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Measurement and analysis of health related quality of life and environmental data
The definition (or construction) of variables and indicators, and the analysis of the evolution of their joint distribution between various populations, times and areas are generally two different, well separated steps of the work for a statistician in the field of environment and/or health related quality of life.
The first step generally deals with calibration and metrology of variables. Key words are measurement or scoring, depending on the area of application. Most of the time, the statistical methods used are exploratory. The kinds of models specified are generally structural: classical factorial analysis models or modern item response theory models. The second step is certainly more known by most inferential statisticians. Linear, generalized linear, time series and survival models are very useful models in this step, where the variables constructed in the first step are incorporated and their joint distribution with the other analysis variables (treatment group, time, duration of life, etc.) is investigated.
In this article, the simple strategy of separating the two steps is compared with the global strategy of defining and analysing a global model, including both the measurement and the analysis step. Copyright © 2004 John Wiley & Sons, Ltd.
Measurement and analysis of health related quality of life and environmental data
The definition (or construction) of variables and indicators, and the analysis of the evolution of their joint distribution between various populations, times and areas are generally two different, well separated steps of the work for a statistician in the field of environment and/or health related quality of life.
The first step generally deals with calibration and metrology of variables. Key words are measurement or scoring, depending on the area of application. Most of the time, the statistical methods used are exploratory. The kinds of models specified are generally structural: classical factorial analysis models or modern item response theory models. The second step is certainly more known by most inferential statisticians. Linear, generalized linear, time series and survival models are very useful models in this step, where the variables constructed in the first step are incorporated and their joint distribution with the other analysis variables (treatment group, time, duration of life, etc.) is investigated.
In this article, the simple strategy of separating the two steps is compared with the global strategy of defining and analysing a global model, including both the measurement and the analysis step. Copyright © 2004 John Wiley & Sons, Ltd.
Measurement and analysis of health related quality of life and environmental data
Mesbah, Mounir (author)
Environmetrics ; 15 ; 471-481
2004-08-01
11 pages
Article (Journal)
Electronic Resource
English
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