7 resultados para Latent class growth analysis

em DigitalCommons@The Texas Medical Center


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Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

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Injection drug use is the third most frequent risk factor for new HIV infections in the United States. A dual mode of exposure: unsafe drug using practices and risky sexual behaviors underlies injection drug users' (IDUs) risk for HIV infection. This research study aims to characterize patterns of drug use and sexual behaviors and to examine the social contexts associated with risk behaviors among a sample of injection drug users. ^ This cross-sectional study includes 523 eligible injection drug users from Houston, Texas, recruited into the 2009 National HIV Behavioral Surveillance project. Three separate set of analyses were carried out. First, using latent class analysis (LCA) and maximum likelihood we identified classes of behavior describing levels of HIV risk, from nine drug and sexual behaviors. Second, eight separate multivariable regression models were built to examine the odds of reporting a given risk behavior. We constructed the most parsimonious multivariable model using a manual backward stepwise process. Third, we examined whether HIV serostatus knowledge (self-reported positive, negative, or unknown serostatus) is associated with drug use and sexual HIV risk behaviors. ^ Participants were mostly male, older, and non-Hispanic Black. Forty-two percent of our sample had behaviors putting them at high risk, 25% at moderate risk, and 33% at low risk for HIV infection. Individuals in the High-risk group had the highest probability of risky behaviors, categorized as almost always sharing needles (0.93), seldom using condoms (0.10), reporting recent exchange sex partners (0.90), and practicing anal sex (0.34). We observed that unsafe injecting practices were associated with high risk sexual behaviors. IDUs who shared needles had higher odds of having anal sex (OR=2.89, 95%CI: 1.69-4.92) and unprotected sex (OR=2.66, 95%CI: 1.38-5.10) at last sex. Additionally, homelessness was associated with needle sharing (OR=2.24, 95% CI: 1.34-3.76) and cocaine use was associated with multiple sex partners (OR=1.82, 95% CI: 1.07-3.11). Furthermore, twenty-one percent of the sample was unaware of their HIV serostatus. The three groups were not different from each other in terms of drug-use behaviors: always using a new sterile needle, or in sharing needles or drug preparation equipment. However, IDUs unaware of their HIV serostatus were 33% more likely to report having more than three sexual partners in the past 12 months; 45% more likely to report to have unprotected sex and 85% more likely to use drug and or alcohol during or before at last sex compared to HIV-positive IDUs. ^ This analysis underscores the merit of LCA approach to empirically categorize injection drug users into distinct classes and identify their risk pattern using multiple indicators and our results show considerable overlap of high risk sexual and drug use behaviors among the high-risk class members. The observed clustering pattern of drug and sexual risk behavior among this population confirms that injection drug users do not represent a homogeneous population in terms of HIV risk. These findings will help develop tailored prevention programs.^

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Two approaches were utilized to investigate the role of pp60c-src activation in growth control of model colon tumor cell lines. The first approach involved analysis of pp60c-src activity in response to growth factor treatment to determine if transient activation of the protein was associated with ligand induced mitogenic signal transduction as occurs in non-colonic cell types. Activation of pp60c-src was detected using colon tumor cell lysates after treatment with platelet derived growth factor (PDGF). Activation of pp60c-src was also detected in response to epidermal growth factor (EGF) treatment using cellular lysates and intact cells. In contrast, down-regulation of purified pp60c-src occurred after incubation with EGF-treated EGFr immune complexes in vitro suggesting additional cellular events were potentially required for the stimulatory response observed in intact cells. The results demonstrated activation of pp60c-src in colon tumor cells in response to PDGF and EGF which is consistent with the role of the protein in mitogenic signal transduction in non-colonic cell types.^ The second approach used to study the role of pp60c-src activation in colonic cell growth control focused on analysis of the role of constitutive activation of the protein, which occurs in approximately 80% of colon tumors and cell lines, in growth control. These studies involved analysis of the effects of the tyrosine kinase specific inhibitor Herbimycin A (HA) on monolayer growth and pp60c-src enzymatic activity using model colon tumor cell lines. HA induced dose-dependent growth inhibition of all colon tumor cell lines examined possessing elevated pp60c-src activity. In HT29 cells the dose-dependent growth inhibition induced by HA correlated with dose-dependent pp60c-src inactivation. Inactivation of pp60c-src was shown to be an early event in response to treatment with HA which preceded induction of HT29 colon tumor cell growth inhibition. The growth effects of HA towards the colon tumor cells examined did not appear to be associated with induction of differentiation or a cytotoxic mechanism of action as changes in morphology were not detected in treated cells and growth inhibition (and pp60c-src inactivation) were reversible upon release from treatment with the compound. The results suggested the constitutive activation of pp60c-src functioned as a proliferative signal in colon tumor cells. Correlation between pp60c-src inactivation and growth inhibition was also observed using HA chemical derivatives confirming the role of tyrosine kinase inactivation by these compounds in inhibition of mitogenic signalling. In contrast, in AS15 cells possessing specific antisense mRNA mediated inactivation of pp60c-src, HA-induced inactivation of the related pp62c-yes tyrosine kinase, which is also activated during colon tumor progression, was not associated with induction of monolayer growth inhibition. These results suggested a function for the constitutively activated pp62c-yes protein in colon tumor cell proliferation which was different from that of activated pp60c-src. (Abstract shortened by UMI.) ^

