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Dissertations / Theses on the topic 'Joint modeling of longitudinal and survival data'

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Published: 4 June 2021

Last updated: 7 February 2022

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1

Pericleous, Paraskevi. "Parametric joint modelling for longitudinal and survival data." Thesis, University of East Anglia, 2016. https://ueaeprints.uea.ac.uk/59673/.

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Joint modelling is the simultaneous modelling of longitudinal and survival data, while taking into account a possible association between them. A common approach in joint modelling studies is to assume that the repeated measurements follow a lin- ear mixed e�ects model and the survival data is modelled using a Cox proportional hazards model. The Cox model, however, requires a strong proportionality assump- tion, which seems to be violated quite often. We, thus, propose the use of parametric survival models. Additionally, joint modelling literature mainly deals with right- censoring only and do

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2

Rajeev, Deepthi. "Separate and Joint Analysis of Longitudinal and Survival Data." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1775.pdf.

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3

Li, Qiuju. "Statistical inference for joint modelling of longitudinal and survival data." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/statistical-inference-for-joint-modelling-of-longitudinal-and-survival-data(65e644f3-d26f-47c0-bbe1-a51d01ddc1b9).html.

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In longitudinal studies, data collected within a subject or cluster are somewhat correlated by their very nature and special cares are needed to account for such correlation in the analysis of data. Under the framework of longitudinal studies, three topics are being discussed in this thesis. In chapter 2, the joint modelling of multivariate longitudinal process consisting of different types of outcomes are discussed. In the large cohort study of UK north Stafforshire osteoarthritis project, longitudinal trivariate outcomes of continuous, binary and ordinary data are observed at baseline, year

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4

Qiu, Feiyou. "JOINT MODELING OF LONGITUDINAL DATA AND DISCRETE-TIME SURVIVAL OUTCOME WITH APPLICATION TO STUDYING TUBERCULOSIS IMMUNOLOGY DATA." Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1322846245.

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5

VanderWyden, Piccorelli Annalisa. "Joint Modeling the Relationship between Longitudinal and Survival Data Subject to Left Truncation with Applications to Cystic Fibrosis." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1283437365.

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6

Lourens, Spencer. "Bias in mixtures of normal distributions and joint modeling of longitudinal and time-to-event data with monotonic change curves." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1685.

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Estimating parameters in a mixture of normal distributions dates back to the 19th century when Pearson originally considered data of crabs from the Bay of Naples. Since then, many real world applications of mixtures have led to various proposed methods for studying similar problems. Among them, maximum likelihood estimation (MLE) and the continuous empirical characteristic function (CECF) methods have drawn the most attention. However, the performance of these competing estimation methods has not been thoroughly studied in the literature and conclusions have not been consistent in published re

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7

McCrink, L. M. "Outlier effects on robust joint modelling of longitudinal and survival date." Thesis, Queen's University Belfast, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.679256.

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Robust joint modelling is an emerging field of research. Through the advancements in electronic patient healthcare records, the popularly of joint modelling approaches has grown rapidly in recent years providing simultaneous analysis of longitudinal and survival data. This research advances previous work through the development of a novel robust joint modelling methodology for one of the most common types of standard joint models, that which links a linear mixed model with a Cox proportional hazards model. Through t-distributional assumptions, longitudinal outliers are accommodated with their

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8

Rajeswaran, Jeevanantham. "JOINT MODELING OF MULTIVARIATE LONGITUDINAL DATA AND COMPETING RISKS DATA." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1354508776.

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9

Wang, Xu. "Joint inference for longitudinal and survival data with incomplete time-dependent covariates." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27842.

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In many longitudinal studies, individual characteristics associated with their repeated measures may be covariates for the time to an event of interest. Thus, it is desirable to model both the survival process and the longitudinal process together. Statistical analysis may be complicated with missing data or measurement errors in the time-dependent covariates. This thesis considers a nonlinear mixed-effects model for the longitudinal process and the Cox proportional hazards model for the survival process. We provide a method based on the joint likelihood for nonignorable missing data, and we e

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10

Crowther, Michael James. "Development and application of methodology for the parametric analysis of complex survival and joint longitudinal-survival data in biomedical research." Thesis, University of Leicester, 2015. http://hdl.handle.net/2381/31597.

