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LIU, XIAOQIU. "Managing Cardiovascular Risk in Hypertension: Methodological Issues in Blood Pressure Data Analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/154475.
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Hypertension remains in 2017 a leading cause of mortality and disability worldwide. A number of issues related to the determinants of cardiovascular risk in hypertensive patients and to the strategies for better hypertension control are still pending. In such a context, aims of my research program were:
1. To investigate the contribution of blood pressure variability to the risk of cardiovascular mortality in hypertensive patients. In this setting, different methods for assessing blood pressure variability and different models exploring the link between blood pressure variability and outcome were investigated.
2. To assess the possibility that a hypertension management strategy based on hemodynamic assessment of patients through impedance cardiography might lead to a better hypertension control over 24 hours than a conventional approach only based on blood pressure measurement during clinic visits.
To these aims, this thesis summarizes data obtained by performing a). An in-depth analysis of a study conducted in the Dublin hypertensive population, including 11492 subjects, and b). The analysis of longitudinal data collected in the frame of BEAUTY (BEtter control of blood pressure in hypertensive pAtients monitored Using the hoTman® sYstem) study.
In Dublin study, the proportional hazard Cox model and accelerated failure time models have been used to estimate the additional effect of blood pressure variability on cardiovascular mortality over and above the effect of increased mean BP levels, with an attempt to identify the best threshold values for risk stratification.
On the other hand, in BEAUTY study, mixed model and generalized estimation equation are used for the longitudinal data analysis.
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Petzold, Max. "Evaluation of information in longitudinal data." Göteborg : Statistical Research Unit, Göteborg University, 2003. http://catalog.hathitrust.org/api/volumes/oclc/52551306.html.
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Yao, Fang. "Functional data analysis for longitudinal data /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2003. http://uclibs.org/PID/11984.
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Zhu, Liang. "Semiparametric analysis of multivariate longitudinal data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6044.
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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 3, 2009) Vita. Includes bibliographical references.
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Mills, Joanna E. "The analysis longitudinal binary data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ57350.pdf.
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Fullwood, Catherine Lee. "Longitudinal analysis of anticoagulent data." Thesis, Lancaster University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431469.
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Bai, Yang, and 柏楊. "Statistical analysis for longitudinal data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B42841756.
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Bai, Yang. "Statistical analysis for longitudinal data." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B42841756.
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Kim, Yangjin. "Statistical analysis of longitudinal data /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3100054.
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Modur, Sharada P. "Missing Data Methods for Clustered Longitudinal Data." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1274876785.
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Boudreau, Christian. "Duration data analysis in longitudinal surveys." Waterloo, Ont. : University of Waterloo, 2003. http://etd.uwaterloo.ca/etd/cboudrea2003.pdf.
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Thesis (Ph.D.)--University of Waterloo, 2003.<br>"A thesis presented to the University of Waterloo in fulfillment of the thesis requirement for the degree of Doctor of Philosophy in Statistics". Includes bibliographical references. Also available in microfiche format.
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Boudreau, Christian. "Duration Data Analysis in Longitudinal Survey." Thesis, University of Waterloo, 2003. http://hdl.handle.net/10012/1043.
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Considerable amounts of event history data are collected through longitudinal surveys. These surveys have many particularities or features that are the results of the dynamic nature of the population under study and of the fact that data collected through longitudinal surveys involve the use of complex survey designs, with clustering and stratification. These particularities include: attrition, seam-effect, censoring, left-truncation and complications in the variance estimation due to the use of complex survey designs. This thesis focuses on the last two points.
Statistical methods based on the stratified Cox proportional hazards model that account for intra-cluster dependence, when the sampling design is uninformative, are proposed. This is achieved using the theory of estimating equations in conjunction with empirical process theory. Issues concerning analytic inference from survey data and the use of weighted versus unweighted procedures are also discussed. The proposed methodology is applied to data from the U. S. Survey of Income and Program Participation (SIPP) and data from the Canadian Survey of Labour and Income Dynamics (SLID).
Finally, different statistical methods for handling left-truncated sojourns are explored and compared. These include the conditional partial likelihood and other methods, based on the Exponential or the Weibull distributions.
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Ricci, Peter J. "Some aspects of longitudinal data analysis /." Title page, contents and summary only, 1994. http://web4.library.adelaide.edu.au/theses/09PH/09phr491.pdf.
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Fear, Simon Charles. "The analysis of categorical longitudinal data." Thesis, University of Liverpool, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266052.
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Stern, Theresa Marie Papa. "Longitudinal analysis of incomplete binary data /." The Ohio State University, 1997. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487947501135652.
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Gao, Dexiang. "Analysis of clustered longitudinal count data /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2007.
