Postoperative complications and their association with post-traumatic stress disorder in academic vascular surgeons
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Keywords:
Univariate analysis
Demographics
Univariate
Traumatic stress
Vascular surgery
I have been promising (or threatening, depending on your point of view) for some time to write on the subject of SEG's demographics and now is the time. One could say that it is SEG's most important issue because our demographic profile says a lot about our society and deeply influences our future. Yet, I have been slow to bring the issue to the fore because I have been puzzled by some aspects of our demographics and also needed to collect some hard data. I think you may find the results interesting.
Demographics
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We examined the comparative behavior of subject-specific multivariate and univariate reference regions, using both computer-generated data and serial (semi-annual) measurements of selected analytes in subjects from a large health-maintenance program. Univariate studies under both homeostatic and random-walk time-series models were helpful in defining expected results, but only the homeostatic model was used in multivariate as well as univariate forms. Analysis of the computer-generated data and the real biochemical series produced similar findings, which showed the multivariate subject-specific reference region to be much more conservative than corresponding univariate intervals. That is, a multidimensional point of p correlated observations is quite likely to lie within the individual's multivariate reference region (based on past observation vectors), even when one or more of the observations lie outside their separate reference intervals for that individual. One consequence of this high specificity against univariate false positives in a large surveillance program is a higher than expected proportion of positive multivariate vectors in which none of the values lie outside their univariate ranges. Thus, although the development of multivariate reference regions should be encouraged, they should be used in conjunction with, not instead of, univariate ranges.
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Univariate analysis
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Problems of conventional (univariate) rainfall statistics are discussed. A multivariate approach is presented. It takes into account separation of rainfall events as well as the description of intensity sequences. More theoretical support as well as methodological simplification seem necessary.
Univariate
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Forecasting methods are reviewed. They may be classified into univariate, multivariate and judgemental methods, and also by whether an automatic or non-automatic approach is adopted. The choice of 'best' method depends on a wide variety of considerations. The use of forecasting competitions to compare the accuracy of univariate methods is discussed. The strengths and weaknesses of different univariate methods are compared, both in automatic and non-automatic mode. Some general recommendations are made as well as some suggestions for future research.
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Strengths and weaknesses
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Univariate
Univariate analysis
Value (mathematics)
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This paper examines the impact that demographics have on policy outcomes. The impact that aldermanic ward‐level demographics have on the number of liquor licenses is measured in two US cities. In one city there is a great deal of direct resident involvement in the issuance process, while in the other city, issuance decisions are handled by elected representatives. This research does find that demographics have a significant impact on policy outcomes. However, the paper does not find a significant difference in outcomes between decisions made by elected representatives and those made by the community.
Demographics
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The delta check methods are methods for detection of random errors in clinical laboratory tests including specimen abnormalities, specimen mix-up, problems in analysis processes, and clerical errors. Methodologically, it is known that the multivariate delta check methods are more superior to the univariate delta check methods. However, due to some problems in reality including technical difficulties, it is hard to put the multivariate delta check methods into practice. Since the univariate delta check methods are methods at hand, there has been a need for an efficient and effective univariate delta check method. In order to meet such a need, we propose "the multi-item univariate delta check (MIUDC) method". By the multi-item univariate delta check (MIUDC) method, we mean a method in which univariate delta checks are performed on multiple items and specimens with the positive univariate delta check in at least k items are put under a detailed investigation. Our research objectives are the determination of an appropriate value of such k and identification of test items deserving of more interest. Through real data and simulation studies, we concluded that an appropriate value of k is 4 because, with k = 4, we can have light checking-out volumes and high efficiency. Also, we identified total cholesterol, albumin, and total protein as items deserving of more interest because the false positive rate associated with them in the MIUDC was zero in a simulation study. We present the MIUDC method as a quality control method that is easy-to-implement and efficient.
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Univariate analysis
Identification
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Univariate
Univariate analysis
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This work compares the performances of univariate and multivariate time series models. Five time series variables from Nigeria’s gross domestic products were used for the comparative study. These series were modelled using both the univariate and multivariate time series framework. The performances of the two methods were evaluated based on the mean error incurred by each approach. The results showed that the univariate linear stationary models perform better than the multivariate models.
Univariate
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The results from 510 consecutive routine determinations of free thyroxine index, free triiodothyronine index and thyrotropin were evaluated using both a trivariate reference region and the combined three univariate reference intervals. The results from 109 patients were discordant when evaluated by both the trivariate and the triple univariate reference regions. In 108 of these subjects the hormone results were found to be abnormal by the triple univariate evaluation method but normal when the trivariate reference region was used. The latter evaluation was in accordance with the clinical findings of the patients, who were euthyroid as evaluated from the 105 medical records we could trace. In one subject, clinically euthyroid, trivariate evaluation misclassified the patient as abnormal in contrast to the classical univariate evaluation. We conclude that the trivariate evaluation method was in better agreement with the clinical diagnosis of the patients and should be used in the routine evaluation of trivariate data in order to diminish the number of false abnormal results.
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Univariate analysis
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