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PubMed1993

Dose-response relationship between total cadmium intake and metallothioneinuria using logistic regression analysis.

The dose-response relationship for environmental cadmium exposure was assessed using logistic regression analysis. The prevalence of metallothioneinuria was employed as a response variable, while age and total cadmium intake, calculated from the average cadmium concentration in rice and duration of residence in the cadmium-polluted area, were used as explanatory variables. The target population comprised of 1843 cadmium-exposed and 240 non-exposed inhabitants of Ishikawa, Japan. The individuals were divided into 96 subgroups by sex, age (4 categories), cadmium concentrations in rice (3 categories) and length of residence in the polluted area (4 categories). Only total cadmium intake had a significant association with the prevalence of metallothioneinuria. In the non-exposed subjects total cadmium intakes corresponding to 2.5% prevalence of metallothioneinuria were calculated. Based on metallothionein levels expressed as either microgram/l urine or microgram/g creatinine, the total intakes were: 2.221 or 2.207 g in men and 2.365 or 0.319 g in women, respectively. Most of these values were similar to those reported by us previously, employing simple regression analysis. It is concluded, therefore, that a maximum allowable intake of about 2 g cadmium is a reasonable estimate for preventing the cadmium-induced renal dysfunction.

Age Factors↗
PubMed2003

Accuracy of contrast-enhanced MR angiography in predicting angiographic stenosis of the internal carotid artery: linear regression analysis.

BACKGROUND AND PURPOSE: We sought to assess whether contrast-enhanced MR angiography is able to predict the degree of angiographic stenosis of the internal carotid artery within a clinically acceptable margin of error, thereby decreasing the need for angiography. In addition, we sought to assess whether adding ultrasound peak systolic velocity (PSV) as an additional regressor improves the accuracy of prediction. METHODS: A retrospective review of our institution's records for a 4-year period was conducted to identify all patients who had undergone evaluation of their carotid arteries using digital subtraction angiography, contrast-enhanced MR angiography, and ultrasonography. All internal carotid artery stenoses ranging from 10% to 90% at carotid angiography were selected (n = 22). Measurements were then obtained based on the North American Symptomatic Carotid Endarterectomy Trial style by using the digital subtraction angiograms and contrast-enhanced MR angiograms in a blinded fashion. The correlation between digital subtraction angiography data and contrast-enhanced MR angiography data was assessed by conducting linear regression analysis. Multiple regression analysis was then conducted to determine whether the inclusion of ultrasound PSV as an additional regressor increased the accuracy of prediction. RESULTS: The correlation between the degree of stenosis measured by digital subtraction angiography and that measured by contrast-enhanced MR angiography was r = 0.967. The 95% confidence interval for the line of means showed low errors bounds, ranging as low as +/-2.8%. The 95% confidence interval for individual prediction of angiographic stenosis based on a given contrast-enhanced MR angiographic measurement, however, was significantly larger, being no less than +/-13.6%. With the inclusion of PSV, the adjusted correlation was r = 0.965. CONCLUSION: A clear linear relationship exists between digital subtraction angiographic and contrast-enhanced MR angiographic measurements of carotid stenosis. Increasing severity of stenosis as measured by contrast-enhanced MR angiography corresponds to increasing severity at angiography. Although the predictive value of contrast-enhanced MR angiography is excellent in the mean, it is less reliable for predicting the degree of angiographic stenosis in the individual patient, showing rather wide confidence intervals. Furthermore, the inclusion of PSV as an additional regressor does not improve the predictive accuracy beyond that of contrast-enhanced MR angiography alone.

Angiography, Digital Subtraction↗
PubMed2004

Perforator vein incompetence in chronic venous disease: a multivariate regression analysis model.

