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Journal articles on the topic 'Joint modeling of longitudinal and survival data'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Crowther, Michael J., Keith R. Abrams, and Paul C. Lambert. "Joint Modeling of Longitudinal and Survival Data." Stata Journal: Promoting communications on statistics and Stata 13, no. 1 (2013): 165–84. http://dx.doi.org/10.1177/1536867x1301300112.

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2

Kim, Sehee, Donglin Zeng, Yi Li, and Donna Spiegelman. "Joint Modeling of Longitudinal and Cure-Survival Data." Journal of Statistical Theory and Practice 7, no. 2 (2013): 324–44. http://dx.doi.org/10.1080/15598608.2013.772036.

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3

Chen, Jia-Yuh, Richard Schulz, and Stewart J. Anderson. "Joint modeling of bivariate longitudinal and survival data in spouse pairs." Journal of Statistical Research 53, no. 1 (2019): 1–25. http://dx.doi.org/10.47302/jsr.2019530101.

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We investigated the association between longitudinally measured depression scores and survival times simultaneously for paired spouse data from the Cardiovascular Health Study (CHS). We propose a joint model incorporating within pair correlations, both in the longitudinal and survival processes. We use bivariate linear mixed-effects models for the longitudinal processes, where the random effects are used to model the temporal correlation within each subject and the correlation across outcomes between subjects. For the survival processes, we incorporate gamma frailties into Weibull proportional

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4

Muniz Terrera, Graciela, Andrea M. Piccinin, Fiona Matthews, and Scott M. Hofer. "Joint Modeling of Longitudinal Change and Survival." GeroPsych 24, no. 4 (2011): 177–85. http://dx.doi.org/10.1024/1662-9647/a000047.

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Joint longitudinal-survival models are useful when repeated measures and event time data are available and possibly associated. The application of this joint model in aging research is relatively rare, albeit particularly useful, when there is the potential for nonrandom dropout. In this article we illustrate the method and discuss some issues that may arise when fitting joint models of this type. Using prose recall scores from the Swedish OCTO-Twin Longitudinal Study of Aging, we fitted a joint longitudinal-survival model to investigate the association between risk of mortality and individual

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5

Wu, Lang, Wei Liu, Grace Y. Yi, and Yangxin Huang. "Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues." Journal of Probability and Statistics 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/640153.

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In the past two decades, joint models of longitudinal and survival data have received much attention in the literature. These models are often desirable in the following situations: (i) survival models with measurement errors or missing data in time-dependent covariates, (ii) longitudinal models with informative dropouts, and (iii) a survival process and a longitudinal process are associated via latent variables. In these cases, separate inferences based on the longitudinal model and the survival model may lead to biased or inefficient results. In this paper, we provide a brief overview of joi

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6

Qiu, Feiyou, Catherine M. Stein, and Robert C. Elston. "Joint modeling of longitudinal data and discrete-time survival outcome." Statistical Methods in Medical Research 25, no. 4 (2016): 1512–26. http://dx.doi.org/10.1177/0962280213490342.

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7

Hsieh, Fushing, Yi-Kuan Tseng, and Jane-Ling Wang. "Joint Modeling of Survival and Longitudinal Data: Likelihood Approach Revisited." Biometrics 62, no. 4 (2006): 1037–43. http://dx.doi.org/10.1111/j.1541-0420.2006.00570.x.

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8

Martins, Rui, Giovani L. Silva, and Valeska Andreozzi. "Bayesian joint modeling of longitudinal and spatial survival AIDS data." Statistics in Medicine 35, no. 19 (2016): 3368–84. http://dx.doi.org/10.1002/sim.6937.

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9

Hwang, Yi-Ting, Chia-Hui Huang, Chun-Chao Wang, Tzu-Yin Lin, and Yi-Kuan Tseng. "Joint modelling of longitudinal binary data and survival data." Journal of Applied Statistics 46, no. 13 (2019): 2357–71. http://dx.doi.org/10.1080/02664763.2019.1590540.

