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Multivariate Design (multivariate + design)
Selected AbstractsSuitability for psychoanalytic psychotherapy: a reviewACTA PSYCHIATRICA SCANDINAVICA, Issue 3 2004K. Valbak Objective:, To review empirical studies on outpatients' pretherapy suitability for psychoanalytic psychotherapy. Method:, A literature search for studies in English was made in the databases MEDLINE, PsychInfo and EM-base. Forty-one studies spanning 20 years were selected for a thorough evaluation. Results:, Seventy-five per cent of the studies concerned brief dynamic psychotherapy. In general, application of single measures of suitability had a modest predictive value with correlations in the range of 0.17,0.73. There was no consistent difference between various formats of therapies. Most promising variables with the highest correlations with good outcome were: ,good quality of object relations', ,psychological mindedness' and ,motivation for change'. Some clinical guidelines can be drawn from quantitative research to provide the therapist with best method and format. Conclusion:, The importance of psychological variables known from the development of the brief dynamic therapies and earlier research was confirmed. Most correlations were modest and single factors could not be identified. Multivariate designs that combine different methods and formats with patient characteristics seem most promising in future predictor-outcome research. [source] Use of software to facilitate pharmaceutical formulation,experiences from a tablet formulationJOURNAL OF CHEMOMETRICS, Issue 3-4 2004Nils-Olof Lindberg Abstract This paper exemplifies the benefits of using experimental design together with software to facilitate the formulation of a tablet for specific purposes, from screening to robustness testing. By applying a multivariate design for the screening experiments, many excipients were evaluated in comparatively few experiments. The formulation work was generally based on designed experiments. Most of the experiments were fractional or full factorial designs, generated and evaluated in Modde with the centre point replicated. The robustness of the formulation was evaluated with experimental designs on two different occasions. Tested flavours were found to have limited influence on the important responses, which was key information in order to proceed with that particular composition. The formulation was also robust towards normal batch-to-batch variation of the excipients and the active pharmaceutical ingredient. A process step was investigated and, by applying experimental design and keeping in mind previous findings, important information could be gained from the study. The different studies yielded good and very useful models. Established relationships between design factors and responses provided information that was vital for the project. In cases of poor models, essential information regarding robustness was obtained. Copyright © 2004 John Wiley & Sons, Ltd. [source] Multivariate methods in pharmaceutical applicationsJOURNAL OF CHEMOMETRICS, Issue 3 2002Jon Gabrielsson Abstract This review covers material published within the field of pharmacy in the last five years. Articles concerning experimental design, optimization and applications of multivariate techniques have been published, from factorial designs to multivariate data analysis, and the combination of the two in multivariate design. The number of publications on this topic testifies to the good results obtained in the studies. Much of the published material highlights the usefulness of experimental design, with many articles dealing with optimization, where much effort is spent on getting useful results. Examples of multivariate data analysis are comparatively few, but these methods are gaining in use. The employment of multivariate techniques in different applications has been reviewed. The examples in this review represent just a few of the possible applications with different aims within pharmaceutical applications. A number of companies are using experimental design as a standard tool in preformulation and in combination with response surface modeling. The properties of e.g. a tablet can be optimized to fulfill a well-specified aim such as a specific release profile, hardness, disintegration time etc. However, none of the companies apply multivariate methods in all steps of the drug development process. As this is still very much a growing field, it is only a question of time before experimental design, optimization and multivariate data analysis are implemented throughout the entire formulation process, from performulation to multivariate process control. Copyright ©,2002 John Wiley & Sons, Ltd. [source] A Retrospective Examination of the Relationship Between Implementation Quality of the Coordinated School Health Program Model and School-Level Academic Indicators Over Time,JOURNAL OF SCHOOL HEALTH, Issue 3 2009Scott Rosas PhD ABSTRACT BACKGROUND:, Although models such as the coordinated school health program (CSHP) are widely available to address student health needs, school professionals have been unconvinced that scarce resources should be allocated to improving student health. Concern that attention may be diverted from meeting academic accountability goals is often seen as a reason not to attend to student health. Despite continuing calls for the study of multicomponent health programs in relation to educational achievement, the understanding of the extent to which adherence to the characteristics of CSHP contributes to or compromises academic outcomes over time remains incomplete. METHODS:, A retrospective study was conducted of CSHP implementation across 158 public schools in Delaware, serving grades K-12. Using a doubly multivariate design, this study examined 3 levels of CSHP implementation across 5 school-level academic indicators for 3 years. Indicators included school performance, school progress, and aggregated student performance in 3 content areas,reading, mathematics, and writing. Data for the years prior to, during, and following implementation of CSHP were analyzed. RESULTS:, Multivariate main effects of year by implementation level were detected. CSHP schools with high levels of implementation had better school-level performance and progress ratings. CSHP implementation did not have an effect on reading, math, and writing indicators, though all groups showed significant improvements over time in these areas. CONCLUSIONS:, Results of this study suggest that quality implementation of CSHP does not adversely impact school-level academic indicators over time. Moreover, findings suggest a better fit with school-wide accountability indicators than with specific content-based achievement indicators. [source] | ||||||||||