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The occurrence of survival, or time-to-event, data is commonplace in medical research, where interest lies in the time it takes from a given baseline, for an event of interest to occur, and the factors that are associated with it. For example, this could be the effect of a treatment on the time to death since diagnosis of cardiovascular disease. The primary aim of this thesis is to develop parametric methods for the analysis of complex survival data, including the extension to joint models of longitudinal and survival data, to provide a number of advantages over the commonly used semi-parametr

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11

Huang, Xin. "A general joint model for longitudinal measurements and competing risks survival data with heterogenous random effects." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1667801741&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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12

Zhang, Hanze. "Bayesian inference on quantile regression-based mixed-effects joint models for longitudinal-survival data from AIDS studies." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7456.

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In HIV/AIDS studies, viral load (the number of copies of HIV-1 RNA) and CD4 cell counts are important biomarkers of the severity of viral infection, disease progression, and treatment evaluation. Recently, joint models, which have the capability on the bias reduction and estimates' efficiency improvement, have been developed to assess the longitudinal process, survival process, and the relationship between them simultaneously. However, the majority of the joint models are based on mean regression, which concentrates only on the mean effect of outcome variable conditional on

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13

Matta, Tyler. "A Joint Modeling Approach to Studying English Language Proficiency Development and Time-to-Reclassification." Thesis, University of Oregon, 2017. http://hdl.handle.net/1794/22265.

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The development of academic English proficiency and the time it takes to reclassify to fluent English proficient status are key issues in monitoring achievement of English learners. Yet, little is known about academic English language development at the domain-level (listening, speaking, reading, and writing), or how English language development is associated with time-to-reclassification as an English proficient student. Although the substantive findings surrounding English proficiency and reclassification are of great import, the main focus of this dissertation was methodological: the explo

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14

Fournier, Marie-Cecile. "Pronostic dynamique de l'évolution de l'état de santé de patients atteints d'une maladie chronique." Thesis, Nantes, 2016. http://www.theses.fr/2016NANT1004/document.

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Pour de nombreuses pathologies chroniques,l’amélioration de la prise en charge des patients passe par une meilleure compréhension de la progression de la pathologie et par la capacité à pronostiquer précocement la survenue d’événements délétères.L’évolution de l’état de santé des patients peut être appréciée à travers des mesures répétées d’un marqueur longitudinal, comme la créatinine sérique en transplantation rénale.Ce travail de thèse en Epidémiologie et Biostatistique appliqué à la transplantation rénale s’intéresse aux modèles conjoints pour données longitudinales et de temps d’évènement

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15

Sun, Yi-ting, and 孫怡婷. "Joint modeling of longitudinal and survival data─A case study in AIDS data." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/50434070715725917871.

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碩士<br>國立中央大學<br>統計研究所<br>99<br>The CD4 lymphocytes is an important indicator of diagnosis of AIDS, this paper mainly studied of AIDS, and investigates the association between the percentage of CD4 and the survival time, and explores the efficacy of HAART to AIDS patients. The patients repeated measurements the percentile of CD4 at different times as an interesting longitudinal data. The traditional approach have been employs the partial likelihood method to analyze the above data, but the partial likelihood method has to recognize the covariate history for each subject, and satisfy the require

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Liao, Che-wei, and 廖哲瑋. "Joint modeling of longitudinal and survival data - A case study in AIDS data." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/69498005917720887145.

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碩士<br>國立中央大學<br>統計研究所<br>100<br>We used the CD4 cell counts appraise AIDS progression, and explored the efficacy of DDI and DDC to AIDS patients. In survival analysis, the Cox model with partial likelihood is the most popular model to describe the relationship between longitudinal covariates and the survival time. However, when using partial likelihood, we have to recognize the complete covariate history for each patient, and the measurement error can not exist. In clinical trials, such situations can not hold due to the individual differences, measurement error of medical machines. Consequent

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Tapsoba, Jean-de-Dieu, and 江達生. "Semiparametric Methods for Joint Modeling of Survival and Longitudinal Data Measured with Error." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/32544570769963794496.

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博士<br>逢甲大學<br>應用統計研究所<br>99<br>In many longitudinal studies, an important objective is the investigation of the association between some error-prone longitudinal covariate and censored survival time processes. Standard statistical methods that ignore measurement error may lead to unreliable conclusion regarding the relationship between the two processes. In this thesis, we propose new methods that address the problem of measurement error in the analysis of survival time and longitudinal data. The methods are built under the joint modeling framework assuming a random effects model for the long

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18

Hsu, Chen-feng, and 許珍鳳. "Joint modeling of longitudinal and survival data–A caes study in Primary Biliary Cirrhosis data." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/42621981453752734228.