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10

Liu, Lei. "Joint modeling longitudinal semi-continuous data and survival, with application to longitudinal medical cost data." Statistics in Medicine 28, no. 6 (2008): 972–86. http://dx.doi.org/10.1002/sim.3497.

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11

Alafchi, Behnaz, Hossein Mahjub, Leili Tapak, Ghodratollah Roshanaei, and Mohammad Ali Amirzargar. "Two-Stage Joint Model for Multivariate Longitudinal and Multistate Processes, with Application to Renal Transplantation Data." Journal of Probability and Statistics 2021 (April 9, 2021): 1–10. http://dx.doi.org/10.1155/2021/6641602.

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In longitudinal studies, clinicians usually collect longitudinal biomarkers’ measurements over time until an event such as recovery, disease relapse, or death occurs. Joint modeling approaches are increasingly used to study the association between one longitudinal and one survival outcome. However, in practice, a patient may experience multiple disease progression events successively. So instead of modeling of a single event, progression of the disease as a multistate process should be modeled. On the other hand, in such studies, multivariate longitudinal outcomes may be collected and their as

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12

Ratcliffe, Sarah J., Wensheng Guo, and Thomas R. Ten Have. "Joint Modeling of Longitudinal and Survival Data via a Common Frailty." Biometrics 60, no. 4 (2004): 892–99. http://dx.doi.org/10.1111/j.0006-341x.2004.00244.x.

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13

Mwanyekange, Josua, Samuel Musili Mwalili, and Oscar Ngesa. "Bayesian Joint Models for Longitudinal and Multi-state Survival Data." International Journal of Statistics and Probability 8, no. 2 (2019): 34. http://dx.doi.org/10.5539/ijsp.v8n2p34.

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Joint models for longitudinal and time to event data are frequently used in many observational studies such as clinical trials with the aim of investigating how biomarkers which are recorded repeatedly in time are associated with time to an event of interest. In most cases, these joint models only consider a univariate time to event process. However, many clinical trials of patients with cancer, involve multiple recurrences of a single event together with a single terminal event experienced by patients over time. Therefore, this article proposes joint modelling approachs for longitudinal and m

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14

Yu, Tingting, Lang Wu, and Peter B. Gilbert. "A joint model for mixed and truncated longitudinal data and survival data, with application to HIV vaccine studies." Biostatistics 19, no. 3 (2017): 374–90. http://dx.doi.org/10.1093/biostatistics/kxx047.

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SUMMARY In HIV vaccine studies, a major research objective is to identify immune response biomarkers measured longitudinally that may be associated with risk of HIV infection. This objective can be assessed via joint modeling of longitudinal and survival data. Joint models for HIV vaccine data are complicated by the following issues: (i) left truncations of some longitudinal data due to lower limits of quantification; (ii) mixed types of longitudinal variables; (iii) measurement errors and missing values in longitudinal measurements; (iv) computational challenges associated with likelihood inf

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15

Preedalikit, Kemmawadee, Ivy Liu, Yuichi Hirose, Nokuthaba Sibanda, and Daniel Fernández. "Joint Modeling of Survival and Longitudinal Ordered Data Using a Semiparametric Approach." Australian & New Zealand Journal of Statistics 58, no. 2 (2016): 153–72. http://dx.doi.org/10.1111/anzs.12153.

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16

Ediebah, Divine E., Francisca Galindo-Garre, Bernard M. J. Uitdehaag, et al. "Joint modeling of longitudinal health-related quality of life data and survival." Quality of Life Research 24, no. 4 (2014): 795–804. http://dx.doi.org/10.1007/s11136-014-0821-6.

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17

Crowther, Michael J., Keith R. Abrams, and Paul C. Lambert. "Flexible parametric joint modelling of longitudinal and survival data." Statistics in Medicine 31, no. 30 (2012): 4456–71. http://dx.doi.org/10.1002/sim.5644.

