Analyzing change in medication use - statistical approaches

The objective of this study was to gain an understanding of the effects of population heterogeneity, missing data, and causal relationships on parameter estimates from statistical models when analyzing change in medication use. From a public health perspective, two timely topics were addressed: the use and effects of statins in populations in primary prevention of cardiovascular disease and polypharmacy in older population. Growth mixture models were applied to characterize the accumulation of cardiovascular and diabetes medications among apparently healthy population of statin initiators. The causal effect of statin adherence on the incidence of acute cardiovascular events was estimated using marginal structural models in comparison with discrete-time hazards models. The impact of missing data on the growth estimates of evolution of polypharmacy was examined comparing statistical models under different assumptions for missing data mechanism. The data came from Finnish administrative registers and from the population-based Geriatric Multidisciplinary Strategy for the Good Care of the Elderly study conducted in Kuopio, Finland, during 2004–07. Five distinct patterns of accumulating medications emerged among the population of apparently healthy statin initiators during two years after statin initiation. Proper accounting for time-varying dependencies between adherence to statins and confounders using marginal structural models produced comparable estimation results with those from a discrete-time hazards model. Missing data mechanism was shown to be a key component when estimating the evolution of polypharmacy among older persons. In conclusion, population heterogeneity, missing data and causal relationships are important aspects in longitudinal studies that associate with the study question and should be critically assessed when performing statistical analyses. Analyses should be supplemented with sensitivity analyses towards model assumptions.

Uncertainties in projecting climate-change impacts in marine ecosystems

Projections of the impacts of climate change on marine ecosystems are a key prerequisite for the planning of adaptation strategies, yet they are inevitably associated with uncertainty. Identifying, quantifying, and communicating this uncertainty is key to both evaluating the risk associated with a projection and building confidence in its robustness. We review how uncertainties in such projections are handled in marine science. We employ an approach developed in climate modelling by breaking uncertainty down into (i) structural (model) uncertainty, (ii) initialization and internal variability uncertainty, (iii) parametric uncertainty, and (iv) scenario uncertainty. For each uncertainty type, we then examine the current state-of-the-art in assessing and quantifying its relative importance. We consider whether the marine scientific community has addressed these types of uncertainty sufficiently and highlight the opportunities and challenges associated with doing a better job. We find that even within a relatively small field such as marine science, there are substantial differences between subdisciplines in the degree of attention given to each type of uncertainty. We find that initialization uncertainty is rarely treated explicitly and reducing this type of uncertainty may deliver gains on the seasonal-to-decadal time-scale. We conclude that all parts of marine science could benefit from a greater exchange of ideas, particularly concerning such a universal problem such as the treatment of uncertainty. Finally, marine science should strive to reach the point where scenario uncertainty is the dominant uncertainty in our projections.

ESSAYS IN ENERGY, ENVIRONMENT AND TECHNOLOGICAL CHANGE

This dissertation studies technological change in the context of energy and environmental economics. Technology plays a key role in reducing greenhouse gas emissions from the transportation sector. Chapter 1 estimates a structural model of the car industry that allows for endogenous product characteristics to investigate how gasoline taxes, R&D subsidies and competition affect fuel efficiency and vehicle prices in the medium-run, both through car-makers' decisions to adopt technologies and through their investments in knowledge capital. I use technology adoption and automotive patents data for 1986-2006 to estimate this model. I show that 92% of fuel efficiency improvements between 1986 and 2006 were driven by technology adoption, while the role of knowledge capital is largely to reduce the marginal production costs of fuel-efficient cars. A counterfactual predicts that an additional $1/gallon gasoline tax in 2006 would have increased the technology adoption rate, and raised average fuel efficiency by 0.47 miles/gallon, twice the annual fuel efficiency improvement in 2003-2006. An R&D subsidy that would reduce the marginal cost of knowledge capital by 25% in 2006 would have raised investment in knowledge capital. This subsidy would have raised fuel efficiency only by 0.06 miles/gallon in 2006, but would have increased variable profits by $2.3 billion over all firms that year. Passenger vehicle fuel economy standards in the United States will require substantial improvements in new vehicle fuel economy over the next decade. Economic theory suggests that vehicle manufacturers adopt greater fuel-saving technologies for vehicles with larger market size. Chapter 2 documents a strong connection between market size, measured by sales, and technology adoption. Using variation consumer demographics and purchasing pattern to account for the endogeneity of market size, we find that a 10 percent increase in market size raises vehicle fuel efficiency by 0.3 percent, as compared to a mean improvement of 1.4 percent per year over 1997-2013. Historically, fuel price and demographic-driven market size changes have had large effects on technology adoption. Furthermore, fuel taxes would induce firms to adopt fuel-saving technologies on their most efficient cars, thereby polarizing the fuel efficiency distribution of the new vehicle fleet.