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Thesis (Ph.D. in Analytic Health Sciences, Department of Preventive Medicine and Biometrics) -- University of Colorado Denver, 2007.<br>Typescript. Includes bibliographical references (leaves 75-77). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;
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Schabenberger, Oliver. "The analysis of longitudinal ordinal data." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-02272007-092413/.
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Cheung, Ka-yan, and 張嘉茵. "Multilevel modeling for the analysis of longitudinal periodontal data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46605496.
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Jiang, Xiaoping. "Nonparametric quasi-likelihood in longitudinal data analysis." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/2078.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.<br>Thesis research directed by: Mathematical Statistics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Li, Zuojing. "Longitudinal data analysis using generalized linear models." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27267.
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In this work we examine various conditions under which the usual asymptotic results (i.e. the weak consistency, the asymptotic normality and the strong consistency) hold for the regressor parameter beta which arises in a linear model (Chapter 2), a generalized linea model (GLM) with a fully specified likelihood (Chapter 3) or as a root of the generalized estimating equation (GEE) associated with a sequence of longitudinal observations (Chapter 4). Our main references for each of these chapters are [12], [9], respectively [20].
We provide detailed proofs of the results found in the above-mentioned references, and we extend the results of [9] to, the case of stochastic regressors (Section 3.4). Finally, in Chapter 5, we identify a fundamental mistake appearing in the recent article [4], which examines the strong consistency of the regressor parameter beta in a GLM for which the likelihood of the density is not specified. In Section 5.2, we give a correction to the main theorem of [4], as well as some new results concerning the weak consistency and asymptotic normality of beta.
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Green, Brittany. "Ultra-high Dimensional Semiparametric Longitudinal Data Analysis." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593171378846243.
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Zhao, Rui. "Integrated Analysis of Longitudinal Tumor Burden Data." Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:14226076.
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The first part of this thesis introduces a new statistical method to estimate parameter values in a mixed population consisting of both single- and bi- phasic longitudinal trajectories. This pro- posed model is capable of categorizing patients according to their longitudinal relationships and estimating the associated parameters of interest, while accounting for between-patient variability. We applied this method to a large phase III randomized trial and found significant differences in patients between different treatment cohorts and within the same treatment cohort, in terms of their longitudinal relationships, with the majority of patients displaying complex bi-phasic trends. In the second part of this thesis, we designed a dynamical system model to explain the observed bi-phasic longitudinal trends and their implications for the underlying cancer biology. We found that a hybrid model encompassing both hierarchical cellular model and clonal expansion model is needed to explain the observed bi-phasic patterns. The third part of this thesis explores the effects of proliferative patterns in colon crypt on crypt stability and rates of somatic evolution.
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Khatiwada, Aastha. "Multilevel Models for Longitudinal Data." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3090.
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Longitudinal data arise when individuals are measured several times during an ob- servation period and thus the data for each individual are not independent. There are several ways of analyzing longitudinal data when different treatments are com- pared. Multilevel models are used to analyze data that are clustered in some way. In this work, multilevel models are used to analyze longitudinal data from a case study. Results from other more commonly used methods are compared to multilevel models. Also, comparison in output between two software, SAS and R, is done. Finally a method consisting of fitting individual models for each individual and then doing ANOVA type analysis on the estimated parameters of the individual models is proposed and its power for different sample sizes and effect sizes is studied by simulation.
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Chang, Janis. "Analysis of ordered categorical data." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/27857.
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Methods of testing for a location shift between two populations in a longitudinal study are investigated when the data of interest are ordered, categorical and non-linear. A non-standard analysis involving modelling of data over time with transition probability matrices is discussed. Next, the relative efficiencies of statistics more frequently used for the analysis of such categorical data at a single time point are examined. The Wilcoxon rank sum, McCullagh, and 2 sample t statistic are compared for the analysis of such cross sectional data using simulation and efficacy calculations. Simulation techniques are then utilized in comparing the stratified Wilcoxon, McCullagh and chi squared-type statistic in their efficiencies at detecting a location shift when the data are examined over two time points. The distribution of a chi squared-type statistic based on the simple contingency table constructed by merely noting whether a subject improved, stayed the same or deteriorated is derived. Applications of these methods and results to a data set of Multiple Sclerosis patients, some of whom were treated with interferon and some of whom received a placebo are provided throughout the thesis and our findings are summarized in the last Chapter.<br>Science, Faculty of<br>Statistics, Department of<br>Graduate
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Fatima, Kaniz. "Analysis of longitudinal data with ordered categorical response." Thesis, University of Reading, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239058.
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Hu, Zonghui. "Semiparametric functional data analysis for longitudinal/clustered data: theory and application." Texas A&M University, 2004. http://hdl.handle.net/1969.1/3088.