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碩士<br>國立中央大學<br>統計研究所<br>98<br>In survival analysis, it’s very common that the interesting covariates were measured intermittently at different measurment times for different patients. In this scenario, the repeated measurments could include measurment errors and measurments can not be observed after the survival time. Those situations could result in biased inferences for study when using Cox partial likelihood. To corret the bias, we use a joint model approach which models survival time and the longitudinal covariates simultaneously. This approach was applied to analyze Primary Biliary Cirrh

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19

Borges, Ana Isabel Coelho. "Joint modelling of longitudinal and survival data on breast cancer." Doctoral thesis, 2015. http://hdl.handle.net/1822/40426.

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Tese de Doutoramento em Ciências (Especialidade em Matemática)<br>The main motivation of this work is to contribute to the understanding of the progression of breast cancer, within the Portuguese population, using a statistical model with more complex assumptions than the traditional analysis. We aim to infer which risk factors a ect the survival of Braga's Hospital patients, diagnosed with breast tumour. While analysing risk factors that a ect two tumour markers used on the surveillance of disease progression the Carcinoma Antigen 15-3 (CA 15-3) and the Carcinoembryonic antigen (CEA).

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20

Chen, Mei-fang, and 陳美芳. "Joint modeling of longitudinal and survival data---A case study in patients with resected melanoma." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/26490926343938064427.

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碩士<br>國立中央大學<br>統計研究所<br>100<br>In this study, we want to investigate the relationship between recurrent time of patients with resected melanoma and their immunoglobulin M serologic. In addition, we are interested in the influence of different types of vaccines. Since the data includes both information of survival and longitudinal processes, joint model approach is applied to analyze the data. The longitudinal data is described by a linear random effects model, and the survival time is fitted by the Cox model. To derive the estimates of all parameters, Monte Carlo EM algorithm is used by takin

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21

Yen, Hsiu-Chen, and 顏秀禎. "Joint modeling of longitudinal and survival data─ A case study in patients with resected melanoma." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/26912082575775272954.

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碩士<br>國立中央大學<br>統計研究所<br>99<br>We utilize immunoglobulin G serologic responses to the vaccine to appraise the progression of patients with resected melanoma, and determine whether there are any adverse response to GMK and evaluate therapeutic efficacy of the combined-modality therapy. The joint model approach has been used to analyze the data, which includes both longitudinal and survival data. It makes estimators contains nice properties, such as consistency, efficiency and asymptotic normality. In the first part, we fit the longit- udinal data with the linear random effects model, and use th

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22

Yeh, Chiu-mei, and 葉秋梅. "Joint modeling of longitudinal and survival data－A case study in the liver cirrhosis patients." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/98031664357991826959.

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碩士<br>國立中央大學<br>統計研究所<br>98<br>Typically, the Cox model is the most popular model to describe the relationship between longitudinal covariates(time-dependent) and the survival time. However, to model the survival time with longitudinal covariates may encounter difficulties when the longitudinal measurement are scattered sparsely and contain measurement errors. There is an additional complexity when the longitudinal process can not be observed due to the event time(death or endpoint), which results in informatively missing data. Therefore, in this study, we applied the joint model to overcome t

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23

Ngwa, Julius S. "Comparing methods for modeling longitudinal and survival data, with consideration of mediation analysis." Thesis, 2013. https://hdl.handle.net/2144/15210.

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Joint modeling of longitudinal and survival data has received much attention and is becoming increasingly useful. In clinical studies, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for longitudinal data and a survival model is applied to event outcomes. The argument in favor of a joint model has been the efficient use of the data as the

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Sabelnykova, Veronica. "Bayesian methods for joint modelling of survival and longitudinal data: applications and computing." Thesis, 2012. http://hdl.handle.net/1828/4378.

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Multi-state models are useful for modelling progression of a disease, where states represent the health status of a subject under study. In practice, patients may be observed when necessity strikes thus implying that the disease and observation processes are not independent. Often, clinical visits are postponed or advanced by the clinician or patient themselves based on the health status of the patient. In such situations, there is a potential for the frequency and timing of the examinations to be dependent on the latent transition times, and the observation process may be informative. We cons

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25

Song, Hui. "Joint Modelling of Longitudinal Quality of Life Measurements and Survival Data in Cancer Clinical Trials." Thesis, 2013. http://hdl.handle.net/1974/7759.

