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Systematics are proposed for the (n, p), (n, alpha) and (n, 2n) reactions cross-sections at 14.5 MeV neutron energy based on the statistical model, with consideration of the Q-value dependence and odd-even effects. The obtained relations are compared with other recently proposed systematics based on the statistical model as well as on the asymmetry parameter dependence.
Between January and December 2002, a total of 21,685 records for bulk tank milk somatic cell count (BTSCC) were obtained from 309 dairy ewe herds belonging to the Sheep Improvement Consortium in Castilla-Leon, Spain. Based on the first statistical model, ANOVA detected significant effects of herd, breed, month within herd, dry therapy, type of milking, contagious agalactia, and installations within machine milking on logBTSCC. A second statistical model was used on herds with machine milking to study the effect of the vacuum level and pulsation rate on BTSCC. Herd and month within herd were important variation factors as they explained 48.4 and 16.1% of the variance in BTSCC. Variability in logBTSCC among breeds ranged from 5.84 (Castellana) to 6.09 (Awassi and Spanish Assaf). Implementing dry-ewe therapy (5.91) significantly reduced logBTSCC compared with when it was not implemented (6.10). Hand milking elicited greater logBTSCC (6.07) than machine milking (5.94). Machine milking of ewes in milking parlors (logBTSCC: 5.88 to 5.94) was associated with better udder health than was the use of bucket-milking machines (6.04). Reduced vacuum levels and elevated pulsation rate during machine milking optimized BTSCC. In all cases, clinical outbreaks of contagious agalactia increased BTSCC. As a result, dry therapy was proposed as the main tool to reduce BTSCC. Optimization of milking-machine standards and parlor systems also improved udder health in dairy sheep.
The specific behavioral manifestation associated with the different disorders producing the syndrome of dementia have remained poorly investigated. We examined the memory performance of three distinct groups of patients with dementia secondary to Alzheimer's disease (AD) multiple-infarctions (MID) and vertebrobasilar insufficiency (VBI) on the ten subtests of the Wechsler Memory Scale (WMS). Statistical methods of analysis were used to maximize the differences between the groups. Univariate statistical procedures revealed that the AD group performed significantly and consistently lower than the two cerebrovascular groups. There were no significant differences between the two cerebrovascular disease groups, even though the MID group tended to perform consistently more poorly than the VBI group. For heuristic and conceptual purposes as well as to determine which combination of the ten WMS variables produced the "best" statistical model differentiating the groups the data was analyzed by multivariate techniques. A discriminate function analysis obtained a 100% valid positive hit rate in discriminating among the three groups. One hundred percent diagnostic accuracy was also obtained in discriminating between MID and AD as well as AD and VBI. The two cerebrovascular groups tended to overlap in their probability distributions with an 81% hit rate. Different predicitive statistical models were identified to differentiate the various diagnostic groups. It was possible to discriminate the three diagnostic groups by different patterns of memory performance.
The objective of the present study was to examine the possible associations between low molecular weight (LMW) apolipoprotein(a) (apo(a)) isoforms (F,B,S1,S2) and coronary heart disease (CHD). We conducted a nested case-control (prospective) study of five cohorts of white men: The 1936 cohort (baseline 1976, n = 548) and four cohorts from MONICA I born in 1923 (n = 463), 1933 (n = 491), 1943 (n = 504) and 1953 (n = 448) studied at baseline in 1983. At follow up in 1991, 52 subjects had developed a first myocardial infarction and 22 had been hospitalized with angina pectoris. Plasma samples obtained at baseline were stored frozen until 1993-94, when case samples (n = 74) were analyzed together with samples from matched (disease free) controls (n = 190). In a statistical model (conditional logistic regression) including all age groups, cholesterol (or apo B) level (P < 0.01), systolic blood pressure (P = 0.05) and smoking (P = 0.02) predicted CHD. In the statistical model Lp(a) interacted significantly with age (OR = 5.7; 95% CI: 1.4-23.6; P = 0.016), and high Lp(a) (over 45 mg/dl) was associated with significantly increased risk in subjects under 60 years (OR = 3.82; 95% CI: 1.47-9.96), but not in older men (OR = 0.67; 95% CI: 0.235-1.89). Therefore, we studied the impact of Lp(a)/apo(a) and other variables in subjects who had been under 60 years when they became cases. Among the younger subjects the presence of LMW apo(a) isoforms significantly predicted the development of CHD (OR = 3.83; 95% CI: 1.18-12.4). The increased risk pertained to high Lp(a) (above versus below 45 mg/dl: OR = 3.68; 95% CI: 1.03-13.10), and to Lp(a) concentrations when entered into the model as a continuous variable (P = 0.04). Cholesterol or apo B (P < 0.01), smoking (P = 0.02), systolic blood pressure (P = 0.05) and low alcohol consumption (under nine drinks/week) (P = 0.04) were also significant predictors of CHD. We conclude that LMW apo(a) isoforms are significantly associated with increased risk of CHD in men under 60 years.