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Semiparametric models play important roles in the field of biological statistics. In this dissertation, two types of semiparametic models are to be studied. One is the partially linear model, where the parametric part is a linear function. We are to investigate the two common estimation methods for the partially linear models when the data is correlated  longitudinal or clustered. The other is a semiparametric model where a latent covariate is incorporated in a mixed effects model. We will propose a semiparametric approach for estimation of this model and apply it to the study on colon carcinogenesis.
First, we study the profilekernel and backfitting methods in partially linear models for clustered/longitudinal data. For independent data, despite the potential rootn inconsistency of the backfitting estimator noted by Rice (1986), the two estimators have the same asymptotic variance matrix as shown by Opsomer and Ruppert (1999). In this work, theoretical comparisons of the two estimators for multivariate responses are investigated. We show that, for correlated data, backfitting often produces a larger asymptotic variance than the profilekernel method; that is, in addition to its bias problem, the backfitting estimator does not have the same asymptotic efficiency as the profilekernel estimator when data is correlated. Consequently, the common practice of using the backfitting method to compute profilekernel estimates is no longer advised. We illustrate this in detail by following Zeger and Diggle (1994), Lin and Carroll (2001) with a working independence covariance structure for nonparametric estimation and a correlated covariance structure for parametric estimation. Numerical performance of the two estimators is investigated through a simulation study. Their application to an ophthalmology dataset is also described.
Next, we study a mixed effects model where the main response and covariate variables are linked through the positions where they are measured. But for technical reasons, they are not measured at the same positions. We propose a semiparametric approach for this misaligned measurements problem and derive the asymptotic properties of the semiparametric estimators under reasonable conditions. An application of the semiparametric method to a colon carcinogenesis study is provided. We find that, as compared with the corn oil supplemented diet, fish oil supplemented diet tends to inhibit the increment of bcl2 (oncogene) gene expression in rats when the amount of DNA damage increases, and thus promotes apoptosis.
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Barry, Sarah Jane Elizabeth. "Longitudinal analysis of three-dimensional facial shape data." Connect to e-thesis, 2008. http://theses.gla.ac.uk/190/.
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Thesis (Ph.D.) - University of Glasgow, 2008.<br>Ph.D. thesis submitted to the Faculty of Information and Mathematical Sciences, Department of Statistics, University of Glasgow, 2008. Includes bibliographical references. Print version also available.
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Barry, Sarah J. E. "Longitudinal analysis of three-dimensional facial shape data." Thesis, University of Glasgow, 2008. http://theses.gla.ac.uk/190/.
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Shape data encompass all the information that is left to describe a shape following removal of location, rotation and scale effects. Much work has been done in the analysis of two-dimensional shapes depicted by anatomical landmarks placed at points of importance. Less has been carried out in the area of three-dimensional shapes, particularly in terms of growth or change over time. This thesis considers the analysis of such longitudinal three-dimensional shape data. In doing so, two well established but normally unrelated areas of Statistics are brought together: those of longitudinal data analysis (specifically, linear mixed effects models) and shape analysis. A recently proposed method of analysing longitudinal high-dimensional data is presented in a novel application within the area of shape analysis, illustrated by a study comparing the facial shapes of cleft-lip and palate children with controls as they grow from three months to two years of age. Both anatomical landmarks and facial curves are considered. Chapter 1 broadly introduces the areas of shape analysis, linear mixed effects models and dimension reduction. Standard methods for measuring shapes are introduced, along with the difficulties inherent in analysing the resulting data. A broad overview of the methods of aligning individual shapes to remove the unwanted effects of location, rotation and scale is given, along with related geometrical issues in terms of the high-dimensional space in which a set of shapes resides. A general introduction to linear mixed effects models compares and contrasts them with simple linear models, explaining the reasons behind using them and presenting the different specifications of the conditional and marginal models. The area of dimension reduction is touched upon, specifically introducing B-splines and principal components analysis, with reference to the analysis of curves consisting of many points at small increments to one another. The data from the cleft-lip and palate study are introduced, along with a discussion of the primary interest of the analysis and the issue of missing data. Chapter 2 presents the statistical definition of a shape and introduces the area of statistical shape analysis in detail, specifically presenting the technicalities of shape space and distances, and methods such as Procrustes alignment of a set of shapes to remove unwanted effects. The concept of tangent coordinates is introduced as a projection of shape data into a Euclidean space, to enable the use of multivariate methods, and an outline given of thin-plate splines and deformations for the