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Ubiquitination is an essential process involved in basic biological processes such as the cell cycle and cell death. Ubiquitination is initiated by ubiquitin-activating enzymes (E1), which activate and transfer ubiquitin to ubiquitin-conjugating enzymes (E2). Subsequently, ubiquitin is transferred to target proteins via ubiquitin ligases (E3). Defects in ubiquitin conjugation have been implicated in several forms of malignancy, the pathogenesis of several genetic diseases, immune surveillance/viral pathogenesis, and the pathology of muscle wasting. However, the consequences of partial or complete loss of ubiquitin conjugation in multi-cellular organisms are not well understood. Here, we report the characterization of nba1, the sole E1 in Drosophila. We have determined that weak and strong nba1 alleluias behave genetically different and sometimes in opposing phenotypes. For example, weak uba1 alleluias protect cells from cell death whereas cells containing strong loss-of-function alleluias are highly apoptotic. These opposing phenotypes are due to differing sensitivities of cell death pathway components to ubiquitination level alterations. In addition, strong uba1 alleluias induce cell cycle arrest due to defects in the protein degradation of Cyclins. Surprisingly, clones of strong uba1 mutant alleluias stimulate neighboring wild-type tissue to undergo cell division in a non-autonomous manner resulting in severe overgrowth phenotypes in the mosaic fly. I have determined that the observed overgrowth phenotypes were due to a failure to downregulate the Notch signaling pathway in nba1 mutant cells. Aberrant Notch signaling results in the secretion of a local cytokine and activation of JAK/STAT pathway in neighboring cells. In addition, we elucidated a model describing the regulation of the caspase Dronc in surviving cells. Binding of Dronc by its inhibitor Diap1 is necessary but not sufficient to inhibit Dronc function. Ubiquitin conjugation and Uba1 function is necessary for the negative regulation of Dronc. ^

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This dissertation develops and tests through path analysis a theoretical model to explain how socioeconomic, socioenvironmental, and biologic risk factors simultaneously influence each other to further produce short-term, depressed growth in preschoolers. Three areas of risk factors were identified: child's proximal environment, maturational stage, and biological vulnerability. The theoretical model represented both the conceptual framework and the nature and direction of the hypotheses. Original research completed in 1978-80 and in 1982 provided the background data. It was analyzed first by nested-analysis of variance, followed by path analysis. The study provided evidence of mild iron deficiency and gastrointestinal symptomatology in the etiology of depressed, short-term weight gain. Also, there was evidence suggesting that family resources for material and social survival significantly contribute to the variability of short-term, age-adjusted growth velocity. These results challenge current views of unifocal intervention, whether for prevention or control. For policy formulations, though, the mechanisms underlying any set of interlaced relationships must be decoded. Theoretical formulations here proposed should be reassessed under a more extensive research design. It is suggested that studies should be undertaken where social changes are actually in progress; otherwise, nutritional epidemiology in developing countries operates somewhere between social reality and research concepts, with little grasp of its real potential. The study stresses that there is a connection between substantive theory, empirical observation, and policy issues. ^

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The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

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The tumor suppressor p53 is a phosphoprotein which functions as a transcriptional activator. By monitoring the transcriptional activity, we studied how p53 functions is regulated in relation to cell growth and contact inhibition. When cells were arrested at G1 phase of the cell cycle by contact inhibition, we found that p53 transactivation function was suppressed. When contact inhibition was overridden by cyclin E overexpression which stimulates cell cycle progression, p53 function was restored. This observation led to the development of a cell density assay to study the regulation of p53 function during cell cycle for the functional significance of p53 phosphorylation. The murine p53 is phosphorylated at serines 7, 9, 12, 18, 37, 312 and 389. To understand the role of p53 phosphorylation, we generated p53 constructs encoding serine-to-alanine or serine-to-glutamate mutations at these codons. The transcriptional activity were measured in cells capable of contact inhibition. In low-density cycling cells, no difference in transcriptional activity was found between wild type p53 and any of the mutants. In contact-inhibited cells, however, only mutations of p53 at serine 389 resulted in altered responses to cell cycle arrest and to cyclin E overexpression. The mutant with serine-to-glutamate substitution at codon 389 retained its function in contact inhibited cells. Cyclin E overexpression in these cells induced p53 phosphorylation at serine 389. Furthermore, we showed that phosphorylation at serine 389 regulates p53 DNA binding activity. Our findings implicate that phosphorylation is an important mechanism for p53 activation.^ p53 is the most frequently mutated gene in human tumors. To study the mechanism of p53 inactivation by mutations, we carried out detailed analysis of a murine p53 mutation with an arginine-to-tryptophane substitution at codon 245. The corresponding human p53 mutation at amino acid 248 is the most frequently mutated codon in tumors. We showed that this mutant is inactive in suppressing focus formation, binding to DNA and transactivation. Structural analysis revealed that this mutant assumes the wild type protein conformation. These findings define a novel class of p53 mutations and help to understand structure-function relationship of p53. ^