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31 
NONPARAMETRIC METHODS IN COMPARING TWO CORRELATED ROC CURVESBandos, Andriy 13 September 2005 (has links)
Receiver Operating Characteristic (ROC) analysis is one of the most widely used methods for summarizing intrinsic properties of a diagnostic system, and is often used in evaluation and comparison of diagnostic technologies, practices or systems. These methods play an important role in public health since they enable researchers to achieve a greater insight into the properties of diagnostic tests and eventually to identify a more appropriate and beneficial procedure for diagnosing or screening for a specific disease or condition. The topic of this dissertation is the nonparametric testing of hypotheses about ROC curves in a paired design setting. Presently only a few nonparametric tests are available for the task of comparing two correlated ROC curves. Thus we focus on this basic problem leaving the extensions to more complex settings for future research. In this work, we study the smallsample properties of the conventional nonparametric method presented by DeLong et al. and develop three novel nonparametric approaches for comparing diagnostic systems using the area under the ROC curve. The permutation approach that we present enables conducting an exact test and allows for an easytouse asymptotic approximation. Next, we derive a closedform bootstrapvariance, construct an asymptotic test, and compare them to the existing competitors. Finally exploiting the idea of discordances we develop a conceptually new conditional approach that offers advantages in certain types of studies.

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ESTIMATION OF THE SURVIVAL FUNCTION FOR GRAY'S PIECEWISECONSTANT TIMEVARYING COEFFICIENTS MODELValenta, Zdenek 29 April 2002 (has links)
Gray's extension of Cox's proportional hazards (PH) model for rightcensored survival data allows for a departure from the PH assumption via introduction of timevarying regression coefficients (TVC) using penalized splines. Gray's work focused on estimation, inference and residual analyses, but no estimator for the survival function has been proposed. We derive a survival function estimator for one important member of the class of TVC models  a piecewiseconstant timevarying coefficients (PCTVC) model. We also derive an estimate for the confidence limits of the survival function. Accuracy in estimating underlying survival times and survival quantiles is assessed for both Cox's and Gray's PCTVC model using a simulation study featuring scenarios violating the PH assumption. Finally, an example of the estimated survival functions and the corresponding confidence limits derived from Cox's PH and Gray's PCTVC model, respectively, is presented for a liver transplant data set.
In the second part of the thesis we examine the effect of model misspecification for two classes of regression models for rightcensored survival data  additive and multiplicative models for the conditional hazard rate. A particular attention is given to data exhibiting timevarying regression coefficients. The class of multiplicative models is represented by Cox PH model and Gray's TVC model, respectively, and for additive models we use Aalen's linear model. Both Gray's TVC model and Aalen's linear model incorporate timevarying coefficients. A simulation study is performed to crossanalyze survival data which follows either a multiplicative or an additive model for the conditional hazard rate. The effect of misspecifying the true model for the conditional hazard rate is assessed by looking at the power of the individual models to detect an existing effect, bias and mean square error observed for each conditional modelbased estimator of survival. We also show that Aalen's model formulae is a first order Taylor series approximation of that of Gray's model which explains the comparably higher flexibility on part of the Aalen's model as compared to the Cox PH when the Gray's TVC model for the data is misspecified.

33 
Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing CovariatesZhang, Zhiwei 12 December 2003 (has links)
Parametric regression models are widely used in public health sciences. This dissertation is concerned with statistical inference under such models with some covariates missing at random. Under natural conditions, parameters remain identifiable from the observed (reduced) data. If the always observed covariates are discrete or can be discretized, we propose a semiparametric maximum likelihood method which requires no parametric specification of the selection mechanism or the covariate distribution. Simple conditions are given under which the semiparametric maximum likelihood estimator (MLE) exists. For ease of computation, we also consider a restricted MLE which maximizes the likelihood over covariate distributions supported by the observed values. The two MLEs are asymptotically equivalent and strongly consistent for a class of topologies on the parameter set. Upon normalization, they converge weakly to a zeromean Gaussian process in a suitable space. The MLE of the regression parameter, in particular, achieves the semiparametric information bound, which can be consistently estimated by perturbing the profile loglikelihood. Furthermore, the profile likelihood ratio statistic is asymptotically chisquared. An EM algorithm is proposed for computing the restricted MLE and for variance estimation. Simulation results suggest that the proposed method performs resonably well in moderatesized samples. In contrast, the analogous parametric maximum likelihood method is subject to severe bias under model misspecification, even in large samples. The proposed method can be applied to related statistical problems.

