986 resultados para Applied Behavioral Analysis


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Mode of access: Internet.

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Spine title: Porter's analysis.

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Mode of access: Internet.

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Mode of access: Internet.

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Literature cited: p. 62-63.

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Background: The aim of this study was to determine the effects of carvedilol on the costs related to the treatment of severe chronic heart failure (CHF). Methods: Costs for the treatment for heart failure within the National Health Service (NHS) in the United Kingdom (UK) were applied to resource utilisation data prospectively collected in all patients randomized into the Carvedilol Prospective Randomized Cumulative Survival (COPERNICUS) Study. Unit-specific, per them (hospital bed day) costs were used to calculate expenditures due to hospitalizations. We also included costs of carvedilol treatment, general practitioner surgery/office visits, hospital out-patient clinic visits and nursing home care based on estimates derived from validated patterns of clinical practice in the UK. Results: The estimated cost of carvedilol therapy and related ambulatory care for the 1156 patients assigned to active treatment was 530,771 pound (44.89 pound per patient/month of follow-up). However, patients assigned to carvedilol were hospitalised less often and accumulated fewer and less expensive days of admission. Consequently, the total estimated cost of hospital care was 3.49 pound million in the carvedilol group compared with 4.24 pound million for the 1133 patients in the placebo arm. The cost of post-discharge care was also less in the carvedilol than in the placebo group (479,200 pound vs. 548,300) pound. Overall, the cost per patient treated in the carvedilol group was 3948 pound compared to 4279 pound in the placebo group. This equated to a cost of 385.98 pound vs. 434.18 pound, respectively, per patient/month of follow-up: an 11.1% reduction in health care costs in favour of carvedilol. Conclusions: These findings suggest that not only can carvedilol treatment increase survival and reduce hospital admissions in patients with severe CHF but that it can also cut costs in the process.

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Principal components analysis (PCA) has been described for over 50 years; however, it is rarely applied to the analysis of epidemiological data. In this study PCA was critically appraised in its ability to reveal relationships between pulsed-field gel electrophoresis (PFGE) profiles of methicillin- resistant Staphylococcus aureus (MRSA) in comparison to the more commonly employed cluster analysis and representation by dendrograms. The PFGE type following SmaI chromosomal digest was determined for 44 multidrug-resistant hospital-acquired methicillin-resistant S. aureus (MR-HA-MRSA) isolates, two multidrug-resistant community-acquired MRSA (MR-CA-MRSA), 50 hospital-acquired MRSA (HA-MRSA) isolates (from the University Hospital Birmingham, NHS Trust, UK) and 34 community-acquired MRSA (CA-MRSA) isolates (from general practitioners in Birmingham, UK). Strain relatedness was determined using Dice band-matching with UPGMA clustering and PCA. The results indicated that PCA revealed relationships between MRSA strains, which were more strongly correlated with known epidemiology, most likely because, unlike cluster analysis, PCA does not have the constraint of generating a hierarchic classification. In addition, PCA provides the opportunity for further analysis to identify key polymorphic bands within complex genotypic profiles, which is not always possible with dendrograms. Here we provide a detailed description of a PCA method for the analysis of PFGE profiles to complement further the epidemiological study of infectious disease. © 2005 Elsevier B.V. All rights reserved.

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Experiments combining different groups or factors and which use ANOVA are a powerful method of investigation in applied microbiology. ANOVA enables not only the effect of individual factors to be estimated but also their interactions; information which cannot be obtained readily when factors are investigated separately. In addition, combining different treatments or factors in a single experiment is more efficient and often reduces the number of replications required to estimate treatment effects adequately. Because of the treatment combinations used in a factorial experiment, the DF of the error term in the ANOVA is a more important indicator of the ‘power’ of the experiment than the number of replicates. A good method is to ensure, where possible, that sufficient replication is present to achieve 15 DF for each error term of the ANOVA. Finally, it is important to consider the design of the experiment because this determines the appropriate ANOVA to use. Some of the most common experimental designs used in the biosciences and their relevant ANOVAs are discussed by. If there is doubt about which ANOVA to use, the researcher should seek advice from a statistician with experience of research in applied microbiology.