analysis of surfaces. Recent literature in the area of shape analysis is presented. Further recent literature addressing the modelling of growth in shapes is presented in Chapter 3, which goes on to discuss the use of linear mixed models on univariate and multivariate longitudinal data. The difficulties of applying mixed models to multivariate data are discussed and a recently proposed alternative method introduced, which involves fitting mixed models to the responses on pairs of outcomes rather than the full set. A description of the R function written as part of this thesis to fit such pairwise models follows, and this is applied to simulated triangles and quadrilaterals as an illustration. The initial application of the pairwise method to the cleft-lip and palate landmark data is presented in Chapter 4. The landmarks are described and the models are fitted to the tangent coordinate responses with different covariance structures for the random effects. The problems that arise and the deficiencies of the fitted models are extensively discussed. Chapter 5 goes on to address the issues raised in Chapter 4. A method of aligning the individual shapes based upon a subset of landmarks is suggested, along with a model that assumes independence of coordinates between dimensions but correlation within, and the benefits of these approaches compared. A simulation study is carried out to investigate the reasons behind and effects of random effects correlations that are estimated as being close to one, concluding that the problem lies in small variances that are poorly estimated, but that this is unlikely to be of severe detriment to the fixed effects estimates. A method of taking the principal components of the tangent coordinates is suggested, where the model responses are the principal components scores, and this proves to be the most appropriate way of applying the pairwise models in terms of model fit and computational efficiency. In Chapter 6, recent literature on the topic of curve analysis is presented, along with the way the facial curves are measured and the need for dimension reduction. Two methods are presented to this end: B-splines and principal components analysis, with the former suffering similar problems to the landmark analyses in terms of poorly estimated random effects variances, and the latter proving more successful. The application of the pairwise models to the principal components scores of the tangent coordinates provides a detailed analysis of the cleft-lip and palate data. Issues surrounding model comparison are addressed in Chapter 7, with several hypothesis tests presented and applied to simulated data. Drawbacks with some of the tests when applied to high dimensional or longitudinal data result in poor performance, but a method suggested by Faraway (1997) and a modification of the likelihood ratio test, both using bootstrapping, show similarly successful results. These are subsequently used to test for any differences in the time trends for the cleft and control groups post-surgery and find that there are significant differences. Condensed forms of this thesis have been presented at invited seminars and international conferences, and may be found in published form in Barry & Bowman (2006), Barry & Bowman (2007) and Barry & Bowman (2008).
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Verzilli, Claudio John. "Methods for the analysis of incomplete longitudinal data." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2003. http://researchonline.lshtm.ac.uk/4646517/.
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Unplanned missing data commonly arise in longitudinal trials. When the mechanism driving the missing data process is related to the outcome under investigation, traditional methods of analysis may yield seriously biased parameter estimates. Motivated by data from two clinical trials, this thesis explores various approaches to dealing with data incompleteness. In the first part, a Monte Carlo EM algorithm is developed and used to fit so called random-co efficient-based dropout models; these models relate the probability of a patient's dropout in follow-up studies to some subject-specific characteristics such as their deviation from the average rate of progression of the disease over time. The approach is used to model incomplete data from a 5-year study of patients with Parkinson's disease. The validity of the results obtained using these methods however, depends in general on distributional and modelling assumptions about the missing data that are inherently untestable as no data were collected. For this reason, many have advocated the need for a sensitivity analysis aimed at assessing the robustness of the conclusions from an analysis that ignores the missing data mechanism. In the second part of the thesis we address these issues. In particular, we present results from sensitivity analyses based on local influence and sampling-based methods used in conjunction with the random-coefficient-based dropout model described in the first part. Recently, a more formal approach to sensitivity analysis for missing data problems has been proposed whereby traditional point estimates are replaced by intervals encoding our lack of knowledge due to incompleteness of the data. In the third part of the thesis, we extend these methods to longitudinal ordinal data. Also, for cross-sectional discrete data having distribution belonging to the exponential family, we propose using the proportion of possible estimates of a parameter of interest, over all solutions corresponding to all sample completions, as a measure of ignorance. We develop a computationally efficient algorithm to calculate this proportion and illustrate our methods using data from a dental pain trial.
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Jangerstad, August. "Transcription factor analysis of longitudinal mRNA expression data." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278693.