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18

Brombin, Chiara, Clelia Di Serio, and Paola MV Rancoita. "Joint modeling of HIV data in multicenter observational studies: A comparison among different approaches." Statistical Methods in Medical Research 25, no. 6 (2016): 2472–87. http://dx.doi.org/10.1177/0962280214526192.

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Disease process over time results from the combination of event history information and longitudinal process. Commonly, separate analyses of longitudinal and survival outcomes are performed. However, discharging the dependence between these components may cause misleading results. Separate analyses are difficult to interpret whenever one deals with observational retrospective multicenter cohort studies where the biomarkers are poorly monitored over time, while the survival component may be affected by several sources of bias, such as multiple endpoints, multiple time-scales, and informative ce

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19

Xiong, Juan, Wenqing He, and Grace Y. Yi. "Joint modeling of survival data and mismeasured longitudinal data using the proportional odds model." Statistics and Its Interface 7, no. 2 (2014): 241–50. http://dx.doi.org/10.4310/sii.2014.v7.n2.a9.

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20

Ibrahim, Joseph G., Haitao Chu, and Liddy M. Chen. "Basic Concepts and Methods for Joint Models of Longitudinal and Survival Data." Journal of Clinical Oncology 28, no. 16 (2010): 2796–801. http://dx.doi.org/10.1200/jco.2009.25.0654.

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Joint models for longitudinal and survival data are particularly relevant to many cancer clinical trials and observational studies in which longitudinal biomarkers (eg, circulating tumor cells, immune response to a vaccine, and quality-of-life measurements) may be highly associated with time to event, such as relapse-free survival or overall survival. In this article, we give an introductory overview on joint modeling and present a general discussion of a broad range of issues that arise in the design and analysis of clinical trials using joint models. To demonstrate our points throughout, we

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21

Farcomeni, Alessio, Bhuvanesh Pareek, and Pulak Ghosh. "Discussion on ‘Joint modeling of survival and longitudinal non-survival data’ by Gould et al." Statistics in Medicine 34, no. 14 (2015): 2198–99. http://dx.doi.org/10.1002/sim.6284.

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22

Price, Dionne L., and Yan Wang. "Commentary on ‘Joint modeling of survival and longitudinal non-survival data: current methods and issues’." Statistics in Medicine 34, no. 14 (2015): 2200–2201. http://dx.doi.org/10.1002/sim.6331.

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23

Lawrence Gould, A., Mark Ernest Boye, Michael J. Crowther, et al. "Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian joint modeling working group." Statistics in Medicine 34, no. 14 (2014): 2181–95. http://dx.doi.org/10.1002/sim.6141.

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24

Tseng, Chi-hong, and Mengling Liu. "Joint Modeling of Survival Data and Longitudinal Measurements Under Nested Case-Control Sampling." Statistics in Biopharmaceutical Research 1, no. 4 (2009): 415–23. http://dx.doi.org/10.1198/sbr.2009.0048.

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25

Baghfalaki, Taban. "Bayesian Sample Size Determination for Joint Modeling of Longitudinal Measurements and Survival Data." Journal of Statistical Research of Iran 15, no. 2 (2019): 213–36. http://dx.doi.org/10.29252/jsri.15.2.213.

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26

Chen, Liddy M., Joseph G. Ibrahim, and Haitao Chu. "Sample size and power determination in joint modeling of longitudinal and survival data." Statistics in Medicine 30, no. 18 (2011): 2295–309. http://dx.doi.org/10.1002/sim.4263.

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27

Ediebah, Divine Ewane, Francisca Galindo-Garre, Bernard M. J. Uitdehaag, et al. "Joint modeling of longitudinal health-related quality of life (HRQoL) data and survival." Journal of Clinical Oncology 32, no. 15_suppl (2014): 2034. http://dx.doi.org/10.1200/jco.2014.32.15_suppl.2034.