OBJECTIVES: In the presence of superficial and deep vein insufficiency the effects, if any, of concurrent incompetent perforator veins (IPVs) on clinical status are masked. On the basis of multivariate regression analysis, this study examines the significance of perforator vein incompetence across the clinical classes of CEAP (C-class CEAP ) in relation to the superficial and deep systems, and assesses the role of factors implicated in the presence and number of IPVs in chronic venous disease (CVD). METHODS: The study included 525 limbs in 360 patients, ages 17 to 96 years, referred for investigation of CVD. The protocol entailed history taking, physical examination, and duplex scanning (reflux > 0.5 s), with emphasis on IPVs. Exclusion criteria included peripheral vascular disease, unrelated edema, severe chronic obstructive pulmonary disease, and recent (< 1 year) deep vein thrombosis (DVT). RESULTS: Limbs were stratified as C 0 , 84; C 1 , 25; C 2 , 231; C 3 , 66; C 4 , 48; C 5 , 23; and C 6 , 48. C-class CEAP was separately regressed with age ( P < .001), sex ( P < .25), contralateral CVD ( P < .2), CVD recurrence ( P = .022), previous DVT ( P < .001), superficial vein reflux ( P < .001); deep vein reflux ( P < .001), perforator vein reflux ( P < .001), and number of IPVs ( P < .001). In an optimized multivariate regression analysis of C class CEAP with all significant variables combined, age ( P < .001), previous DVT ( P = .017), superficial vein reflux ( P < .001), deep vein reflux ( P < .001), and number of IPVs ( P = .008) emerged as predictors of CVD severity (CEAP), based on the equation C class CEAP = -0.2807 + 0.028013 Age + 0.58530 Previous DVT + 0.3450 Superficial vein reflux + 0.17781 Deep Reflux + 0.14537 IPVs ( R 2 = 37.4%; P < .001). Perforator incompetence was predicted by superficial vein reflux ( P < .001) and deep vein reflux ( P = .044), age ( P = .019), CVD recurrence ( P = .038), and sex ( P = .018), as follows: Perforator incompetence = -0.2532 + 0.006457 Age + 0.41366 Superficial reflux + 0.06766 Deep reflux + 0.2450 CVD recurrence - 0.21310 Sex ( R 2 = 33.3%; P < .001). Number of IPVs per limb was best associated with superficial reflux ( P < .001) and deep reflux ( P = .023), linked as IPVs = - 0.11789 + 0.41323 Superficial reflux + 0.07646 Deep reflux ( R 2 = 26.1%; P < .001). CONCLUSION: Perforator incompetence proved to be a significant factor for determination of CVD severity according to C-class CEAP , withstanding the conspicuous confounding effects of the superficial and deep venous systems. Perforator incompetence was significantly linked to aging, superficial or deep vein incompetence, recurrence of superficial disease, and sex, whereas the IPV number, regardless of location, depended on the presence of superficial or deep venous reflux.

Adolescent↗
PubMed1996

Regression analysis and multivariate analysis.

Proper evaluation of data does not necessarily require the use of advanced statistical methods; however, such advanced tools offer the researcher the freedom to evaluate more complex hypotheses. This overview of regression analysis and multivariate statistics describes general concepts. Basic definitions and conventions are reviewed. The types of regression analysis are then discussed, including simple regression, multiple regression, multivariate multiple regression, and logistic regression. The various steps required to perform these analyses are described, and the advantages and disadvantages of each is detailed.

Analysis of Variance↗
PubMed1976

The significance for breeding of linear regression analysis of genotype-environment interactions.

Methods of regression analysis of genotype-environment interaction are considered in relation to existing theory dealing with the relative efficiencies of selection for general or specific adaptation to the environment, and the choice of environments for assessment. The two alternative models is involving regression on to environmental effects (model 2) or genotypic effects (model 3) are equivalent when regression lines are concurrent, but are shown to be mutually exclusive when concurrence is absent...

Crosses, Genetic↗
PubMed1986

Multiple regression analysis of twin data obtained from selected samples.

The multiple regression analysis of twin data in which a cotwin's score is predicted from that of a proband (the member of a twin pair selected because of a deviant score) and the coefficient of relationship provides a powerful test of genetic etiology (DeFries and Fulker: Behav Genet 15:467-473, 1985). Moreover, when an augmented model containing an interaction term is fitted to the same data set, direct estimates of heritability (h2) and the proportion of variance owing to shared environmental influences (c2) are also obtained. In the present paper, the expected partial regression coefficients estimated from these models are derived, and the flexibility of the general approach is illustrated. An extended model is formulated for the analysis of data from combined samples of affected and control twin pairs that yields tests for differential h2 and c2 in the two groups as well as pooled estimates of these parameters. The application of these models is illustrated by an analysis of data from reading-disabled and control twin pairs. Because of the ease, flexibility, and utility of the multiple regression analysis of twin data, it is an appealing alternative to more traditional model-fitting approaches.

