<p>Quantum chemistry methods nowadays reach its maturity with various robust ground state correlation methods. However, many problems related to response do not have satisfactory solutions. Chemical reactivity indexes are some static response to external fields and number of particle change. These chemical reactivity indexes have important chemical significance, while not all of them had analytical expressions for direct evaluations. By solving coupled perturbed self-consistent field equations, analytical expressions were obtained and verified numerically. In the particle-particle (pp) channel, the response to the pairing field can describe <italic>N&plusmn;2</italic> excitations, i.e. double ionization potentials and double electron affinities. The linear response time-dependent density-functional theory (DFT) with pairing fields is the response theory in the density-functional theory (DFT) framework to describe $N\pm 2$ excitations. Both adiabatic and dynamic kernels can be included in this response theory. The correlation energy based on this response, the correlation energy of the particle-particle random phase approximation (pp-RPA), can also be proved equivalent to the ladder approximation of the well-established coupled-cluster doubles. These connections between the response theory, <italic>ab initio</italic> methods, and Green's function theory would be beneficial for further development. Based on RPA and pp-RPA, the theory of second RPA and the second pp-RPA with restrictions can be used to capture single and double excitations efficiently. We also present a novel methods, variational fractional spin DFT, to calculate singlet-triplet energy gaps for diradicals, which are usually calculated through spin-flip response theories.</p> / Dissertation

The Neuropsychological Symptom Inventory (NSI; Rattan, Dean, & Rattan, 1989), a self report measure of psychiatric and neurological symptoms, was revised to be presented in an electronic format. This revised instrument, the Neuropsychological Symptom Scale (Dean, 2010), was administered to 1,141 adult volunteers from a medium-sized Midwestern university. The collected data was subjected to exploratory factor analysis which suggested three primary factors related to emotional, cognitive, and somatosensory functioning. The items on the NSS were then organized into three subscales reflecting these areas of functioning. A fourth experimental subscale was also created to facilitate the collection of data on items that did not load on any of the three primary subscales. Item Response Theory (IRT) analysis and Classical Test Theory (CTT) approaches were then applied and compared as means of developing standard scores on the three primary subscales of the NSS. The results of these analyses are provided along with recommendations related to the further development of the NSS as an assessment tool. / Department of Educational Psychology

With societies aging all around the world, the global burden of degenerative diseases is expected to increase exponentially. From the perspective drug development, degenerative diseases represent an especially challenging class. Clinical trials, in this context often termed disease progression studies, are long, costly, require many individuals, and have low success rates. Therefore, it is crucial to use informative study designs and to analyze efficiently the obtained trial data. The development of novel approaches intended towards facilitating both the design and the analysis of disease progression studies was the aim of this thesis. This aim was pursued in three stages (i) the characterization and extension of pharmacometric software, (ii) the development of new methodology around statistical power, and (iii) the demonstration of application benefits. The optimal design software PopED was extended to simplify the application of optimal design methodology when planning a disease progression study. The performance of non-linear mixed effect estimation algorithms for trial data analysis was evaluated in terms of bias, precision, robustness with respect to initial estimates, and runtime. A novel statistic allowing for explicit optimization of study design for statistical power was derived and found to perform superior to existing methods. Monte-Carlo power studies were accelerated through application of parametric power estimation, delivering full power versus sample size curves from a few hundred Monte-Carlo samples. Optimal design and an explicit optimization for statistical power were applied to the planning of a study in Alzheimer's disease, resulting in a 30% smaller study size when targeting 80% power. The analysis of ADAS-cog score data was improved through application of item response theory, yielding a more exact description of the assessment score, an increased statistical power and an enhanced insight in the assessment properties. In conclusion, this thesis presents novel pharmacometric methods that can help addressing the challenges of designing and planning disease progression studies.

国立情報学研究所で電子化したコンテンツを使用している。

国立情報学研究所で電子化したコンテンツを使用している。

国立情報学研究所で電子化したコンテンツを使用している。

国立情報学研究所で電子化したコンテンツを使用している。

国立情報学研究所で電子化したコンテンツを使用している。

国立情報学研究所で電子化したコンテンツを使用している。

国立情報学研究所で電子化したコンテンツを使用している。