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Transcription factors (TFs) are key regulatory proteins that regulate transcriptionthrough precise, but highly variable binding events to cis-regulatory elements.The complexity of their regulatory patterns makes it difficult to determinethe roles of different TFs, a task which the field is still struggling with.Experimental procedures for this purpose, such as knock out experiments, arehowever costly and time consuming, and with the ever-increasing availabilityof sequencing data, computational methods for inferring the activity of TFsfrom such data have become of great interest. Current methods are howeverlacking in several regards, which necessitates further exploration of alternatives. A novel tool for estimating the activity of individual TFs over time fromlongitudinal mRNA expression data was in this project therefore put togetherand tested on data from Mus musculus liver and brain. The tool is based onprincipal component analysis, which is applied to data subsets containing theexpression data of genes likely regulated by a specific TF to acquire an estimationof its activity. Though initial tests on 17 selected TFs showed issues withunspecific trends in the estimations, further testing is required for a statementon the potential of the estimator.<br>Transcriptionsfaktorer (TFer) är viktiga regulatoriska protein som reglerar transkriptiongenom att binda till cis-regulatoriska element på precisa, menmycketvarierande vis. Komplexiteten i deras regulatoriska mönster gör det svårt attavgöra vilka roller olika TFer har, vilket är en uppgift som fältet fortfarandebrottas med. Experimentella procedurer i detta syfte, till exempel "knockout"experiment, är dock kostsamma och tidskrävande, och med den evigt ökandetillgången på sekvenseringsdata har metoder för att beräkna TFers aktivitetfrån sådan data fått stort intresse. De beräkningsmetoder som finns idag bristerdock på flera punker, vilket erfordrar ett fortsatt sökande efter alternativ. Ett nytt vektyg för att upskatta aktiviteten hos individuella TFer över tidmed hjälp av longitunell mRNA-uttrycksdata utvecklades därför i det här projektetoch testades på data från Mus musculus lever och hjärna. Verktyget ärbaserat på principalkomponentsanalys, som applicerades på set med uttrycksdatafrån gener sannolikt reglerade av en specifik TF för att erhålla en uppskattningav dess aktivitet. Trots att de första testerna för 17 utvalda TFer påvisadeproblem med ospecifika trender i upskattningarna krävs forsatta tester för attkunna ge ett tydligt svar på vilken potential estimatorn har.
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French, Benjamin. "Analysis of aggregate longitudinal data with time-dependent exposure /." Thesis, Connect to this title online; UW restricted, 2008. http://hdl.handle.net/1773/9569.
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Moore, Page Casey Seaman John Weldon. "A restriction method for the analysis of discrete longitudinal missing data." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/4880.
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Fu, Bo, and 傅博. "Some topics in longitudinal data analysis and panel time seriesmodels." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B31244166.
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Kurland, Brenda F. "Analysis of binary longitudinal data with dropout and death /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/9593.
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Solis-Trapala, Ivonne Lissette. "Likelihood methods for the analysis of discrete longitudinal data." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615839.
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Duncombe, Alastair Richard. "Bayesian analysis of longitudinal data with non random dropout." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272133.
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Jaffrezic, Florence. "Statistical models for the genetic analysis of longitudinal data." Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/12274.
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The first objective of this work was to compare and contrast different methodologies for genetic analysis. As the range of all possible models can be very large in practice, it is advisable to have a preliminary idea of the covariance structure of the data, and a non-parametric approach based on the variogram was proposed. It is especially adapted for exploratory analysis when a large number of observations is available per subject over time and was applied to the analysis of daily records for milk production in dairy cattle. Model comparisons in the univariate case showed that character processes were generally better able to fit the covariance structure than random regression with fewer parameters. However, CP models do not allow a straightforward extension to the multivariate case. Further research showed that structured antedependence models offer similar advantages to character processes compared to random regression while allowing an extension to multi-trait analyses. SAD models were even able to capture the highly non-stationary correlation pattern in the application to lactation curve analysis. For genetic evaluation of dairy cattle, longitudinal models can easily provide estimation of individual cumulative milk productions as well as genetic values at 305 days. However, these predictions do not take into account the drying-off process and can be highly overestimated for short lactations. A methodology to correct them was suggested. All these analyses were performed in the case of normally distributed longitudinal data. An extension to the genetic analysis of non-normally repeated measures was considered. Estimation procedure becomes much more complicated and requires the use of Markov Chain Monte Carlo methods. In this study antedependence models appeared to be the most appropriate for genetic analysis of longitudinal data.
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Beaghen, Michael Jr. "Canonical Variate Analysis and Related Methods with Longitudinal Data." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/29840.
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Canonical variate analysis (CVA) is a widely used method for analyzing group structure in multivariate data. It is mathematically equivalent to a one-way multivariate analysis of variance and often goes by the name of canonical discriminant analysis. Change over time is a central feature of many phenomena of interest to researchers. This dissertation extends CVA to longitudinal data. It develops models whose purpose is to determine what is changing and what is not changing in the group structure. Three approaches are taken: a maximum likelihood approach, a least squares approach, and a covariance structure analysis approach. All methods have in common that they hypothesize canonical variates which are stable over time.
The maximum likelihood approach models the positions of the group means in the subspace of the canonical variates. It also requires modeling the structure of the within-groups covariance matrix, which is assumed to be constant or proportional over time. In addition to hypothesizing stable variates over time, one can also hypothesize canonical variates that change over time. Hypothesis tests and confidence intervals are developed. The least squares methods are exploratory. They are based on three-mode PCA methods such as the Tucker2 and parallel factor analysis. Graphical methods are developed to display the relationships between the variables over time.
Stable variates over time imply a particular structure for the between-groups covariance matrix. This structure is modeled using covariance structure analysis, which is available in the SAS package Proc Calis.