Diseases in Twins↗
PubMed1997

Outcome after severe head injury: an analysis of prediction based upon comparison of neural network versus logistic regression analysis.

More reliable prediction of outcome would be helpful for clinicians who treat severely head-injured patients. To determine if neural network modeling would improve outcome prediction compared with standard logistic regression analysis and to determine if data available 24 h after severe head injury allows better prediction than data obtained within 6 h, we tested the ability of both techniques at these two times to predict outcome (dead versus alive) at 6 months. One thousand sixty-six consecutive patients with Glasgow Coma Scale scores of 8 or less during the first 24 h after injury were randomly divided into two groups. Data from the first group (n = 799) were used to develop the models; data from the second group (n = 267) were used to test the accuracy, sensitivity, and specificity of the models by comparing predicted and actual outcomes. The 6-month mortality rate was 63.5%. Our findings confirm the importance of age, Glasgow Coma Scale scores, and hypotension in predicting outcome. Using data available at 24 h improved the predictive power of both models compared with admission data; at both time points, however, the differences in the results obtained with the two models were negligible. We conclude that outcome (dead versus alive) at 6 months after severe head injury can be predicted with logistic regression or neural network models based on data available at 24 h. Critical therapeutic decisions, such as cessation of therapy, should be based on the patient's status 1 day after injury and only rarely on admission status alone.

Adolescent↗
PubMed2006

Cardiorespiratory fitness and laboratory stress: a meta-regression analysis.

We performed a meta-regression analysis of 73 studies that examined whether cardiorespiratory fitness mitigates cardiovascular responses during and after acute laboratory stress in humans. The cumulative evidence indicates that fitness is related to slightly greater reactivity, but better recovery. However, effects varied according to several study features and were smallest in the better controlled studies. Fitness did not mitigate integrated stress responses such as heart rate and blood pressure, which were the focus of most of the studies we reviewed. Nonetheless, potentially important areas, particularly hemodynamic and vascular responses, have been understudied. Women, racial/ethnic groups, and cardiovascular patients were underrepresented. Randomized controlled trials, including naturalistic studies of real-life responses, are needed to clarify whether a change in fitness alters putative stress mechanisms linked with cardiovascular health.

Cardiovascular Physiological Phenomena↗
PubMed2005

Prognosis of conservatively treated patients with Pott's paraplegia: logistic regression analysis.

OBJECTIVE: To evaluate the prognostic significance of various clinical, radiological, and neurophysiological findings in conservatively treated patients with Pott's paraplegia, using multiple regression analysis. METHODS: The study included 43 patients with Pott's paraplegia, managed conservatively. The diagnosis of Pott's spine was based on clinical, magnetic resonance imaging, and computed tomography or ultrasound guided aspiration biopsy. All patients were examined clinically, and motor evoked potentials (MEPs) to lower limbs and tibial somatosensory evoked potentials (SEP) were recorded. Outcome at six months was defined as good or poor. For evaluating predictors of outcome, 15 clinical, investigative, and evoked potential variables were analysed, using multiple logistic regression analysis. RESULTS: The age range of the patients was 16-70 years, and 22 were female. Mild spasticity with hyperreflexia only was seen in 13 patients. In the remaining, weakness was severe in eight, and moderate and mild in 11 patients each. Twenty patients had loss of joint position sensation. MEP and SEP were abnormal in 19 and 18 patients, respectively. On multiple regression analysis, the best model predicting six month outcome included power, paraplegia score, SEP, and MEP. CONCLUSION: Patients with Pott's paraplegia are likely to recover completely by six months if they have mild weakness, lower paraplegia score and normal SEPs and MEPs.

Adolescent↗
PubMed2000

Multiple regression analysis with optimal molecular descriptors.

