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  • 1990-1994  (4)
  • 1965-1969
  • Chemical Engineering  (2)
  • Osteoporosis  (2)
  • 1
    Electronic Resource
    Electronic Resource
    Evaluation of adverse health outcomes associated with vertebral fractures (1991)
    Springer
    Osteoporosis international 1 (1991), S. 134-140 
    Details
    ISSN: 1433-2965
    Keywords: Osteoporosis ; Pain and disability ; Psychosocial problems ; Risk analysis ; Statistical methods ; Vertebral fractures
    Source: Springer Online Journal Archives 1860-2000
    Topics: Medicine
    Notes: Abstract Little is known about the frequency or degree to which vertebral fractures cause pain and physical disability. The purpose of this investigation was to examine the advantages of risk analysis over other statistical techniques (e.g., correlation analysis) for quantifying relationships between vertebral fractures and outcomes such as pain and disability. Subjects who volunteered to participate in studies of osteoporosis were asked about pain and disability. The number and degree of vertebral deformities were assessed from radiographs. Strong associations were observed between the most severe vertebral deformities and the risk of high pain or disability scores, while weaker associations were observed for moderate deformities. There did not appear to be any association between vertebral deformity and risk of moderate levels of pain or disability. Because of the potential for bias in cross-sectional studies such as this, the magnitude of these findings must be considered tentative. We conclude that risk analysis is an appropriate method for quantifying the relationship of vertebral fractures with pain and disability, but that prospective studies are now needed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01625442
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    When should bone density measurements be repeated? (1994)
    Springer
    Calcified tissue international 55 (1994), S. 243-248 
    Details
    ISSN: 1432-0827
    Keywords: Osteoporosis ; Bone density ; Longitudinal studies ; Statistical models ; Decision models
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Medicine , Physics
    Notes: Abstract We calculated how long to wait before repeating bone mineral density (BMD) measurements to reassess fracture risk. Correlation results from serial measurements of 495 postmenopausal Japanese-American women were used to estimate 95% confidence intervals (CI) for future BMD. After 7 years of follow-up, BMD correlations with the initial measurement ranged between 0.81 and 0.94, depending on age group and measurement site. In this analysis, the period between measurements was defined as the time required for the lower 95% CI to fall below the BMD value corresponding to doubling of fracture risk. Progressive bone loss causes fracture risk to double after 10 years, on average. However, the 95% CIs indicate that a second BMD measurement will detect risk doubling after only 2 or 3 years for some women. For untreated, early postmenopausal women, the period between measurements was approximately 2–5 years for the radius and 4–6 years for the calcaneus, depending on the initial BMD level. The period was approximately 1 year longer for women age 60 and older. Treatments that halve the bone loss rate would increase the period by 1–3 years. In the absence of a second measurement of BMD, the CI will continue to expand with time, corresponding to a wider range in risk between individuals, and a greater proportion of women will be at increased fracture risk. Obtaining a second BMD measurement pinpoints the patient's status within the precision of the measurement. We conclude that repeated BMD measurements will provide a more accurate estimate of fracture risk than a single, baseline measurement.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00310399
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    Gross error detection when variance-covariance matrices are unknown (1993)
    Hoboken, NJ : Wiley-Blackwell
    AIChE Journal 39 (1993), S. 1335-1341 
    Details
    ISSN: 0001-1541
    Keywords: Chemistry ; Chemical Engineering
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: Equations introduced here identify measurement biases and process leaks, when gross errors exist in measured process variables and the variance-covariance matrix of the measurements, Σ, is unknown. Σ is estimated by the sample variance, S, using process data.For an unknown Σ, the global test statistic is the well-known Hotelling T2 statistic. Its power function has a noncentral F-distribution. For component tests used for specific identification of measurement biases and nodal leaks, two tests are presented with Σ unknown. The first test is independent of the number of component tests, k, and is given by a statistic with an F-distribution. The second test depends on k and has a student t-distribution. The power functions for both component tests are provided. Process examples and a Monte Carlo simulation study presented demonstrate the use and performance of these statistical equations in identifying biases and process leaks.
    Additional Material: 1 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/aic.690390810
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    Paper (German National Licenses)
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  • 4
    Electronic Resource
    Electronic Resource
    Unbiased estimation of gross errors in process measurements (1992)
    Hoboken, NJ : Wiley-Blackwell
    AIChE Journal 38 (1992), S. 563-572 
    Details
    ISSN: 0001-1541
    Keywords: Chemistry ; Chemical Engineering
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: A new approach to gross error detection provides unbiased estimates and 100(1-α)% simultaneous confidence intervals of process variables when biased process measurements and process leaks exist. Presented in this article are estimation equations for process variables, as well as equations that help identify biased measurements and process leaks. These equations include the power function for a global test, and two types of α-level component tests and their power functions. Important strengths and weaknesses of this approach are compared to those of the serial compensation strategy, in particular, by varying the significance level (α), the variance-covariance matrix (Σ), the size of measurement bias (δ), the number of biased variables, and the sample size (N). Accuracy of δ estimation and performance in detecting the presence of process leaks (γ) are also evaluated and compared. The proposed approach has unique features that can provide a basis for improving the reconciliation of variables in process operations.
    Additional Material: 1 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/aic.690380410
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
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