Methods related to CVA are also discussed. First, the least squares methods are extended to canonical correlation analysis, redundancy analysis, Procrustes rotation and correspondence analysis with longitudinal data. These least squares methods lend themselves equally well to data from multiple datasets. Lastly, a least squares method for the common principal components model is developed.<br>Ph. D.
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Tharu, Bhikhari Prasad. "Statistical Analysis and Modeling Health Data: A Longitudinal Study." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6413.
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Lung cancer has been considered one of the leading causes of deaths while cancer re- mains the second most common cause of deaths in the USA. Understanding the behavior of a disease over time could yield important information to make decisions about the disease. Statistical models could provide crucial clues and help to make a decision about the dis- ease, budget allocation, evaluation, and implement prevention. Longitudinal trend analysis of the diseases helps to understand long term effects and nature. Cholesterol level is one of the most contributing risk factors for Coronary Heart Disease. Studying cholesterol statistically helps to know more about its nature and provides crucial information to mitigate its effectiveness in diagnosing its impact to public health.
In our study, we have analyzed lung cancer mortality in the USA based on age at death, period at death, and birth cohort to investigate its nature in longitudinal effects. The attempt has been made to estimate mortality rate based on age for different age groups and to find the relative risk of mortality due to period effect and relative risk due to birth cohort for lung cancer in the United States. Our statistical analysis and modeling are based on the data obtained from Surveillance Epidemiology and End Results (SEER) program of the United States.
We have also investigated the probabilistic behavior of average cholesterol level based on gender and ethnicity. The study reveals significant differences with respect to the distribution they follow and their basic inferences which could be beneficial to draw conclusions in various ways in addressing related issues. At the same time, the change of cholesterol level over time for an individual might be a good source to study the association of cholesterol level, coronary heart disease and their effects on age. The cholesterol data is obtained from inter-university Consortium for Political and Social Research and National Health and Nutrition Examination Survey (NHANS) of the United States.
Understanding the average change in total serum cholesterol level over time as people get older could be vital to explore it. We have studied the longitudinal behavior of the association of sex and time with cholesterol level. It is observed that age, sex, and time have an individual effect and can impact differently upon collective considerations. Their adverse effect in increasing cholesterol level could promote to worsen the cholesterol re- lated issues and hence heart related diseases. We believe our study pivots knowing more about target population of cholesterol level and helps to have the useful inference about cholesterol levels for public health.
Finally, we also analyzed the average cholesterol data using a functional data analysis approach to understand its nature and effect on age. Since functional data analysis approach presents more flexibility in modeling, it could provide more insight in studying cholesterol level.
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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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Rogers-McMillan, Sarah. "Diminishing egocentricity: a secondary analysis of longitudinal adolescent data." Thesis, Boston University, 2009. https://hdl.handle.net/2144/32000.
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Thesis (Ed.D.)--Boston University<br>PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>This study attempts to demonstrate the process of diminishing egocentricity, which appears to be central to the individual's evolving capacity to be in relation to the other, in the developing early-to-middle adolescent. It examines the widely accepted developmental theories that view egocentrism and cognitive maturation as being functions of subject-object differentiation. While egocentrism is acknowledged among developmental psychologists to occur, this study attempts to describe the still unarticulated understanding of the change process of diminishing egocentricity, its particular constitutive parts, their nature and function. As the field of developmental psychology has been by itself insufficient to the task of illuminating these processes, a psycho-philosophical mixed study is undertaken in a secondary analysis of A.C. Petersen's (1998) Adolescent Mental Health Study, 1978-1990 longitudinal data collected on early adolescents and followed-up in late adolescence and early adulthood. Existential phenomenology and G.W.F. Hegel's (1977) dialectical method inform the study's theoretical reframing of the problem of diminishing egocentricity in early-to-middle adolescence. The study utilizes CAQDAS, close reading method, grounded theory, and hermeneutical analysis to examine the narrative responses of 45 subjects to Petersen's (1998) study's Self-Image Questionnaire for Young Adolescents (SIQYA) in the qualitative analysis. The quantitative portion of the study makes use of Individual Growth Modeling (IGM) to analyze Petersen's (1998) full sample of SIQY A respondents as confirmation or refutation of the qualitative analysis. In addition to successfully arriving at a phenomenology of diminishing egocentricity that demonstrates the importance of a more authentic and integrated dialectical methodology than previously used in developmental research, the study's findings promote a critical retooling of concepts believed to be essential to our understanding of cognitive development generally and shown here to be relevant to diminishing egocentricity in particular, including abstract and concrete thinking qualities/capacities, object permanency, object relations, and subject-object-differentiation. The reframing of the current youth crisis in this more fully developed and unified theoretical (psychological/philosophical) system suggests that a greater emphasis on distinctively social experiential education/opportunities and skills-based activities in schools and therapeutic settings may provide one course for meaningful corrective action. Further study to create an integrated approach to experiential opportunities that promote social cognition is recommended.<br>2031-01-02
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Hu, Shuwen. "Statistical modeling and machine learning in longitudinal data analysis." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/211253/1/Shuwen_Hu_Thesis.pdf.
