos and larvae. We found that expected environmental concentrations of the microbial biopesticide Monterrey B.t. did not significantly reduce survival in embryos or larvae. However, the higher doses used to assess threshold toxicity levels caused significant mortality. Our data suggest that biopesticides are not directly harmful to amphibian embryos or larvae in concentrations regularly applied for pest control. Environ Toxicol Chem 2019;39:155-161. © 2019 SETAC.
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Affiliation(s)
- Denita Mychele Weeks
- Department of Biological Sciences, Colorado Mesa University, Grand Junction, Colorado, USA
| | - Matthew James Parris
- Department of Biological Sciences, University of Memphis, Memphis, Tennessee, USA
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5
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Park E, Kwon S, Kwon J, Sylvester R, Ha ID. Penalized h‐likelihood approach for variable selection in AFT random‐effect models. STAT NEERL 2019. [DOI: 10.1111/stan.12179] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Eunyoung Park
- Department of StatisticsPukyong National University Busan South Korea
| | - Sookhee Kwon
- Department of StatisticsPukyong National University Busan South Korea
| | - Jihoon Kwon
- Department of Clinical Pharmacology and Therapeutics, College of MedicineSeoul National University Hospital Seoul South Korea
| | - Richard Sylvester
- European Organisation for Research and Treatment of Cancer Brussels Belgium
| | - Il Do Ha
- Department of StatisticsPukyong National University Busan South Korea
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6
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Park E, Ha ID. Penalized variable selection for accelerated failure time models with random effects. Stat Med 2019; 38:878-892. [PMID: 30411376 DOI: 10.1002/sim.8023] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Revised: 09/22/2018] [Accepted: 10/11/2018] [Indexed: 11/07/2022]
Abstract
Accelerated failure time (AFT) models allowing for random effects are linear mixed models under the log-transformation of survival time with censoring and describe dependence in correlated survival data. It is well known that the AFT models are useful alternatives to frailty models. To the best of our knowledge, however, there is no literature on variable selection methods for such AFT models. In this paper, we propose a simple but unified variable-selection procedure of fixed effects in the AFT random-effect models using penalized h-likelihood (HL). We consider four penalty functions (ie, least absolute shrinkage and selection operator (LASSO), adaptive LASSO, smoothly clipped absolute deviation (SCAD), and HL). We show that the proposed method can be easily implemented via a slight modification to existing h-likelihood estimation procedures. We thus demonstrate that the proposed method can also be easily extended to AFT models with multilevel (or nested) structures. Simulation studies also show that the procedure using the adaptive LASSO, SCAD, or HL penalty performs well. In particular, we find via the simulation results that the variable selection method with HL penalty provides a higher probability of choosing the true model than other three methods. The usefulness of the new method is illustrated using two actual datasets from multicenter clinical trials.
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Affiliation(s)
- Eunyoung Park
- Department of Statistics, Pukyong National University, Busan, South Korea
| | - Il Do Ha
- Department of Statistics, Pukyong National University, Busan, South Korea
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7
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Su C, Nešlehová JG, Wang W. Modelling hierarchical clustered censored data with the hierarchical Kendall copula. CAN J STAT 2019. [DOI: 10.1002/cjs.11484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Chien‐Lin Su
- Institute of Statistics, National Chiao Tung University Hsinchu Taiwan, ROC
- Department of Mathematics and StatisticsMcGill University Montréal Quebec Canada H3A 0G4
| | - Johanna G. Nešlehová
- Department of Mathematics and StatisticsMcGill University Montréal Quebec Canada H3A 0G4
| | - Weijing Wang
- Institute of Statistics, National Chiao Tung University Hsinchu Taiwan, ROC
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Tawiah R, Yau KKW, McLachlan GJ, Chambers SK, Ng SK. Multilevel model with random effects for clustered survival data with multiple failure outcomes. Stat Med 2018; 38:1036-1055. [PMID: 30474216 DOI: 10.1002/sim.8041] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 10/18/2018] [Accepted: 10/27/2018] [Indexed: 12/27/2022]
Abstract
We present a multilevel frailty model for handling serial dependence and simultaneous heterogeneity in survival data with a multilevel structure attributed to clustering of subjects and the presence of multiple failure outcomes. One commonly observes such data, for example, in multi-institutional, randomized placebo-controlled trials in which patients suffer repeated episodes (eg, recurrent migraines) of the disease outcome being measured. The model extends the proportional hazards model by incorporating a random covariate and unobservable random institution effect to respectively account for treatment-by-institution interaction and institutional variation in the baseline risk. Moreover, a random effect term with correlation structure driven by a first-order autoregressive process is attached to the model to facilitate estimation of between patient heterogeneity and serial dependence. By means of the generalized linear mixed model methodology, the random effects distribution is assumed normal and the residual maximum likelihood and the maximum likelihood methods are extended for estimation of model parameters. Simulation studies are carried out to evaluate the performance of the residual maximum likelihood and the maximum likelihood estimators and to assess the impact of misspecifying random effects distribution on the proposed inference. We demonstrate the practical feasibility of the modeling methodology by analyzing real data from a double-blind randomized multi-institutional clinical trial, designed to examine the effect of rhDNase on the occurrence of respiratory exacerbations among patients with cystic fibrosis.