Interaction of Multiwalled Carbon Nanotube Produces Structural Alteration of DNA

Multiwalled carbon nanotube (MWCNT) has been found to produce structural changes in Calf Thymus-DNA (CT-DNA). The interaction or binding of the multi-walled carbon nanotubes (MWCNT) was investigated in order to discover if it brings about any significant changes of the DNA double helix using CD spectra of the CT-DNA at two concentration levels of MWCNT representing an increasing MWCNT/DNA molar ratio. In addition, spectrophotometric titrations between MWCNT and CT-DNA were carried out in order to utilize spectral changes as a means of detecting specific binding modes of either intercalation or degradation of DNA. Interactions of MWCNT induced significant changes in the CD spectra of the B-form of natural DNA. The intensities of the positive CD band at 280 nm decreased significantly. This decrease was found to be concentration-dependent. Following spectrophotometric titrations; specific subtle conformational changes were observed with a molar ratio combination of 2:1 between MWCNT and CT-DNA and these were characterized by a formation constant of the order of 103 M-1 and a negative Gibbs free energy suggesting that MWCNT avidly binds to DNA. Thermodynamic considerations revealed that electrostatic interactions between the DNA base pairs and the MWCNT are taking place accounting for the negative free energy change, positive enthalpy change with a small entropy change. The results obtained in the study of the binding interactions of MWCNT with DNA confirm that a cytogenetic effect of MWCNT with DNA is a possibility in vivo.

Platinum–tin/carbon catalysts for ethanol oxidation: Influence of Sn content on the electroactivity and structural characteristics

Carbon-supported Pt–Sn catalysts commonly contain Pt–Sn alloy and/or Pt–Sn bimetallic systems (Sn oxides). Nevertheless, the origin of the promotion effect due to the presence of Sn in the Pt–Sn/C catalyst towards ethanol oxidation in acid media is still under debate and some contradictions. Herein, a series of Ptx–Sny/C catalysts with different atomic ratios are synthesized by a deposition process using formic acid as the reducing agent. Catalysts structure and chemical compositions are investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) and their relationship with catalytic behavior towards ethanol electro-oxidation was established. Geometric structural changes are producing by highest Sn content (Pt1–Sn1/C) promoted the interaction of Pt and Sn forming a solid solution of Pt–Sn alloy phase, whereas, the intermediate and lowest Sn content (Pt2–Sn1/C and Pt3–Sn1/C, respectively) promoted the electronic structure modifications of Pt by Sn addition without the formation of a solid solution. The amount of Sn added affects the physical and chemical characteristics of the bimetallic catalysts as well as reducing the amount of Pt in the catalyst composition and maintaining the electrocatalytic activities at the anode. However, the influence of the Sn oxidation state in Pt–Sn/C catalysts surfaces and the alloy formation between Pt and Sn as well as with the atomic ratio on their catalytic activity towards ethanol oxidation appears minimal. Similar methodologies applied for synthesis of Ptx–Sny/C catalysts with a small change show differences with the results obtained, thus highlighting the importance of the conditions of the preparation method.

Experimentally and coupled CFD/CSD assisted structural and aeroelastic modelling of the Javelin UAV outboard wing section

This study presents the procedure followed to make a prediction of the critical flutter speed for a composite UAV wing. At the beginning of the study, there was no information available on the materials used for the construction of the wing, and the wing internal structure was unknown. Ground vibration tests were performed in order to detect the structure’s natural frequencies and mode shapes. From tests, it was found that the wing possesses a high stiffness, presenting well separated first bending and torsional natural frequencies. Two finite element models were developed and matched to experimental results. It has been necessary to introduce some assumptions, due to the uncertainties regarding the structure. The matching process was based on natural frequencies’ sensitivity with respect to a change in the mechanical properties of the materials. Once experimental results were met, average material properties were also found. Aerodynamic coefficients for the wing were obtained by means of a CFD software. The same analysis was also conducted when the wing is deformed in its first four mode shapes. A first approximation for flutter critical speed was made with the classical V - g technique. Finally, wing’s aeroelastic behavior was simulated using a coupled CFD/CSD method, obtaining a more accurate flutter prediction. The CSD solver is based on the time integration of modal dynamic equations, requiring the extraction of mode shapes from the previously performed finite-element analysis. Results show that flutter onset is not a risk for the UAV, occurring at velocities well beyond its operative range.