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This thesis mainly concerns the statistical modelling and machine learning methods for the analysis of longitudinal data. As a contribution to this area, this thesis provides theoretical discussion and empirical illustrations of longitudinal data analysis. The first contribution is developing methods to obtain robust and efficient variance estimators when the cluster size is large. The second one is comparing a traditional parametric approach, the linear mixed model with machine learning methods in longitudinal data analysis. The last one is extracting new features to improve sheep behaviour classification accuracy of different machine learning algorithms.
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Chang, Shu-Ching. "Antedependence Models for Skewed Continuous Longitudinal Data." Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/4827.
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This thesis explores the problems of fitting antedependence (AD) models and partial antecorrelation (PAC) models to continuous non-Gaussian longitudinal data. AD models impose certain conditional independence relations among the measurements within each subject, while PAC models characterize the partial correlation relations. The models are parsimonious and useful for data exhibiting time-dependent correlations.
Since the relation of conditional independence among variables is rather restrictive, we first consider an autoregressively characterized PAC model with independent asymmetric Laplace (ALD) innovations and prove that this model is an AD model. The ALD distribution previously has been applied to quantile regression and has shown promise for modeling asymmetrically distributed ecological data. In addition, the double exponential distribution, a special case of the ALD, has played an important role in fitting symmetric finance and hydrology data. We give the distribution of a linear combination of independent standard ALD variables in order to derive marginal distributions for the model. For the model estimation problem, we propose an iterative algorithm for the maximum likelihood estimation. The estimation accuracy is illustrated by some numerical examples as well as some longitudinal data sets.
The second component of this dissertation focuses on AD multivariate skew normal models. The multivariate skew normal distribution not only shares some nice properties with multivariate normal distributions but also allows for any value of skewness. We derive necessary and sufficient conditions on the shape and covariance parameters for multivariate skew normal variables to be AD(p) for some p. Likelihood-based estimation for balanced and monotone missing data as well as likelihood ratio hypothesis tests for the order of antedependence and for zero skewness under the models are presented.
Since the class of skew normal random variables is closed under the addition of independent standard normal random variables, we then consider an autoregressively characterized PAC model with a combination of independent skew normal and normal innovations. Explicit expressions for the marginals, which all have skew normal distributions, and maximum likelihood estimates of model parameters, are given.
Numerical results show that these three proposed models may provide reasonable fits to some continuous non-Gaussian longitudinal data sets. Furthermore, we compare the fits of these models to the Treatment A cattle growth data using penalized likelihood criteria, and demonstrate that the AD(2) multivariate skew normal model fits the data best among those proposed models.
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Cao, Yu. "Bayesian nonparametric analysis of longitudinal data with non-ignorable non-monotone missingness." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5750.
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In longitudinal studies, outcomes are measured repeatedly over time, but in reality clinical studies are full of missing data points of monotone and non-monotone nature. Often this missingness is related to the unobserved data so that it is non-ignorable. In such context, pattern-mixture model (PMM) is one popular tool to analyze the joint distribution of outcome and missingness patterns. Then the unobserved outcomes are imputed using the distribution of observed outcomes, conditioned on missing patterns. However, the existing methods suffer from model identification issues if data is sparse in specific missing patterns, which is very likely to happen with a small sample size or a large number of repetitions. We extend the existing methods using latent class analysis (LCA) and a shared-parameter PMM. The LCA groups patterns of missingness with similar features and the shared-parameter PMM allows a subset of parameters to be different among latent classes when fitting a model, thus restoring model identifiability. A novel imputation method is also developed using the distribution of observed data conditioned on latent classes. We develop this model for continuous response data and extend it to handle ordinal rating scale data. Our model performs better than existing methods for data with small sample size. The method is applied to two datasets from a phase II clinical trial that studies the quality of life for patients with prostate cancer receiving radiation therapy, and another to study the relationship between the perceived neighborhood condition in adolescence and the drinking habit in adulthood.
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Rocha, Everton Batista da. "Modelos para a análise de dados de contagens longitudinais com superdispersão: estimação INLA." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-05112015-144057/.