We consider construction of optimal molecular descriptors to be used for multiple regression analysis of several properties of alcohols. The descriptors are obtained by considering shorter paths with variable weight x for carbon-oxygen bond in alcohol. In particular we consider as molecular descriptors paths of length 1, 2 and 3. The multiple regression analysis of the following molecular properties was examined: - log S (S = solubility), CSA (cavity surface area), log P (P = octanol/water partition), and log gamma (gamma = infinite solution activity coefficient). By minimizing the standard error of the regression for each property we found optimal variable weight.

Alcohols↗
PubMed1985

Survival with primary cutaneous malignant melanoma, evaluated from 2012 cases. A multivariate regression analysis.

Cox's multivariate regression model for survival data was applied to 2,012 patients with primary cutaneous melanoma in order to evaluate the relative prognostic value of numerous clinical and histological variables and to establish their prognostically most efficient combination. The material was divided into 4 groups according to the size of resection margin of the primary lesion (less than 2.0 cm, 2.0 cm, 2.1-4.9 cm, and greater than or equal to 5.0 cm). Data were analysed separately in these 4 groups and equivalent results were obtained. The risk factors were clinical stage, site of tumour, tumour thickness, level of invasion, mitotic activity, ulceration, lymphocytic reaction, predominant type of invasive tumour cell and partial regression. When accounting for these factors, histological type, nuclear pleomorphism, nucleolar size, vascular invasion, pigmentation, verrucous growth pattern, and dermal elastosis were without prognostic influence. The effect of sex and age of patient was uncertain and both variables, therefore, were retained in the model. By using Cox's method it is possible to make a qualified estimate of the survival for the individual patient.

Adult↗
PubMed2004

Multiple linear regression analysis of bacterial deposition to polyurethane coatings after conditioning film formation in the marine environment.

Many studies have shown relationships of substratum hydrophobicity, charge or roughness with bacterial adhesion, although bacterial adhesion is governed by interplay of different physico-chemical properties and multiple regression analysis would be more suitable to reveal mechanisms of bacterial adhesion. The formation of a conditioning film of organic compounds adsorbed from seawater affects the properties of substratum surfaces prior to bacterial adhesion, which is a complicating factor in studying the mechanism of bacterial adhesion. In this paper, the impact of conditioning films adsorbed from natural seawater to four polyurethane coatings with different hydrophobicity, elasticity and roughness was studied for three different marine bacterial strains in a multiple linear regression analysis. The water contact angle on hydrophobic coatings decreased on average by 8 degrees and increased on average by the same amount on hydrophilic coatings. These changes were accompanied by increased concentrations of oxygen and nitrogen on the surface as determined by X-ray photoelectron spectroscopy, indicative of adsorption of proteinaceous material. Furthermore, the mean surface roughness increased on average by 4 nm after conditioning film formation. Multiple linear regression analysis revealed that changes in deposition due to conditioning film formation of Marinobacter hydrocarbonoclasticus, Psychrobacter sp. SW5H and Halomonas pacifica in a stagnation-point flow chamber could be explained in a model comprising hydrophobicity and the prevalence of nitrogen-rich components on the surface for the most hydrophobic strain. For the two more hydrophilic strains, deposition was governed by a combination of surface roughness and hydrophobicity. Elasticity was not a factor in bacterial adhesion to conditioning films.

Bacterial Adhesion↗
PubMed2001

Comparison of the prediction of extremely low birth weight neonatal mortality by regression analysis and by neural networks.

AIMS: To compare the prediction of mortality in individual extremely low birth weight (ELBW) neonates by regression analysis and by artificial neural networks. STUDY DESIGN: A database of 23 variables on 810 ELBW neonates admitted to a tertiary care center was divided into training, validation, and test sets. Logistic regression and neural network models were developed on the training set, validated, and outcome (mortality) predicted on the test set. Stepwise regression identified significant variables in the full set. Regression models and neural networks were then tested using data sets with only the identified significant variables, and then with variables excluded one at a time. RESULTS: The area under the curve (AUC) of receiver operating characteristic (ROC) curves for neural networks and regression was similar (AUC 0.87+/-0.03; p=0.31). Birthweight or gestational age and the 5-min Apgar score contributed most to AUC. CONCLUSIONS: Both neural networks and regression analysis predicted mortality with reasonable accuracy. For both models, analyzing selected variables was superior to full data set analysis. We speculate neural networks may not be superior to regression when no clear non-linear relationships exist.