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28

He, Bo, and Sheng Luo. "Joint modeling of multivariate longitudinal measurements and survival data with applications to Parkinson’s disease." Statistical Methods in Medical Research 25, no. 4 (2016): 1346–58. http://dx.doi.org/10.1177/0962280213480877.

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29

Song, Xiao, and C. Y. Wang. "Semiparametric Approaches for Joint Modeling of Longitudinal and Survival Data with Time-Varying Coefficients." Biometrics 64, no. 2 (2008): 557–66. http://dx.doi.org/10.1111/j.1541-0420.2007.00890.x.

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30

Tang, An-Min, and Nian-Sheng Tang. "Semiparametric Bayesian inference on skew-normal joint modeling of multivariate longitudinal and survival data." Statistics in Medicine 34, no. 5 (2015): 824–43. http://dx.doi.org/10.1002/sim.6373.

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31

Rizopoulos, Dimitris. "Comments on ‘Joint modeling of survival and longitudinal non-survival data: current methods and issues. Report of the DIA Bayesian Joint Modeling Working Group’." Statistics in Medicine 34, no. 14 (2015): 2196–97. http://dx.doi.org/10.1002/sim.6260.

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32

Sattar, Abdus, and Sanjoy K. Sinha. "Joint modeling of longitudinal and survival data with a covariate subject to a limit of detection." Statistical Methods in Medical Research 28, no. 2 (2017): 486–502. http://dx.doi.org/10.1177/0962280217729573.

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We develop and study an innovative method for jointly modeling longitudinal response and time-to-event data with a covariate subject to a limit of detection. The joint model assumes a latent process based on random effects to describe the association between longitudinal and time-to-event data. We study the role of the association parameter on the regression parameters estimators. We model the longitudinal and survival outcomes using linear mixed-effects and Weibull frailty models, respectively. Because of the limit of detection, missing covariate (explanatory variable, x) values may lead to t

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33

Wang, Jue, and Sheng Luo. "Bayesian multivariate augmented Beta rectangular regression models for patient-reported outcomes and survival data." Statistical Methods in Medical Research 26, no. 4 (2015): 1684–99. http://dx.doi.org/10.1177/0962280215586010.

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Many longitudinal studies (e.g. observational studies and randomized clinical trials) have collected multiple rating scales at each visit in the form of patient-reported outcomes (PROs) in the close unit interval [0 ,1]. We propose a joint modeling framework to address the issues from the following data features: (1) multiple correlated PROs; (2) the presence of the boundary values of zeros and ones; (3) extreme outliers and heavy tails; (4) the PRO-dependent terminal events such as death and dropout. Our modeling framework consists of a multivariate augmented mixed-effects sub-model based on

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34

Dai, Hongsheng, and Jianxin Pan. "Joint Modelling of Survival and Longitudinal Data with Informative Observation Times." Scandinavian Journal of Statistics 45, no. 3 (2018): 571–89. http://dx.doi.org/10.1111/sjos.12314.

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35

Xu, Cong, Paul D. Baines, and Jane-Ling Wang. "Standard error estimation using the EM algorithm for the joint modeling of survival and longitudinal data." Biostatistics 15, no. 4 (2014): 731–44. http://dx.doi.org/10.1093/biostatistics/kxu015.

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Abstract Joint modeling of survival and longitudinal data has been studied extensively in the recent literature. The likelihood approach is one of the most popular estimation methods employed within the joint modeling framework. Typically, the parameters are estimated using maximum likelihood, with computation performed by the expectation maximization (EM) algorithm. However, one drawback of this approach is that standard error (SE) estimates are not automatically produced when using the EM algorithm. Many different procedures have been proposed to obtain the asymptotic covariance matrix for t

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36

Franco Soto, Diana Carolina, Antonio Carlos Pedroso de Lima, and Julio Da Motta Singer. "A Birnbaum-Saunders Model for Joint Survival and Longitudinal Analysis of Congestive Heart Failure Data." Revista Colombiana de Estadística 43, no. 1 (2020): 83–101. http://dx.doi.org/10.15446/rce.v43n1.77851.