A statistical model is presented of the direct and indirect influences of 21 variables upon success at selection for medical school.
INTRODUCTION: New defibrillation techniques are often compared to standard approaches using the defibrillation threshold. However, inference from thresholding data necessitates extrapolation from reactions to relatively ineffective shocks, an error prone procedure requiring large sample sizes for hypothesis testing and large safety margins for defibrillator implantation. In contrast, this article presents a clinically validated statistical model of a minimum error, four-shock defibrillation testing protocol for estimating the 80% effective defibrillation strength for a given patient (ED80). METHODS AND RESULTS: A Bayesian statistical model was constructed assuming that the defibrillation dose-response curve is sigmoidal, and the ED80 is between 150 and 750 V. The model was used to design a minimum predicted error testing protocol and estimates. To prospectively validate the testing protocol and estimates, 170 patients received voltage-programmed biphasic testing. Four fibrillation episodes were induced and terminated in each patient according to the Bayesian up-down protocol. In addition, a validation attempt was made at the estimated ED80 rounded up to the nearest 50 V. In order to estimate the safety margin, in 136 patients, a defibrillation attempt was made at the rounded ED80 + 100 V. Of the 170 attempts at the rounded ED80, 143 (84%) attempts terminated fibrillation. Of the 136 attempts at the rounded ED80 + 100 V, 133 (98%) were effective. CONCLUSIONS: The four-shock Bayesian up-down protocol is the first clinical protocol to accurately predict an ED80 voltage. A 100 V increment above the ED80 provides an adequate safety margin. This simple and accurate method for estimating a highly effective defibrillation dose may be a valuable tool for population-based clinical hypothesis testing, as well as defibrillator implantation.
It has been suggested that use of interactive statistical models would greatly increase the proportion of variance accounted for by studies of physician utilization. The purpose of this paper is to evaluate and describe the benefits and pitfalls of using interactive statistical models of physician utilization. The paper presents Monte Carlo simulation data and real world data to determine how much more of the variance in physician utilization can be accounted for by interactive regression models. Results indicate that adding interaction terms is unlikely to produce large increases in variance accounted for. The usefulness of interactive models is particularly low when there is substantial measurement error in the predictor variables. Other advantages and disadvantages of interactive models are discussed, including 1) improved understanding, 2) inflation of alpha, 3) sensitivity to transformations and scale of measurement, and 4) confounding of interaction effects with nonlinear effects.
This study aimed to develop statistical models describing the learning of endotracheal intubation (ETI). We collected data from 100 subjects undergoing ETI training with intubatable medical models and manikins (airway trainers). Trainees initially viewed a video about ETI and an instructor demonstrated the technique. Subjects then made up to 17 supervised trials. Each trial was scored as a success or failure; this score was the primary outcome used in analyses. Random effects and population-averaged logit models, and a learning model intended to quantify the relative contributions of failed and successful trials to the learning process, were fitted to the data. The logit models provided evidence of differences in difficulty between different airway trainers and differences in success rate related to previous ETI experience. Trainees became familiar with an airway trainer after multiple trials, as demonstrated by a 50% decrease in the odds of successful ETI when starting on a new trainer. The learning model indicated that a trainee learns about as much from 1 successful ETI as from 12 (95% confidence interval, 2-23) failed trials. The results demonstrate the feasibility of statistical modeling of the learning of ETI and provide insight into the learning process.
Daily self-reports of moods, using a Swedish Mood Adjective Check List with six bipolar dimensions, were performed in patients with alcohol dependence. One group (n = 13), consisting of inpatients in late withdrawal at a home for addicts, was studied for 6 weeks; another group (n = 12), consisting of outpatients in full remission, was studied for 1 week. The results of the ratings on the check-list were used to test a statistical model for its capacity to describe the individual courses of mood states during the recovery process. Time Series Analysis revealed that the individual courses of moods fitted an autoregressive statistical model and could thereby be summarized in numerical measures for individual baselines, amplitudes, and lengths of recovery. Such individual profiles of the alcohol recovery process, may be useful for both clinical and research purposes, making it possible, for instance, to predict points in time for an individual's recovery with respect to his total well-being, as well as the various dimensions of his mood states.
BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.
To compare the gastric ulcer healing rates of lansoprazole with histamine2-receptor antagonists (H2RAs) (ranitidine, famotidine, cimetidine, and roxatidine), a meta-analysis was performed using data from five published and eight unpublished randomized controlled trials. Analyses were performed using (1) both evaluable patients (n = 1527) and all randomized patients (n = 1655) (assuming that patients lost to follow-up were treatment failures); (2) all studies and a subset of studies that received high methodologic quality scores; and (3) fixed-effects, random-effects, and Bayesian statistical models. In all cases, lansoprazole was associated with a significantly higher rate of endoscopic healing at both 4 and 8 weeks compared with the H2RAs. When the most conservative Bayesian statistical model and intent-to-treat analysis were used, lansoprazole was associated with a 33% higher healing rate at 4 weeks (risk ratio = 1.33; 95% confidence interval [CI] = 1.19 to 1.49) and a 12% higher healing rate at 8 weeks (risk ratio = 1.12; 95% CI = 1.06 to 1.19) than were the H2RA agents. Similar results were obtained when the meta-analysis was performed on evaluable rather than all randomized patients and using the three different analytical techniques noted above. Slightly lower, though still highly significant, improvement in ulcer healing rates was obtained when the meta-analysis was performed using a subset of six studies that received high methodologic quality scores. These results support the conclusion that lansoprazole heals ulcers more quickly than do the H2RAs and also achieves higher overall rates of healing. The eradication of Helicobacter pylori associated with gastric ulcers was not assessed in individual studies.
The combined impact of multiple plant parasites on plant performance can either be additive (the total damage equals the sum of the individual effects) or nonadditive (synergistic or antagonistic damage). Two statistical models are available for testing the independent (=additive) effects of two factors. Here we suggest that the natural history of the plant-parasite system should motivate the choice of a statistical model to test for additivity. Using in-field, manipulative experiments, we examined the interactions between the herbivorous mite Calacarus flagelliseta Fletchmann, De Moraes and Barbosa (Acari: Eriophyidae), the fungal pathogen Oidium caricae F. Noack (a powdery mildew), and their host plant Carica papaya L. in Hawaii. First, we found that herbivorous mites had a moderate negative effect on powdery mildew: when mites were absent, powdery mildew colonies were larger and more numerous. Second, we showed that each plant parasite, when evaluated alone, significantly reduced several measures of plant performance. Third, we found that the combined impact of mites and mildew on plant performance is mostly additive and, for a few variables, less than additive. Finally, we explored compensatory responses and found no evidence for nonlinearities in the relationship between plant performance and cumulative parasite impact. Plants are almost universally subject to attack by multiple herbivores and pathogens; thus a deeper understanding of how multiple plant parasites shape each other's population dynamics and plant performance is essential to understanding plant-parasite interactions.
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The objective of the present study was to analyse the association between cigarette smoking and progression of retinopathy and nephropathy, respectively, in a prospective multicentre study including 636 people with Type 1 diabetes: 81% of the original cohort of consecutively referred patients, aged 15 to 40 years and free of severe late diabetic complications. At baseline, all patients had participated in a 5-day in-patient group treatment and teaching programme for intensification of insulin therapy. Patients were examined at recruitment, and after 1, 2, 3 and 6 years including assessment of smoking status, blood pressure, metabolic control, and degree of nephropathy. Degree of retinopathy was assessed by ophthalmoscopy or fundus photography at baseline and after 6 years. Several logistic regression analyses were performed by describing the responses retinopathy and nephropathy, respectively, either as progression yes/no or as actual status at the 6-year follow-up and by using different measures for smoking. Adjustments for important covariables were made. While significant associations between smoking, and retinopathy and nephropathy respectively, were found, the relations were variable depending on the statistical model used. The results show that the real associations between smoking and retinopathy and nephropathy are complex and that more emphasis should be put on the complete description of the response variables and the statistical models used in clinical and epidemiological research.
