Global assessment variables

Global assessment variables involve an investigator s overall impression of improvement or benefit. Usually this is done in terms of an ordinal scale of categories. While the guidelines allow such variables, experience shows that they 

ICH E9 (1998) Note for Guidance on Statistical Principles for Clinical Trials  

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There may also be circumstances where a categorisation combines responses measured, on different domains of action for example to give a single dichotomous responder/non-responder outcome. There are connections here with global assessment variables. This approach is taken in Alzheimer s disease where the effect of treatment is ultimately expressed in terms of the proportion of patients who achieve a meaningful benefit (response) see the CPMP (1997) Note for Guidance on Medicinal Products in the Treatment of Alzheimer s Disease . In oncology. 

Mode - a global assessment variable for the response to an anti-inflammatory drug... 

Table 2.3 Individual changes in a global assessment variable following treatment with an anti-inflammatory (61 patients)...

If objective variables are considered by the investigator when making a global assessment, then those objective variables should be considered as additional primary, or at least important secondary, variables. ... 

This chapter summarizes climate projections presented by the recent Fourth Assessment Report (AR4) of the IPCC, both for the global scale and for the so-called European and Mediterranean region. Besides the IPCC projections, other results regarding the Mediterranean area will be reviewed as well. The emphasis will be mostly placed on temperature and precipitation projections, while other variables, such as wind, solar radiation, or sea level, will not be commented in depth. Some attention will be paid to methodologies used to develop projections, and the corresponding uncertainties will be commented. In general, projections will be given for the end of the current century. 

The inventory results should be presented in clear form, how much and what substances from the environment enter the system and how much get out. These results serve for subsequent life cycle impact assessment [48], The aim of the life cycle impact assessment is to measurably compare the environmental impacts of product systems and to compare their severity with new quantifiable variables identified as impact category. The impact categories are areas of specific environmental problems such as global warming, climate changes, acidification, eutrophication, ecotoxicity and others. Already in the phase of definition of the LCA study scope, it is necessary to describe what impact category will be applied and which of their environmental mechanisms will serve as a basis for impact assessment [46],... 

National Assessment Synthesis Team, Climate Change Impacts on the United States The Potential Consequences of Climate Variability and Change (Washington, D.C. U.S. Global Change Research Program, 2000). 

To assess and quantify the impacts of past and future climatic variability and change on components of the cryosphere and their consequences, particularly for global energy and water budgets, frozen ground conditions, sea level change, and the maintenance of polar sea ice covers. 

The progress achieved in studying Arctic environment variability is due to the accomplishment of a number of international research programs. Of particular importance is the Arctic Climate System Study (ACSYS) project set up in 1991 by the WCRP as a practicable program for the next decade to assess the role of the Arctic in the global climate. Five areas were emphasized ... 

Analysis of the shape of error surfaces. To conclude this section, we consider a more quantitative approach to error estimation. The first step is to estimate the accuracy of the individual data points this can either be done by analysis of the variability of replicate measurements, or from the variation of the fitted result. From that, one can assess the shape of the error surface in the region of the minimum. The procedure is straightforward the square root of the error, defined as the SSD, is taken as a measure of the quality of the fit. A maximum allowed error is defined which depends on the reliability of the individual points, for example, 30% more than with the best fit, if the points are scattered by about 30%. Then each variable (not the SSD as before) is minimised and also maximised. A further condition is imposed that the sum of errors squared (SSD) should not increase by more than the fraction defined above. This method allows good estimates to be made of the different accuracy of the component variables, and also enables accuracy to be estimated reliably even in complex analyses. Finally, it reveals whether parameters are correlated. This is an important matter since it happens often, and in some extreme cases where parameters are tightly correlated it leads to situations where individual constants are effectively not defined at all, merely their products or quotients. Correlations can also occur between global and local parameters. 

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