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We consider a parametric joint modelling of longitudinal measurements and survival times, motivated by a study conducted at the Heart Institute (Incor), São Paulo, Brazil, with the objective of evaluating the impact of B-type Natriuretic Peptide (BNP) collected at different instants on the survival of patients with Congestive Heart Failure (CHF). We employ a linear mixed model for the longitudinal response and a Birnbaum-Saunders model for the survival times, allowing the inclusion of subjects without longitudinal observations. We derive maximum likelihood estimators of the joint model paramet

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37

Wang, Jue, and Sheng Luo. "Joint modeling of multiple repeated measures and survival data using multidimensional latent trait linear mixed model." Statistical Methods in Medical Research 28, no. 10-11 (2018): 3392–403. http://dx.doi.org/10.1177/0962280218802300.

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Impairment caused by Amyotrophic lateral sclerosis (ALS) is multidimensional (e.g. bulbar, fine motor, gross motor) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of ALS use multiple longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we develop a joint model consisting of a multidimensional latent trait

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38

Mwanyekange, Josua, Samuel Mwalili, and Oscar Ngesa. "Bayesian Inference in a Joint Model for Longitudinal and Time to Event Data with Gompertz Baseline Hazards." Modern Applied Science 12, no. 9 (2018): 159. http://dx.doi.org/10.5539/mas.v12n9p159.

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Longitudinal and time to event data are frequently encountered in many medical studies. Clinicians are more interested in how longitudinal outcomes influences the time to an event of i nterest. To study the association between longitudinal and time to event data, joint modeling approaches were found to be the most appropriate techniques for such data. The approaches involves the choice of the distribution of the survival times which in most cases authors prefer either exponential or Weibull distribution. However, these distributions have some shortcomings. In this paper, we propose an alternat

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39

Armero, Carmen, Anabel Forte, Hèctor Perpiñán, María José Sanahuja, and Silvia Agustí. "Bayesian joint modeling for assessing the progression of chronic kidney disease in children." Statistical Methods in Medical Research 27, no. 1 (2016): 298–311. http://dx.doi.org/10.1177/0962280216628560.

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Joint models are rich and flexible models for analyzing longitudinal data with nonignorable missing data mechanisms. This article proposes a Bayesian random-effects joint model to assess the evolution of a longitudinal process in terms of a linear mixed-effects model that accounts for heterogeneity between the subjects, serial correlation, and measurement error. Dropout is modeled in terms of a survival model with competing risks and left truncation. The model is applied to data coming from ReVaPIR, a project involving children with chronic kidney disease whose evolution is mainly assessed thr

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40

Li, Kan, and Sheng Luo. "Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer’s disease." Statistical Methods in Medical Research 28, no. 2 (2017): 327–42. http://dx.doi.org/10.1177/0962280217722177.

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In the study of Alzheimer’s disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients’ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model to account for functional predictors in both longitudinal and sur

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41

Tapsoba, Jean de Dieu, Shen-Ming Lee, and C. Y. Wang. "Joint modeling of survival time and longitudinal data with subject-specific changepoints in the covariates." Statistics in Medicine 30, no. 3 (2010): 232–49. http://dx.doi.org/10.1002/sim.4107.

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42

Fu, Rong, and Peter B. Gilbert. "Joint modeling of longitudinal and survival data with the Cox model and two-phase sampling." Lifetime Data Analysis 23, no. 1 (2016): 136–59. http://dx.doi.org/10.1007/s10985-016-9364-1.

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43

Chen, Qingxia, Ryan C. May, Joseph G. Ibrahim, Haitao Chu, and Stephen R. Cole. "Joint modeling of longitudinal and survival data with missing and left-censored time-varying covariates." Statistics in Medicine 33, no. 26 (2014): 4560–76. http://dx.doi.org/10.1002/sim.6242.