Area Under Curve↗
PubMedDate not supplied

Relationship between the oncogene activation profiles and the tumor suppressor gene inactivation profiles in 19 human neoplasias - a regression analysis study of the intercancer linkage with the world cancer incidence data.

This study represents an extension of our statistical studies of age-adjusted incidence rates (AAIRs) of 19 neoplasias from 47 population units of the world. We have invented 2 data manipulation methods (topological data conversion and sequential regression analysis method) to estimate separately the intensities of each oncogene activation and tumor suppressor gene inactivation of a given tumor (marker tumor) relative to a counterpart tumor (reference tumor) in terms of r seq value. This study prepared the r seq table of all permutations of tumor pairs for each of the 2 cancer genes and for each sex first, and then investigated the relation between the r seq profile of oncogene activation and that of tumor suppressor gene inactivation for each tumor. A profile containing 16 (male) or 17 (female) r seq data was prepared for each tumor pair, for each cancer gene, and for each sex. The extent of similarity between 2 r seq profiles was assessed by the 1st order regression analysis in terms of the correlation coefficient r value. Results obtained are given as follows: a) The proportions of both the tumor pairs with r seq values of less than -0.90 in the oncogene activation tables of two sexes and those with r seq values of more than +0.90 in the tumor suppressor gene inactivation tables of the two sexes were all more than 50%. A small number of tumor pairs in both the oncogene activation tables and the tumor suppressor gene inactivation tables have invaded deep into each the plus- and the minus-areas to constitute the very end of long tails of the r seq profiles. b) In spite of the above symmetry of data distribution between the 2 cancer-gene tables, individual cancer pairs very rarely gave 2 cancer-gene profiles that fit the definition of symmetry. Taken together, our data manipulation was a success in presenting an oncogene activation profile and a tumor suppressor gene inactivation profile separately. c) The similarity test was conducted with all combinations of tumor pair profiles for each cancer gene and for each sex. The frequency distributions of r values in the oncogene activation tables of both sexes looked normal with long tails to both the plus- and the minus-areas. In contrast, the corresponding frequency distributions of r in the tumor suppressor gene inactivation tables of both sexes were skewed towards the direction of +1.0. It was indicated that the morphological specificity of the oncogene activation profiles was much higher than that of the tumor suppressor gene inactivation profiles. d) Male versus female comparison in 2 neoplasias with sex discrimination of cancer risk revealed that the combination of the general depression of r seq values in the oncogene profile of dominant gender and the general elevation of r seq values in the oncogene profile of recessive gender was the common trait of female-dominant breast cancer and male-dominant laryngeal cancer. It is suggested that the predominance of oncogene activation impact over the tumor suppressor gene inactivation impact was implicated in the creation of sex discrimination of cancer risk. e) Application of a new test method (reciprocal regression analysis) to the r seq table data led to the conclusion that the 2 cancer genes are interfering with each other, and that the balance of power between the 2 cancer genes varies from one marker tumor to the other. f) The results obtained in this study together with the consistency of data interpretation is discussed in light of thermodynamics.

Female↗
PubMed1992

Decreased concentration of myofibrils and myofiber hypertrophy are structural determinants of impaired left ventricular function in patients with chronic heart diseases: a multiple logistic regression analysis.