Cognitive Competence and Life Course Change in Multi-Problem Adolescents

The dissertation reports on two studies. The purpose of Study I was to develop and evaluate a measure of cognitive competence (the Critical Problem Solving Skills Scale – Qualitative Extension) using Relational Data Analysis (RDA) with a multi-ethnic, adolescent sample. My study builds on previous work that has been conducted to provide evidence for the reliability and validity of the RDA framework in evaluating youth development programs (Kurtines et al., 2008). Inter-coder percent agreement among the TOC and TCC coders for each of the category levels was moderate to high, with a range of .76 to .94. The Fleiss’ kappa across all category levels was from substantial agreement to almost perfect agreement, with a range of .72 to .91. The correlation between the TOC and the TCC demonstrated medium to high correlation, with a range of r(40)=.68, p Study II reports an investigation of a positive youth development program using an Outcome Mediation Cascade (OMC) evaluation model, an integrated model for evaluating the empirical intersection between intervention and developmental processes. The Changing Lives Program (CLP) is a community supported positive youth development intervention implemented in a practice setting as a selective/indicated program for multi-ethnic, multi-problem at risk youth in urban alternative high schools in the Miami Dade County Public Schools (M-DCPS). The 259 participants for this study were drawn from the CLP’s archival data file. The study used a structural equation modeling approach to construct and evaluate the hypothesized model. Findings indicated that the hypothesized model fit the data (χ2 (7) = 5.651, p = .83; RMSEA = .00; CFI = 1.00; WRMR = .319). My study built on previous research using the OMC evaluation model (Eichas, 2010), and the findings are consistent with the hypothesis that in addition to having effects on targeted positive outcomes, PYD interventions are likely to have progressive cascading effects on untargeted problem outcomes that operate through effects on positive outcomes.

Hilbert Space Valued Signal Plus Noise Models: Analysis of Structural Breaks under High Dimensionality and Temporal Dependence

This thesis is concerned with change point analysis for time series, i.e. with detection of structural breaks in time-ordered, random data. This long-standing research field regained popularity over the last few years and is still undergoing, as statistical analysis in general, a transformation to high-dimensional problems. We focus on the fundamental »change in the mean« problem and provide extensions of the classical non-parametric Darling-Erdős-type cumulative sum (CUSUM) testing and estimation theory within highdimensional Hilbert space settings. In the first part we contribute to (long run) principal component based testing methods for Hilbert space valued time series under a rather broad (abrupt, epidemic, gradual, multiple) change setting and under dependence. For the dependence structure we consider either traditional m-dependence assumptions or more recently developed m-approximability conditions which cover, e.g., MA, AR and ARCH models. We derive Gumbel and Brownian bridge type approximations of the distribution of the test statistic under the null hypothesis of no change and consistency conditions under the alternative. A new formulation of the test statistic using projections on subspaces allows us to simplify the standard proof techniques and to weaken common assumptions on the covariance structure. Furthermore, we propose to adjust the principal components by an implicit estimation of a (possible) change direction. This approach adds flexibility to projection based methods, weakens typical technical conditions and provides better consistency properties under the alternative. In the second part we contribute to estimation methods for common changes in the means of panels of Hilbert space valued time series. We analyze weighted CUSUM estimates within a recently proposed »high-dimensional low sample size (HDLSS)« framework, where the sample size is fixed but the number of panels increases. We derive sharp conditions on »pointwise asymptotic accuracy« or »uniform asymptotic accuracy« of those estimates in terms of the weighting function. Particularly, we prove that a covariance-based correction of Darling-Erdős-type CUSUM estimates is required to guarantee uniform asymptotic accuracy under moderate dependence conditions within panels and that these conditions are fulfilled, e.g., by any MA(1) time series. As a counterexample we show that for AR(1) time series, close to the non-stationary case, the dependence is too strong and uniform asymptotic accuracy cannot be ensured. Finally, we conduct simulations to demonstrate that our results are practically applicable and that our methodological suggestions are advantageous.

Diachronic and Synchronic Indexing: Modeling Conceptual Change in Indexing Languages

This paper outlines a model of conceptual change in indexing languages. Findings from this modeling effort point to three ways meaning and relationships are established and then change in an indexing language. These ways: structural, terminological, and textual point to ways indexing language metadata can aid in managing conceptual change in indexing languages.