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Affiliation(s)
- Richard Tawiah
- School of Medicine and Menzies Health Institute Queensland, Griffith University, Queensland, Australia
| | - Kelvin K W Yau
- Department of Management Sciences, City University of Hong Kong, Hong Kong
| | | | - Suzanne K Chambers
- School of Medicine and Menzies Health Institute Queensland, Griffith University, Queensland, Australia
| | - Shu-Kay Ng
- School of Medicine and Menzies Health Institute Queensland, Griffith University, Queensland, Australia
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Ha ID, Christian NJ, Jeong JH, Park J, Lee Y. Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties. Stat Methods Med Res 2016; 25:2488-2505. [PMID: 24619110 PMCID: PMC5771528 DOI: 10.1177/0962280214526193] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Competing risks data often exist within a center in multi-center randomized clinical trials where the treatment effects or baseline risks may vary among centers. In this paper, we propose a subdistribution hazard regression model with multivariate frailty to investigate heterogeneity in treatment effects among centers from multi-center clinical trials. For inference, we develop a hierarchical likelihood (or h-likelihood) method, which obviates the need for an intractable integration over the frailty terms. We show that the profile likelihood function derived from the h-likelihood is identical to the partial likelihood, and hence it can be extended to the weighted partial likelihood for the subdistribution hazard frailty models. The proposed method is illustrated with a dataset from a multi-center clinical trial on breast cancer as well as with a simulation study. We also demonstrate how to present heterogeneity in treatment effects among centers by using a confidence interval for the frailty for each individual center and how to perform a statistical test for such heterogeneity using a restricted h-likelihood.
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Affiliation(s)
- Il Do Ha
- Department of Asset Management, Daegu Haany University, Gyeongsan, South Korea
| | | | - Jong-Hyeon Jeong
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, USA
| | - Junwoo Park
- Department of Statistics, Seoul National University, Seoul, South Korea
| | - Youngjo Lee
- Department of Statistics, Seoul National University, Seoul, South Korea
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10
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Lee M, Ha ID, Lee Y. Frailty modeling for clustered competing risks data with missing cause of failure. Stat Methods Med Res 2016; 26:356-373. [PMID: 25125452 DOI: 10.1177/0962280214545639] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Competing risks data often occur within a center in multi-center clinical trials where the event times within a center may be correlated due to unobserved factors across individuals. In this paper, we consider the cause-specific proportional hazards model with a shared frailty to model the association between the event times within a center in the framework of competing risks. We use a hierarchical likelihood approach, which does not require any intractable integration over the frailty terms. In a clinical trial, cause of death information may not be observed for some patients. In such a case, analyses through exclusion of cases with missing cause of death may lead to biased inferences. We propose a hierarchical likelihood approach for fitting the cause-specific proportional hazards model with a shared frailty in the presence of missing cause of failure. We use multiple imputation methods to address missing cause of death information under the assumption of missing at random. Simulation studies show that the proposed procedures perform well, even if the imputation model is misspecified. The proposed methods are illustrated with data from EORTC trial 30791 conducted by European Organization for Research and Treatment of Cancer (EORTC).