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Regression Analysis In Longitudinal Studies With Nonignorable Missing OutcomesShen, Changyu 21 April 2004 (has links)
One difficulty in regression analysis for longitudinal data is that the outcomes are often
missing in a nonignorable way (Little & Rubin, 1987). Likelihood based approaches to
deal with nonignorable missing outcomes can be divided into selection models and pattern
mixture models based on the way the joint distribution of the outcome and the missingdata
indicators is partitioned. One new approach from each of these two classes of models is
proposed. In the first approach, a normal copulabased selection model is constructed to
combine the distribution of the outcome of interest and that of the missingdata indicators
given the covariates. Parameters in the model are estimated by a pseudo maximum likelihood
method (Gong & Samaniego, 1981). In the second approach, a pseudo maximum likelihood
method introduced by Gourieroux et al. (1984) is used to estimate the identifiable parameters
in a pattern mixture model. This procedure provides consistent estimators when the mean
structure is correctly specified for each pattern, with further information on the variance
structure giving an efficient estimator. A Hausman type test (Hausman, 1978) of model
misspecification is also developed for model simplification to improve efficiency. Separate
simulations are carried out to assess the performance of the two approaches, followed by
applications to real data sets from an epidemiological cohort study investigating dementia,
including Alzheimer's disease.

35 
FORMULATION OF POPULATION PHARMACOKINETIC MODELS OF ANTICANCER AGENTSRadhakrishnan, Rajkumar 21 May 2004 (has links)
The primary objective of the study is to assemble population pharmacokinetic models from the cancer pharmacokinetics literature for different types of anticancer drugs and to formulate them in ways suitable for input into cancer simulation programs.
To fulfill the objectives, a stepbased approach is adopted:
1)To catalogue the types of pharmacokinetic models through general review articles and books
2)To develop a search strategy for defining a body of research literature related to cancer pharmacokinetics in clinical trials for a limited set of drugs (Taxol, Platinum compounds. Fluoropyrimidine and Topoisomerase inhibitors)
3)To collect pharmacokinetic articles according to defined search criteria
4)To gather information from the collected PK articles
5)To synthesize the information separately for each drug, using a questionnaire instrument and present them in template form for each class of antineoplastic agent.
6)To formulate population pharmacokinetic models for each anticancer drug, from the constituent submodels for components of the overall model.
This work will promote public health, specifically in support of the development of anticancer drug regimens for cancer patients, by
providing standardized information about pharmacokinetics for input into simulations.

36 
An Analytic Approach in Identifying a Latent Structure to Determine Scoring Criteria for a Clinical Diagnosis of Traumatic GriefBarrow, Genevieve M 08 July 2004 (has links)
Although reliably recognized as a psychiatric syndrome, traumatic grief has not been identified as a primary axis I diagnosis in the Diagnostic and Statistical Manual of Mental Disorders fourth edition (DSMIV). Due to the newness of recognition of this diagnosis, a universally accepted set of diagnostic criteria does not exist. The objective of this thesis was to evaluate the psychometric properties of a newly developed twentynine item structured clinical interview for the diagnosis of traumatic grief (SCITG) by: assessing its internal consistency; evaluating its interrater reliability; describing its factor structure; and determining its construct validity.
The SCITG was administered to 166 patients enrolled in an ongoing traumatic grief therapy randomized clinical trial (TGTRCT) MH060783. The SCITG showed good internal consistency as assessed by the Cronbachs coefficient alpha (0.74), and good interrater reliability (0.81). Exploratory principal components factor analysis yielded the selection of three factors corresponding to symptoms of: guilt, failure to adapt, and separation distress, respectively. Demonstrating convergent validity, the total score of the SCITG was significantly correlated with the Inventory of Complicated Grief (ICG), the Hamilton Depression Rating Scale (HDRS), the Structured Interview Guidelines for the Hamilton Rating Scale for Anxiety (SIGHA), the Impact of Events Scale (IES), and the Adult Separation Anxiety Disorder (ASAD). The estimated factor scores on factor 1, guilt, were not significantly correlated with any of these instruments, and the estimated factor scores on the separation distress factor was not significantly correlated with the ASAD, signifying the uniqueness of traumatic grief symptoms. The results of the factor analyses could be used to create subscales of the new 17item SCITG. The distribution of the SCITG scores based on the reduced scale resulting from the factor analyses was proposed to be used in determining the scoring of this instrument. Studies of the treatment of bereavement with antidepressants have proven ineffective in treating grief symptoms. The public health relevance of this thesis is in defining these symptoms and developing an instrument to adequately identify such symptoms.

37 
MAPPING GENES FOR QUANTITATIVE TRAITS USING SELECTED SAMPLES OF SIBLING PAIRSSzatkiewicz, Jin Peng 22 July 2004 (has links)
One of the most important research areas in human genetics is the effort to map genes associated with complex diseases such as cancer, heart disease, and diabetes. The public health relevance of these kinds of work is that gene mapping will bring an understanding of genetic risk and protective factors, and a description of the interaction between environment and genetic variation. In the last ten years there has been a dramatic increase in the number of studies seeking to map genes for quantitative traits. This has caused an explosion of new work on statistical methods for human quantitative trait locus (QTL) mapping. However, little of that work has dealt with selected samples, which are more common than population samples for human studies. This dissertation focuses on sibling pairs and considers the most common types of selected sampling. I surveyed most QTL mapping methods in the literature to evaluate which are appropriate for selected samples, and also developed new statistics for selected samples. Using simulation and analytical approaches, I identified the most powerful statistics for each type of sampling considered. I then compared various sampling designs using the best statistic for each and gave guidelines for choosing appropriate and powerful designs under different scenarios.

