A simple model to predict coronary disease in patients undergoing operation for mitral regurgitation
Eric LimZiad A. AliClifford W. BarlowChristopher JacksonAmir‐Reza HosseinpourJames C. HalsteadJohn B. BarlowFrancis C. Wells
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Stepwise regression
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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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Discharged medical patients are at risk for venous thromboembolism (VTE). It is difficult to identify which discharged patients would benefit from extended duration thromboprophylaxis. The Intermountain Risk Score is a prediction score derived from discrete components of the complete blood cell count and basic metabolic panel and is highly predictive of 1-year mortality. We sought to ascertain if the Intermountain Risk Score might also be predictive of 90-day postdischarge hospital-associated VTE (HA-VTE).
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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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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
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In univariate and in multivariate analyses, it is difficult to identify outliers in the case of skewed or heavy-tailed distributions. In this article, we propose simple univariate and multivariate outlier identification procedures that perform well with these types of distributions while keeping the computational complexity low. We describe the commands gboxplot (univariate case) and sdasym (multivariate case), which implement these procedures in Stata.
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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.
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