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Browsing by Subject "Bayesian hierarchical models"

Now showing 1 - 3 of 3
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    Application of an active comparator-based benefit-risk Assessment in evaluating clinical trial design features of a new chemical entity using a Bayesian decision-theoretic framework.
    (2010-06) Goel, Varun
    During the drug development process, drug candidates are screened for their efficacy and toxicity. Dose selection is a crucial part of drug development and specifying the right dose imparts pharmacological activity while minimizing side effects. Evaluation of the benefit/risk ratio is typically done by examining the effect of a drug on efficacy and safety endpoints. However, this comparison can be difficult when there are multiple endpoints that are clinically and commercially relevant. A decision-based clinical utility is proposed and evaluated to aid in dose selection. A dose is viable if it has higher efficacy and lower toxicity than the values specified in multi-attribute decision criteria. PD 0200390 is a ligand of the α2δ subunit of the voltage-gated calcium channel being investigated for the treatment of primary insomnia and non-restorative sleep. Wake after sleep onset and number of awakenings are the measures of sleep consolidation while ease of awakening and morning behavior following wakefulness are the measures of residual effects. The objective of this research is to select a dose that maximizes the probability of a decision criterion characterized over safety and efficacy attributes. Data is obtained from two phase II double blind, randomized, placebo controlled crossover studies in subjects with primary insomnia. Dose response models are developed as hierarchical nonlinear model using NONMEM® and WinBUGS®. A Sensitivity analysis is performed to test the robustness of the selected dose with varying decision attributes.
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    Statistical methods for multivariate meta-analysis
    (2018-07) Lian, Qinshu
    As health problems get more complicated, the medical decisions and policies are rarely determined by evidence on a single effect. In recent years, there is a wide acknowledgment of the drawbacks of using separate univariate meta-analyses to solve a clearly multivariate problem. This has led to increased attention to multivariate meta-analysis, which is a generalization of standard univariate meta-analysis to synthesize evidence on multiple outcomes or treatments. Recently developments in multivariate meta-analysis have been driven by a wide variety of application areas. This thesis focuses on three areas in which multivariate meta-analysis is highly important but is not yet well developed: network meta-analysis of diagnostic tests, meta-analysis of observational studies accounting for exposure misclassification, and meta-regression methods adjusting for post-randomization variables. In studies evaluating the accuracy of diagnostic tests, three designs are commonly used, crossover, randomized, and non-comparative. Existing methods for meta-analysis of diagnostic tests mainly consider simple cases in which the reference test in all or none of the studies can be considered a gold standard test, and in which all studies use either a randomized or non-comparative design. To overcome the limitations of current methods, the Bayesian hierarchical summary receiver operating characteristic model is extended to network meta-analysis of diagnostic tests to simultaneously compare multiple tests within a missing data framework. The method accounts for correlations between multiple tests and for heterogeneity between studies. It also allows different studies to include different subsets of diagnostic tests and provides flexibility in the choice of summary statistics. In observational studies, misclassification of exposure is ubiquitous and can substantially bias the estimated association between an outcome and an exposure. Although misclassification in a single observational study has been well studied, few papers have considered it in a meta-analysis. A novel Bayesian approach is proposed to fill this methodological gap. We simultaneously synthesize two (or more) meta-analyses, with one on the association between a misclassified exposure and an outcome (main studies), and the other on the association between the misclassified exposure and the true exposure (validation studies). We extend the current scope for using external validation data by relaxing the "transportability'' assumption by means of random effects models. The proposed model accounts for heterogeneity between studies and can be extended to allow different studies to have different exposure measurements. Meta-regression is widely used in systematic reviews to investigate sources of heterogeneity and the association of study-level covariates with treatment effectiveness. Although existing meta-regression approaches have been successful in adjusting for baseline covariates, these methods have several limitations in adjusting for post-randomization variables. We propose a joint meta-regression method adjusting for post-randomization variables. The proposed method simultaneously estimates the treatment effect on the primary outcome and on the post-randomization variables. It takes both between- and within-study variability in post-randomization variables into consideration. Missing data is allowed in the primary outcome and the post-randomization variables, and uncertainty in the missing data is taken into consideration. All the proposed models are evaluated in simulation studies and are illustrated using real meta-analytic datasets.
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    Voxel-wise Classification of Prostate Cancer Using Multi-parametric MRI Data
    (2019-06) Jin, Jin
    As a continuously developing tool for the diagnosis and prognosis of prostate cancer, multi-parametric magnetic resonance imaging (mpMRI) has been widely used in a variety of prostate cancer-related topics. While current research has shown the great potential of mpMRI in detecting prostate cancer, further investigation is needed for modeling some specific features of mpMRI, including the anatomic difference between different regions of a prostate, the spatial correlation between voxels within each prostate image, and the difference in the distribution of the observed mpMRI parameters between patients. This dissertation focuses on novel statistical methods for the voxel-wise classification of prostate cancer using mpMRI data. Systematic modeling frameworks will be proposed to improve cancer classification by incorporating the aforementioned features of mpMRI. Three topics are discussed in depth: (1) development of a general Bayesian modeling framework that can incorporate the various mpMRI features; (2) how to model the mpMRI features in the proposed Bayesian framework, preferably in a computationally efficient manner; (3) development of an alternative approach to accounting for the mpMRI features, which uses a multi-resolution modeling technique to account for the regional heterogeneity, and is flexible to be extended to more complex classification problems for prostate cancer. The solutions are presented in the following order. In Chapter 2, we propose a Bayesian hierarchical modeling framework that allows complex distributional assumptions for the various data components. Based on the modeling framework, two approaches will be proposed for modeling the heterogeneity between regions of the prostate, which can be combined with a spatial Gaussian kernel smoother to account for residual spatial correlation and reduce random noise in the data. In Chapter 3, we add additional layers in the hierarchical model to model the spatial correlation structure and between-patient heterogeneity. Modeling the spatial correlation structure is computationally challenging and even infeasible for our mpMRI data set, due to the large number of voxels within each image. Three scalable spatial modeling approaches are then proposed for the correlation between voxels. In Chapter 4, we develop an alternative, machine learning-based method to account for the mpMRI features: a super learner with an ensemble learning technique is utilized to combine base learners trained in multi-resolution sub-regions. Specific algorithms will be introduced for both the classification of binary cancer status, and a more complex problem: classification of an ordinal outcome that indicates the clinical significance of prostate cancer. Method performance will be illustrated by simulation studies and applications to in vivo data that motivated the method's development.