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Em ensaios clínicos é muito comum a ocorrência de dados longitudinais discretos. Para sua análise é necessário levar em consideração que dados observados na mesma unidade experimental ao longo do tempo possam ser correlacionados. Além dessa correlação inerente aos dados é comum ocorrer o fenômeno de superdispersão (ou sobredispersão), em que, existe uma variabilidade nos dados além daquela captada pelo modelo. Um caso que pode acarretar a superdispersão é o excesso de zeros, podendo também a superdispersão ocorrer em valores não nulos, ou ainda, em ambos os casos. Molenberghs, Verbeke e Demétrio (2007) propuseram uma classe de modelos para acomodar simultaneamente a superdispersão e a correlação em dados de contagens: modelo Poisson, modelo Poisson-gama, modelo Poisson-normal e modelo Poisson-normal-gama (ou modelo combinado). Rizzato (2011) apresentou a abordagem bayesiana para o ajuste desses modelos por meio do Método de Monte Carlo com Cadeias de Markov (MCMC). Este trabalho, para modelar a incerteza relativa aos parâmetros desses modelos, considerou a abordagem bayesiana por meio de um método determinístico para a solução de integrais, INLA (do inglês, Integrated Nested Laplace Approximations). Além dessa classe de modelos, como objetivo, foram propostos outros quatros modelos que também consideram a correlação entre medidas longitudinais e a ocorrência de superdispersão, além da ocorrência de zeros estruturais e não estruturais (amostrais): modelo Poisson inacionado de zeros (ZIP), modelo binomial negativo inacionado de zeros (ZINB), modelo Poisson inacionado de zeros - normal (ZIP-normal) e modelo binomial negativo inacionado de zeros - normal (ZINB-normal). Para ilustrar a metodologia desenvolvida, um conjunto de dados reais referentes à contagens de ataques epilépticos sofridos por pacientes portadores de epilepsia submetidos a dois tratamentos (um placebo e uma nova droga) ao longo de 27 semanas foi considerado. A seleção de modelos foi realizada utilizando-se medidas preditivas baseadas em validação cruzada. Sob essas medidas, o modelo selecionado foi o modelo ZIP-normal, sob o modelo corrente na literatura, modelo combinado. As rotinas computacionais foram implementadas no programa R e são parte deste trabalho.<br>Discrete and longitudinal structures naturally arise in clinical trial data. Such data are usually correlated, particularly when the observations are made within the same experimental unit over time and, thus, statistical analyses must take this situation into account. Besides this typical correlation, overdispersion is another common phenomenon in discrete data, defined as a greater observed variability than that nominated by the statistical model. The causes of overdispersion are usually related to an excess of observed zeros (zero-ination), or an excess of observed positive specific values or even both. Molenberghs, Verbeke e Demétrio (2007) have developed a class of models that encompasses both overdispersion and correlation in count data: Poisson, Poisson-gama, Poisson-normal, Poissonnormal- gama (combined model) models. A Bayesian approach was presented by Rizzato (2011) to fit these models using the Markov Chain Monte Carlo method (MCMC). In this work, a Bayesian framework was adopted as well and, in order to consider the uncertainty related to the model parameters, the Integrated Nested Laplace Approximations (INLA) method was used. Along with the models considered in Rizzato (2011), another four new models were proposed including longitudinal correlation, overdispersion and zero-ination by structural and random zeros, namely: zero-inated Poisson (ZIP), zero-inated negative binomial (ZINB), zero-inated Poisson-normal (ZIP-normal) and the zero-inated negative binomial-normal (ZINB-normal) models. In order to illustrate the developed methodology, the models were fit to a real dataset, in which the response variable was taken to be the number of epileptic events per week in each individual. These individuals were split into two groups, one taking placebo and the other taking an experimental drug, and they observed up to 27 weeks. The model selection criteria were given by different predictive measures based on cross validation. In this setting, the ZIP-normal model was selected instead the usual model in the literature (combined model). The computational routines were implemented in R language and constitute a part of this work.
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O'Keeffe, Aidan Gerard. "Causal inference and dynamic modelling in the analysis of longitudinal data." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609903.
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Park, Jeanseong. "Longitudinal Data Analysis Using Generalized Linear Model with Missing Responses." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/33355.
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Longitudinal studies rely on data collected at several occasions from a set of selected individuals. The purpose of these studies is to use a regression-type model to express a response variable as a function of explanatory variables, or covariates. In this thesis, we use marginal models for the analysis of such data, which, coupled with the method of estimating equations, provide estimators of the main regression parameter. When some of the responses are missing or there is error in the recorded covariates, the original estimating equation may be biased. We use techniques available in the literature to modify it and regain the unbiasedness property. We prove the asymptotic normality of the regression estimator obtained under these more realistic circumstances, and provide theoretical and numerical examples to illustrate this approach.
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Kohlmann, Mareike. "Discriminant Analysis for Longitudinal Data with Application in Medical Diagnostics." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-127645.
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Yi, Qilong. "Random effects and AR(1) models in longitudinal data analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ49731.pdf.
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Salem, Rany Mansour. "Statistical methods for genetic association analysis involving complex longitudinal data." Diss., [La Jolla] : [San Diego] : University of California, San Diego ; San Diego State University, 2009. http://wwwlib.umi.com/cr/ucsd/fullcit?p3366492.
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Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2009.<br>Title from first page of PDF file (viewed Aug. 14, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.