OBJECTIVES: The aim of this study was to perform a multiple logistic regression analysis to identify independent structural determinants of impaired left ventricular function. BACKGROUND: The association between contractile failure and structural alterations of the myocardium has been demonstrated in several studies, and multiple interactions between myocardial structure and cardiac performance are likely. METHODS: Morphometric data assessed from 130 left ventricular biopsy specimens were analyzed. The endomyocardial specimens were obtained from 57 patients with normal coronary arteries (17 with normal left ventricular ejection fraction and 40 with impaired left ventricular function [dilated cardiomyopathy]), 15 patients with hypertrophic cardiomyopathy and 32 patients with aortic valve disease. Transmural biopsy specimens were assessed in 6 donor hearts before heart transplantation and in 20 patients with left anterior descending coronary artery disease whose specimens were obtained from the left ventricular anterior wall during aortocoronary bypass surgery. Global or regional left ventricular function was evaluated from left cineventriculograms. The volume fraction of cardiac fibrous tissue, intracellular volume fraction of myofibrils, volume fraction of myofibrils related to myocardial tissue (including fibrosis) and myofiber diameters were determined from semithin sections of the biopsy specimens with the use of light microscopic morphometry. RESULTS: Multiple logistic regression analysis revealed decreased volume fraction of myofibrils (p < 0.005) and increased fiber diameter (p < 0.002) as independent determinants of impaired left ventricular function. CONCLUSIONS: These data indicate that, independent of the underlying heart disease, both decreased concentration of contractile proteins and myocyte hypertrophy are independently associated with impaired left ventricular function.

Age Factors↗
PubMed1992

Using regression analysis to predict emergency patient volume at the Indianapolis 500 mile race.

BACKGROUND: Emergency physicians often plan and provide on-site medical care for mass gatherings. Most of the mass gathering literature is descriptive. Only a few studies have looked at factors such as crowd size, event characteristics, or weather in predicting numbers and types of patients at mass gatherings. PURPOSE: We used regression analysis to relate patient volume on Race Day at the Indianapolis Motor Speedway to weather conditions and race characteristics. METHODS: Race Day weather data for the years 1983 to 1989 were obtained from the National Oceanic and Atmospheric Administration. Data regarding patients treated on 1983 to 1989 Race Days were obtained from the facility hospital (Hannah Emergency Medical Center) data base. Regression analysis was performed using weather factors and race characteristics as independent variables and number of patients seen as the dependent variable. Data from 1990 were used to test the validity of the model. RESULTS: There was a significant relationship between dew point (which is calculated from temperature and humidity) and patient load (P less than .01). Dew point, however, failed to predict patient load during the 1990 race. No relationships could be established between humidity, sunshine, wind, or race characteristics and number of patients. CONCLUSION: Although higher dew point was associated with higher patient load during the 1983 to 1989 races, dew point was a poor predictor of patient load during the 1990 race. Regression analysis may be useful in identifying relationships between event characteristics and patient load but is probably inadequate to explain the complexities of crowd behavior and too simplified to use as a prediction tool.

Automobiles↗
PubMed1991

Antibiotic disk diffusion testing revisited. Single strain regression analysis. Review article.

The standardized (NCCLS, ICS, DIN etc.) disk diffusion method is the most widespread technique for antibiotic susceptibility testing. Interpretive zone breakpoints are calculated from the regular regression line between minimum inhibitory concentrations (MIC) of bacterial isolates and the corresponding inhibition zone diameters around the disk containing the antibiotic. Studies of the regression line has revealed marked differences between different bacterial species. A newly described equation, the single strain regression analysis (SRA) equation, can be used to determine the regression line constants for individual strains. This method was applied to ciprofloxacin and S. aureus, E. faecalis, E. coli, P. mirabilis, P. aeruginosa, and P. maltophilia. The slope and intercept constants were determined for all 40 strains and showed a strong similarity within each species. A close similarity was also observed between the two Pseudomonas species and between S. aureus and E. faecalis. When the regression lines calculated by SRA for individual strains were extrapolated towards higher MIC values, the lines obtained for the more susceptible strains predicted the zones of more resistant strains within the species. The applications of SRA to several other antibiotics and bacterial species in earlier studies were reviewed. One exception to the predictive power of SRA has been detected earlier, H. influenzae and erythromycin. This led to the formulation of the standard curve regression analysis (SCA) equation which requires the use of two or more strains. Methodological aspects of SRA/SCA applications were presented. Three areas are particularly well suited for the use of SRA/SCA: 1. Calculation of interpretive zone breakpoints corresponding to recommended MIC limits in the individual laboratory. 2. Analysis of the effects of various disk contents of antibiotic on the resulting inhibition zones for various bacteria when new antibiotics are introduced. 3. Analytical tool as part of external quality control programmes.

Diffusion↗