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In cancer clinical trials, longitudinal Quality of Life (QoL) measurements on a patient may be analyzed by classical linear mixed models but some patients may drop out of study due to recurrence or death, which causes problems in the application of classical methods. Joint modelling of longitudinal QoL measurements and survival times may be employed to explain the dropout information of longitudinal QoL measurements, and provide more efficient estimation, especially when there is strong association between longitudinal measurements and survival times. Most joint models in the literature assu

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26

Yang, Lili. "Joint models for longitudinal and survival data." Thesis, 2014. http://hdl.handle.net/1805/4666.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Epidemiologic and clinical studies routinely collect longitudinal measures of multiple outcomes. These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. In the first part of this thesis, we proposed to use bivariate change point models for two longitudinal outcomes with a focus on estimating the correlation between the two change points. We adopted a Bayesian approach for parameter estimation and inference. In the second par

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Zheng, Mengjie. "Joint modeling of longitudinal and survival outcomes using generalized estimating equations." Diss., 2018. https://doi.org/10.7912/C2KS92.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Joint models for longitudinal and time-to-event data has been introduced to study the association between repeatedly measured exposures and the risk of an event. The use of joint models allows a survival outcome to depend on some characteristic functions from the longitudinal measures. Current estimation methods include a two-stage approach, Bayesian and maximum likelihood estimation (MLEs) methods. The twostage method is computationally straightforward but often yields biased estimates. Bayesian and MLE methods rely on the joi

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28

Shih, Yi-shan, and 施伊珊. "Joint Model for Survival and Longitudinal Data － A Case Study on Taiwan AIDS Data." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/99985856413118889895.

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碩士<br>國立中央大學<br>統計研究所<br>95<br>Over the past decade, much research have been done on Highly Active Antiretroviral Therapy (HAART), which has been proven to be able to postpone morbidity and lower mortality rate. This research collects data from 141 AIDS patients in Taiwan, which are separated into two groups, HAART and non-HAART. The research objective is to investigate the effect HAART and the CD4 cells to lifetime of AIDS patients. Research methods include graphic techniques and joint-modeling approach. Graphic techniques allow us to understand basic information from data, and the relationsh

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Yang, Ya-fang, and 楊雅芳. "Joint Model of Longitudinal and Survival Data-New Approach and Numerical Improvement." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/22528421560494346096.

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博士<br>國立中央大學<br>統計研究所<br>101<br>Joint likelihood approaches have been widely used to handle longitudinal and survival data at the same time because the estimation is consistent and semi-parametrically efficient. When accelerated failure time (AFT) model is employed as the survival component of the joint likelihood, the unspecified baseline hazard function is usually assumed to be a piecewise constant function. The non-smooth step function leads to very spiky likelihood function which is very hard to find the globe maximum. Moreover, due to non-smoothness of the likelihood function, direct sear

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Hsu, Yong-Dong, and 徐永東. "Joint Modeling of Additive-Multiplicative Hazard Model and Longitudinal Data." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/77708539010049156328.

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碩士<br>國立中央大學<br>統計研究所<br>104<br>In many survival studies, we know that the Cox PH model is a popular model, which is widely discussed and applied in lots of situations. However, there is a problem if we use the Cox model with more covariates, It assumes that all covariates in the model should satisfy the proportional hazard assumption. Once one covariate fails to the assumption, the model can not be used. So in this paper, we suggest a way to solve this problem. We suggest using the Cox-Aalen model, and we put those covariates, failing to that assumption, into the additive part in the Cox-Aale

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He, Zangdong. "Variable selection and structural discovery in joint models of longitudinal and survival data." Thesis, 2014. http://hdl.handle.net/1805/6365.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Joint models of longitudinal and survival outcomes have been used with increasing frequency in clinical investigations. Correct specification of fixed and random effects, as well as their functional forms is essential for practical data analysis. However, no existing methods have been developed to meet this need in a joint model setting. In this dissertation, I describe a penalized likelihood-based method with adaptive least absolute shrinkage and selection operator (ALASSO) penalty functions for model selection. By reparameterizing

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Wang, Jiun-Rong, and 王畯嶸. "Joint modeling of parametric additive-multiplicative hazards model and longitudinal data." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/b8m3rp.

