Application of the Generalized Propensity Score. Evaluation of public contributions to Piedmont enterprises.

In this article, we apply a generalization of the propensity score of Rosenbaum and Rubin (1983b). Techniques based on the propensity score have long been used for causal inference in observational studies for reducing bias caused by non-random treatment assignment. In last years, Joffe and Rosenbaum (1989) and Imbens and Hirano (2000) suggested two possible extensions to standard propensity score for ordinal and categorical treatments respectively. Propensity score techniques, allowing for continuous treatments effect evaluation, were, instead, recently proposed by Van Dick Imai (2003) and Imbens and Hirano (2004). We refer to Imbens' approach for the use of the generalized propensity score, to widen its application for continuous treatment regimes.

POLYMORPH FORMATION OF TOLFENAMIC ACID: AN INVESTIGATION OF PRE-NUCLEATION ASSOCIATION

The majority of pharmaceutical products are formulated as solids in the crystalline state. With the potential to exist in different crystalline modifications or polymorphs, each solid form bears its own physical and chemical properties, influencing directly bioavailability and manufacturability of the final dosage form. In view of the importance of crystalline form selection in the drug development process, it is imperative for pharmaceutical scientists to work arduously on various aspects of polymorphism, ranging from fundamental understanding of the phenomenon at the molecular level to practical utilization of a specific crystalline form. One common feature of organic crystals is the existence of distinct molecular conformations in different polymorphic structures, known as conformational polymorphism. Conformational polymorphs are routinely observed in drug development, produced when crystal growth conditions vary. Crystallization from solution involves nucleation and crystal growth, the mechanisms that influence the polymorphic outcome. The embryonic solute aggregate has been recognized to play a critical role in dictating the final crystal structure, and solution conditions are also known to drastically influence the self-association behavior of solute molecules during crystallization, affecting crystal packing of organic molecules. For the crystal growth of conformational polymorphs, changes in molecular conformation not only determine the growth kinetics, but also influence the nature and strength of interactions present in the crystal structures. How conformation and intermolecular interaction affect each other underlines the intricacy and the wonder of crystal growth of the organic. Thus, the overall goal of this research is to provide the fundamental understanding of the extent to which solution conditions influence the molecular conformation in the solid-state of a model drug, tolfenamic acid. By combining experimental studies with advanced computational tools, this dissertation offers novel insights into solution species during pre-nucleation and molecular packing of conformational polymorphs of tolfenamic acid. In-depth understanding of the underlying connection between molecular conformation and crystal packing will help advance the knowledge required for rational control of crystal growth

Nonparametric Estimators of Dose-Response Functions

We propose two semiparametric estimators of the dose-response function based on spline techniques. Under uncounfoundedness, the generalized propensity score can be used to estimate dose-response functions (DRF) and marginal treatment effect functions. In many observational studies treatment may not be binary or categorical. In such cases, one may be interested in estimating the dose-response function in a setting with a continuous treatment. We evaluate the performance of the proposed estimators using Monte Carlo simulation methods. The simulation results suggested that the estimated DRF is robust to the specific semiparametric estimator used, while the parametric estimates of the DRF were sensitive to model mis-specification. We apply our approach to the problem of evaluating the effect on innovation sales of Research and Development (R&D) financial aids received by Luxembourgish firms in 2004 and 2005.Continuous treatment; Dose-response function; Generalized Propensity Score; Non-parametric methods; R&D investment