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44

Zhu, Huirong, Stacia M. DeSantis, and Sheng Luo. "Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective." Statistical Methods in Medical Research 27, no. 4 (2016): 1258–70. http://dx.doi.org/10.1177/0962280216659312.

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Longitudinal zero-inflated count data are encountered frequently in substance-use research when assessing the effects of covariates and risk factors on outcomes. Often, both the time to a terminal event such as death or dropout and repeated measure count responses are collected for each subject. In this setting, the longitudinal counts are censored by the terminal event, and the time to the terminal event may depend on the longitudinal outcomes. In the study described herein, we expand the class of joint models for longitudinal and survival data to accommodate zero-inflated counts and time-to-

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45

Ko, Feng-shou. "An issue of identifying longitudinal biomarkers for competing risks data with masked causes of failure considering frailties model." Statistical Methods in Medical Research 29, no. 2 (2019): 603–16. http://dx.doi.org/10.1177/0962280219842352.

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In this paper, we consider joint modeling of repeated measurements and competing risks failure time data to allow for more than one distinct failure type in the survival endpoint. Hence, we can fit a cause-specific hazards submodel to allow for competing risks, with a separate latent association between longitudinal measurements and each cause of failure. We also consider the possible masked causes of failure in joint modeling of repeated measurements and competing risks failure time data. We also derive a score test to identify longitudinal biomarkers or surrogates for a time-to-event outcome

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46

Gould, A. Lawrence, Mark Ernest Boye, Michael J. Crowther, et al. "Responses to discussants of ‘Joint modeling of survival and longitudinal non-survival data: current methods and issues. report of the DIA Bayesian joint modeling working group’." Statistics in Medicine 34, no. 14 (2015): 2202–3. http://dx.doi.org/10.1002/sim.6502.

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47

Ivanova, Anna, Geert Molenberghs, and Geert Verbeke. "Fast and highly efficient pseudo-likelihood methodology for large and complex ordinal data." Statistical Methods in Medical Research 26, no. 6 (2015): 2758–79. http://dx.doi.org/10.1177/0962280215608213.

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In longitudinal studies, continuous, binary, categorical, and survival outcomes are often jointly collected, possibly with some observations missing. However, when it comes to modeling responses, the ordinal ones have received less attention in the literature. In a longitudinal or hierarchical context, the univariate proportional odds mixed model (POMM) can be regarded as an instance of the generalized linear mixed model (GLMM). When the response of the joint multivariate model encompass ordinal responses, the complexity further increases. An additional problem of model fitting is the size of

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48

Al-Huniti, Nidal, Dmitry Onishchenko, James Dunyak, et al. "Dynamic predictions of patient survival using longitudinal tumor size in non-small cell lung cancer: Approach towards personalized medicine." Journal of Clinical Oncology 35, no. 15_suppl (2017): e20606-e20606. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e20606.

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e20606 Background: Tumor burden has long been used for the clinical diagnosis, staging, prognosis and treatment of non-small cell lung cancer (NSCLC), as described, for example, in the 7th edition of the AJCC/UICC NSCLC staging guidelines. Previous longitudinal tumor size approaches have used fixed tumor kinetic parameters or tumor shrinkage at a given timepoint, to correlate PFS and OS in a stepwise fashion. Here we describe a joint modeling approach which allows for individual, patient-level predictions of survival during NSCLC treatment. Joint modeling simultaneously fits OS and tumor size

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49

Baghfalaki, T., and M. Ganjali. "Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data." Statistical Methods in Medical Research 30, no. 6 (2021): 1484–501. http://dx.doi.org/10.1177/09622802211002868.

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Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partiall

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50

de Dieu Tapsoba, Jean, Shen-Ming Lee, and C. Y. Wang. "Approximate nonparametric corrected-score method for joint modeling of survival and longitudinal data measured with error." Biometrical Journal 53, no. 4 (2011): 557–77. http://dx.doi.org/10.1002/bimj.201000180.

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