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碩士<br>國立中央大學<br>統計研究所<br>105<br>Joint modeling approaches offer a solution to analyzing both survival and longitudinal processes simultaneously. The existing approaches focus mostly on developing adaptive and flexible longitudinal processes based on a preselected survival model, most commonly the Cox proportional hazards model. When the proportional hazards assumption fails for some of time dependent covariates of interest, an alternative model robust to proportionality assumption may needed to replace the Cox model. By combining the Cox model and Aalen additive hazards model, we propose a joi

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Liao, Ran. "Joint modeling of bivariate time to event data with semi-competing risk." Diss., 2016. http://hdl.handle.net/1805/12076.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>Survival analysis often encounters the situations of correlated multiple events including the same type of event observed from siblings or multiple events experienced by the same individual. In this dissertation, we focus on the joint modeling of bivariate time to event data with the estimation of the association parameters and also in the situation of a semi-competing risk. This dissertation contains three related topics on bivariate time to event mod els. The ﬁrst topic is on estimating the cross ratio which is an association

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TSAI, HAO-YUN, and 蔡昊澐. "Joint modeling of logistic and longitudinal data - Applications to stroke patients with orthostatic hypotension." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/01149371702119254786.

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碩士<br>國立臺北大學<br>統計學系<br>96<br>Orthostatic hypertension (OH) is one of the cardiovascular diseases. If a patient having stroke also has OH, then it is possible to have a higher chance to fall or syncope during the recovery. This may cause this patient to have possible fracture and the burden of medical cost therefore increases. How to diagnosis OH is clinically important. However, there is no obvious clinical method. This thesis uses a clinical data to identify potential clinical factors that are associated with OH. Since this data include repeatedly observed systolic pressures, patient's basic

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Lin, Wen-Chi, and 林汶琪. "Bayesian joint modeling of the mean and scale covariancestructures for longitudinal data using the multivariate tdistribution." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/90639247012642954616.

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碩士<br>國立中興大學<br>應用數學系所<br>96<br>In this thesis, we provide a fully Bayesian approach to multivariate t regression models with its mean vector and scale covariance matrix modeled jointly for analyzing incomplete longitudinal data. To facilitate the computation and simplify theoretic derivations, we introduce two binary auxiliary matrices for indexing the observed and missing components of the dependent variables. A computationally flexible Markov chain Monte Carlo (MCMC) algorithm utilizing the data augmentation (DA) technique is implemented for computing the entire posterior distributions of p

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Lin, Tzu-Yin, and 林姿吟. "Joint models for longitudinal binary and survival data - an application to survey of health and living status for elderly population in Taiwan." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/29395768270115245728.

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碩士<br>國立臺北大學<br>統計學系<br>99<br>The medical cost will increase substantially, if the elderly have higher incidence of chronic diseases, disability and unable to live independently, especially in an aging society. Healthy lifestyle not only affects elderly individuals but also affects the whole community. When assessing the healthy lifestyle, survival and quality of life should be considered simultaneously. Thus, how to simultaneously identify the association between the survival and long-term quality of life becomes an important issue. The two-stage model proposed by Tsiatis (1995) and the joint

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Kusiak, Caroline. "Real-Time Dengue Forecasting In Thailand: A Comparison Of Penalized Regression Approaches Using Internet Search Data." 2018. https://scholarworks.umass.edu/masters_theses_2/708.

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Dengue fever affects over 390 million people annually worldwide and is of particu- lar concern in Southeast Asia where it is one of the leading causes of hospitalization. Modeling trends in dengue occurrence can provide valuable information to Public Health officials, however many challenges arise depending on the data available. In Thailand, reporting of dengue cases is often delayed by more than 6 weeks, and a small fraction of cases may not be reported until over 11 months after they occurred. This study shows that incorporating data on Google Search trends can improve dis- ease predictions

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Han, Baoguang. "Statistical analysis of clinical trial data using Monte Carlo methods." Thesis, 2014. http://hdl.handle.net/1805/4650.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>In medical research, data analysis often requires complex statistical methods where no closed-form solutions are available. Under such circumstances, Monte Carlo (MC) methods have found many applications. In this dissertation, we proposed several novel statistical models where MC methods are utilized. For the first part, we focused on semicompeting risks data in which a non-terminal event was subject to dependent censoring by a terminal event. Based on an illness-death multistate survival model, we proposed flexible random effects mo

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