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Affiliation(s)
- Minjung Lee
- 1 Department of Computer Science and Statistics, Chosun University, Gwangju, South Korea
| | - Il Do Ha
- 2 Department of Statistics, Pukyong National University, Busan, South Korea
| | - Youngjo Lee
- 3 Department of Statistics, Seoul National University, Seoul, South Korea
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11
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Liebl AL, Martin LB. Living on the edge: range edge birds consume novel foods sooner than established ones. Behav Ecol 2014. [DOI: 10.1093/beheco/aru089] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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12
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Biard L, Porcher R, Resche-Rigon M. Permutation tests for centre effect on survival endpoints with application in an acute myeloid leukaemia multicentre study. Stat Med 2014; 33:3047-57. [PMID: 24676752 DOI: 10.1002/sim.6153] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 02/21/2014] [Accepted: 03/02/2014] [Indexed: 11/10/2022]
Abstract
When analysing multicentre data, it may be of interest to test whether the distribution of the endpoint varies among centres. In a mixed-effect model, testing for such a centre effect consists in testing to zero a random centre effect variance component. It has been shown that the usual asymptotic χ(2) distribution of the likelihood ratio and score statistics under the null does not necessarily hold. In the case of censored data, mixed-effects Cox models have been used to account for random effects, but few works have concentrated on testing to zero the variance component of the random effects. We propose a permutation test, using random permutation of the cluster indices, to test for a centre effect in multilevel censored data. Results from a simulation study indicate that the permutation tests have correct type I error rates, contrary to standard likelihood ratio tests, and are more powerful. The proposed tests are illustrated using data of a multicentre clinical trial of induction therapy in acute myeloid leukaemia patients.
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Affiliation(s)
- L Biard
- Service de Biostatistique et Information Médicale, Hôpital Saint-Louis, AP-HP, F-75010 Paris, France; Université Paris Diderot - Paris 7, Sorbonne Paris Cité, F-75010 Paris, France; INSERM, ECSTRA Team, UMR-S 1153, F-75010 Paris, France
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13
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Lee Y, Noh M. Modelling random effect variance with double hierarchical generalized linear models. STAT MODEL 2012. [DOI: 10.1177/1471082x12460132] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Random-effect models are becoming increasingly popular in the analysis of data. Lee and Nelder (2006) introduced double hierarchical generalized linear models (DHGLMs) in which not only the mean but also the residual variance (overdispersion) can be further modelled as random-effect models. In this article, we introduce DHGLMs that allow random-effect models for both the variances of random effects and the residual variance. We show how to use this general model class for the analysis of data and discuss how to select the best fitting model using the likelihood and various model-checking plots.
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Affiliation(s)
- Youngjo Lee
- Department of Statistics, Seoul National University, Seoul 151–742, South Korea
| | - Maengseok Noh
- Department of Statistics, Pukyong National University, Busan 608–737, South Korea
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14
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Zhao Y, Lee AH, Yau KK, McLachlan GJ. Assessing the adequacy of Weibull survival models: a simulated envelope approach. J Appl Stat 2011. [DOI: 10.1080/02664763.2010.545115] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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15
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Ha ID, Sylvester R, Legrand C, Mackenzie G. Frailty modelling for survival data from multi-centre clinical trials. Stat Med 2011; 30:2144-59. [PMID: 21563206 DOI: 10.1002/sim.4250] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2010] [Accepted: 02/28/2011] [Indexed: 11/05/2022]
Abstract
Despite the use of standardized protocols in, multi-centre, randomized clinical trials, outcome may vary between centres. Such heterogeneity may alter the interpretation and reporting of the treatment effect. Below, we propose a general frailty modelling approach for investigating, inter alia, putative treatment-by-centre interactions in time-to-event data in multi-centre clinical trials. A correlated random effects model is used to model the baseline risk and the treatment effect across centres. It may be based on shared, individual or correlated random effects. For inference we develop the hierarchical-likelihood (or h-likelihood) approach which facilitates computation of prediction intervals for the random effects with proper precision. We illustrate our methods using disease-free time-to-event data on bladder cancer patients participating in an European Organization for Research and Treatment of Cancer trial, and a simulation study. We also demonstrate model selection using h-likelihood criteria.
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Affiliation(s)
- Il Do Ha
- Department of Asset Management, Daegu Haany University, Gyeongsan 712-715, South Korea.