38 
USING TRAJECTORIES FROM A BIVARIATE GROWTH CURVE OF COVARIATES IN A COX MODEL ANALYSISDang, Qianyu 27 August 2004 (has links)
In many maintenance treatment trials, patients are first enrolled into an open treatment
before they are randomized into treatment groups. During this period, patients are followed
over time with their responses measured longitudinally. This design is very common in
today's public health studies of the prevention of many diseases. Using mixed model theory, one
can characterize these data using a wide array of across subject models. A statespace
representation of the mixed model and use of the Kalman filter allow more fexibility in
choosing the within error correlation structure even in the presence of missing and unequally
spaced observations. Furthermore, using the statespace approach, one can avoid inverting
large matrices resulting in eficient computations. Estimated trajectories from these models can be used as predictors in a survival analysis in judging the efacacy of the maintenance treatments. The statistical problem lies in accounting for the estimation error in these predictors. We considered a bivariate growth curve where the longitudinal responses were unequally spaced and assumed that the within subject errors followed a continuous first
order autoregressive (CAR (1)) structure. A simulation study was conducted to validate
the model. We developed a method where estimated random effects for each subject from
a bivariate growth curve were used as predictors in the Cox proportional hazards model,
using the full likelihood based on the conditional expectation of covariates to adjust for the estimation errors in the predictor variables. Simulation studies indicated that error corrected estimators for model parameters are mostly less biased when compared with the
nave regression without accounting for estimation errors. These results hold true in Cox
models with one or two predictors. An illustrative example is provided with data from a maintenance treatment trial for major depression in an elderly population. A Visual Fortran 90 and a SAS IML program are developed.

39 
GENERALIZED ADDITIVE MODELS FOR DATA WITH CONCURVITY: STATISTICAL ISSUES AND A NOVEL MODEL FITTING APPROACHHe, Shui 03 December 2004 (has links)
The Generalized Additive model (GAM) has been used as a standard tool for epidemiologic analysis exploring the effect of air pollution on population health during the last decade as it allows nonparametric relationships between the independent predictors and response. One major concern to the use of the GAM is the presence of concurvity in the data. The standard statistical software, such as Splus, can seriously overestimate the GAM model parameters and underestimate their variances in the presence of concurvity. We explore an alternate class of models, generalized linear models with natural cubic splines (GLM+NS), that may not be affected as much by concurvity. We make systematic comparisons between GLM+NS and GAMs with smoothing splines (GAM+S) in the presence of varying degrees of concurvity using simulated data. Our results suggest that GLM+NS perform better than GAM+S when mediumtohigh concurvity exists in the data. Since GLM+NS result in loss in flexibility, we also investigate an alternative approach to fit a GAM. This approach, which is based on partial residuals, gives regression coefficients and variance estimates with less bias in the presence of concurvity, compared to the estimates obtained by the standard approach. It can accommodate asymmetric smoothers and is more robust with respect to the choice of smoothing parameters. Illustrative examples are provided. The public health significance of this study is that the proposed approach improves the estimate of adverse health effect of air pollution, which is important for public and governmental agencies to revise healthbased regulatory standards for ambient air pollution.

40 
POSITIVE ASPECTS OF ALZHEIMER'S CAREGIVING: THE ROLE OF ETHNICITYSun, Kang 02 February 2006 (has links)
The study examined differences in positive aspects of caregiving (PAC) among 232 Hispanic caregivers and 691 NonHispanic Caucasian (NHC) caregivers of individuals with Alzheimer's disease, using baseline data of National Institutes of Health Resources for Enhancing Alzheimer's Care Health (REACH) study. Multiple linear regression models, mediation analysis and Sobel's test were performed to assess the mediating effects of five possible mediators (education, socioeconomic status, behavior bother, social support and religiosity). Hispanics caregivers reported higher scores on PAC than their NHC counterparts. Hispanic caregivers' higher religiosity partially mediated the relationship between ethnicity and PAC. Additional variables that contributed to their higher PAC scores were caregivers' lower education level and lower socioeconomic status. A similar approach was used to compare values of PAC between 77 Mexican and 88 Cuban female caregivers. Mexican female caregivers reported statistically significant higher PAC when compared with Cuban female caregivers. The full mediation of socioeconomic status (SES) and partial mediation of education were seen to exist in the relationship between PAC and ethnicity. The question of how or why the PAC differences exist between ethnic groups was partially answered by employing the mediation analysis. The public health importance of this thesis is to provide the information on the ethnic differences in PAC, which is useful for social and psychological interventions.

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