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16
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Wang N, Xu S, Fang J. Hierarchical likelihood approach for the Weibull frailty model. J STAT COMPUT SIM 2011. [DOI: 10.1080/00949650903348148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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17
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Lim JW, Ashing-Giwa KT. Examining the effect of minority status and neighborhood characteristics on cervical cancer survival outcomes. Gynecol Oncol 2010; 121:87-93. [PMID: 21183210 DOI: 10.1016/j.ygyno.2010.11.041] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2010] [Revised: 11/22/2010] [Accepted: 11/28/2010] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Understanding the factors that contribute to mortality and survival is central to health outcome research. The purpose of this study was to investigate the following: (1) differences in survival status by ethnicity and neighborhood median income level; and (2) individual- and neighborhood-level factors influencing cervical cancer survival. METHODS This study was based on data from 1811 cervical cancer cases obtained through the California Cancer Surveillance Program. The dependent variable was days of survival from date of cancer diagnosis. Zip code-based neighborhood-level variables were obtained from Census 2000 data. RESULTS Ethnicity was significantly associated with survival (χ²=20.58; p<0.001), with African-Americans showing the shortest survival. The 5-year survival rates of European-, African-, Latino-, and Asian-American patients for all stages combined were 85%, 75%, 85%, and 84%, respectively. Differences in survival between high- and low-income regions were not observed. However, when ethnicity was considered, Asian-Americans who lived in high-income regions showed longer survival than their low-income community counterparts (χ²=4.531; p<0.05). The multilevel model demonstrated ethnicity, age at diagnosis, and cancer stage stratified by surgery to be significantly associated with cervical cancer survival at the individual level. At the neighborhood level, residing in neighborhoods with a high proportion of African-Americans increased the risk of death by 1%. CONCLUSIONS The neighborhood context may be an influential contributor to survival for Asian- and African-Americans specifically. These findings necessitate closer examination of the unique contribution of ethnicity and neighborhood on cancer survival to disentangle the role of ethnic group membership, socio-ecological contexts and stress, and medical factors on disease outcomes.
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Affiliation(s)
- Jung-won Lim
- Mandel School of Applied Social Sciences, Case Western Reserve University, Cleveland, OH, USA
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18
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Lai X, Yau KK. Extending the long-term survivor mixture model with random effects for clustered survival data. Comput Stat Data Anal 2010. [DOI: 10.1016/j.csda.2010.03.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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20
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Ha ID. A General Mixed Linear Model with Left-Censored Data. COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS 2008. [DOI: 10.5351/ckss.2008.15.6.969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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21
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Wang K, Yau KKW, Lee AH, McLachlan GJ. Multilevel survival modelling of recurrent urinary tract infections. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2007; 87:225-9. [PMID: 17619063 DOI: 10.1016/j.cmpb.2007.05.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2006] [Revised: 01/16/2007] [Accepted: 05/31/2007] [Indexed: 05/16/2023]
Abstract
A multilevel survival frailty model is presented for analyzing clustered and recurrent urinary tract infections among elderly women residing in aged-care institutions. At the subject level, serial dependence is expected between recurrent events recorded on the same individual. At the cluster level, correlations of observations within the same institution are present due to the inherent residential environment and hierarchical setting. Two random components are therefore incorporated explicitly within the survival frailty model to account for the simultaneous heterogeneity and autoregressive structure. A Splus computer program is developed for the estimation of fixed effect and variance component parameters.
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Affiliation(s)
- Kui Wang
- School of Public Health, Curtin University of Technology, GPO Box U 1987, Perth, WA 6845, Australia
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22
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Ha ID, Lee Y, Pawitan Y. Genetic mixed linear models for twin survival data. Behav Genet 2007; 37:621-30. [PMID: 17401640 DOI: 10.1007/s10519-007-9150-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2006] [Accepted: 03/01/2007] [Indexed: 10/23/2022]
Abstract
Twin studies are useful for assessing the relative importance of genetic or heritable component from the environmental component. In this paper we develop a methodology to study the heritability of age-at-onset or lifespan traits, with application to analysis of twin survival data. Due to limited period of observation, the data can be left truncated and right censored (LTRC). Under the LTRC setting we propose a genetic mixed linear model, which allows general fixed predictors and random components to capture genetic and environmental effects. Inferences are based upon the hierarchical-likelihood (h-likelihood), which provides a statistically efficient and unified framework for various mixed-effect models. We also propose a simple and fast computation method for dealing with large data sets. The method is illustrated by the survival data from the Swedish Twin Registry. Finally, a simulation study is carried out to evaluate its performance.
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Affiliation(s)
- Il Do Ha
- Department of Asset Management, Daegu Haany University, Gyeongsan 712-715, Korea.
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Abstract
In medical research recurrent event times can be analysed using a frailty model in which the frailties for different individuals are independent and identically distributed. However, such a homogeneous assumption about frailties could sometimes be suspect. For modelling heterogeneity in frailties we describe dispersion frailty models arising from a new class of models, namely hierarchical generalized linear models. Using the kidney infection data we illustrate how to detect and model heterogeneity among frailties. Stratification of frailty models is also investigated.
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Affiliation(s)
- Maengseok Noh
- Department of Statistics, Seoul National University, Seoul 151-747, Korea
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24
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Ha ID, Noh M, Yoon S. Genetic Mixed Effects Models for Twin Survival Data. COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS 2005. [DOI: 10.5351/ckss.2005.12.3.759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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