BACKGROUND: Criticism has been made of observational studies in clinical practice because of their failure to control for unobserved factors that correlate with both initial treatment selection and observed outcomes. METHOD: A two-stage statistical model was applied to data obtained from a large general practitioner medical records database (DIN-LINK) to estimate the effect of initial antidepressant selection on the duration of antidepressant therapy and on the likelihood of being prescribed an average daily dose above the minimum recommended dose. The statistical model controlled for unobserved factors correlated with initial treatment selection and the observed outcomes as well as for observed confounders. RESULTS: Unobserved factors correlated with treatment selection were not a statistically significant determinant of the number of days of antidepressant therapy. However, unobserved factors correlated with treatment selection were a statistically significant determinant of the likelihood of receiving an average dose during therapy greater than the minimum recommended. After controlling for relevant confounders, those patients who began treatment with sertraline as opposed to fluoxetine had fewer days of antidepressant therapy and were more likely to receive average doses greater than the minimum recommended during therapy. CONCLUSION: Unobserved factors correlated with treatment selection can impact outcomes in observational studies and should be tested and controlled for whenever possible.
Association studies are designed to identify main effects of alleles across a potentially wide range of genetic backgrounds. To control for spurious associations, effects of the genetic background itself are often incorporated into the linear model, either in the form of subpopulation effects in the case of structure or in the form of genetic relationship matrices in the case of complex pedigrees. In this context epistatic interactions between loci can be captured as an interaction effect between the associated locus and the genetic background. In this study I developed genetic and statistical models to tie the locus by genetic background interaction idea back to more standard concepts of epistasis when genetic background is modeled using an additive relationship matrix. I also simulated epistatic interactions in four-generation randomly mating pedigrees and evaluated the ability of the statistical models to identify when a biallelic associated locus was epistatic to other loci. Under additive-by-additive epistasis, when interaction effects of the associated locus were quite large (explaining 20% of the phenotypic variance), epistasis was detected in 79% of pedigrees containing 320 individuals. The epistatic model also predicted the genotypic value of progeny better than a standard additive model in 78% of simulations. When interaction effects were smaller (although still fairly large, explaining 5% of the phenotypic variance), epistasis was detected in only 9% of pedigrees containing 320 individuals and the epistatic and additive models were equally effective at predicting the genotypic values of progeny. Epistasis was detected with the same power whether the overall epistatic effect was the result of a single pairwise interaction or the sum of nine pairwise interactions, each generating one ninth of the epistatic variance. The power to detect epistasis was highest (94%) at low QTL minor allele frequency, fell to a minimum (60%) at minor allele frequency of about 0.2, and then plateaued at about 80% as alleles reached intermediate frequencies. The power to detect epistasis declined when the linkage disequilibrium between the DNA marker and the functional polymorphism was not complete.
The clinical evaluation of venous catheters for catheter-induced infections must conform to a strict biometric methodology. The statistical planning of the study (target population, design, degree of blinding), data management (database design, definition of variables, coding), quality assurance (data inspection at several levels) and the biometric evaluation of the Erlanger silver catheter project are described. The three-step data flow included: 1) primary data from the hospital, 2) relational database, 3) files accessible for statistical evaluation. Two different statistical models were compared: analyzing the first catheter only of a patient in the analysis (independent data) and analyzing several catheters from the same patient (dependent data) by means of the generalized estimating equations (GEE) method. The main result of the study was based on the comparison of both statistical models.
OBJECTIVE: To provide an account of the state of diet and chronic disease research designs and methods; to discuss the role and potential of aggregate and analytical observational studies and randomised controlled intervention trials; and to propose strategies for strengthening each type of study, with particular emphasis on the use of nutrient biomarkers in cohort study settings. DESIGN: Observations from diet and disease studies conducted over the past 25 years are used to identify the strengths and weaknesses of various study designs that have been used to associate nutrient consumption with chronic disease risk. It is argued that a varied research programme, employing multiple study designs, is needed in response to the widely different biases and constraints that attend aggregate and analytical epidemiological studies and controlled intervention trials. Study design modifications are considered that may be able to enhance the reliability of aggregate and analytical nutritional epidemiological studies. Specifically, the potential of nutrient biomarker measurements that provide an objective assessment of nutrient consumption to enhance analytical study reliability is emphasised. A statistical model for combining nutrient biomarker data with self-report nutrient consumption estimates is described, and related ongoing work on odds ratio parameter estimation is outlined briefly. Finally, a recently completed nutritional biomarker study among 102 postmenopausal women in Seattle is mentioned. The statistical model will be applied to biomarker data on energy expenditure, urinary nitrogen, selected blood fatty acid measurements and various blood micronutrient concentrations, and food frequency self-report data, to identify study subject characteristics, such as body mass, age or socio-economic status, that may be associated with the measurement properties of food frequency nutrient consumption estimates. This information will be crucial for the design of a potential larger nutrient biomarker study within the cohort study component of the Women's Health Initiative. SETTING AND SUBJECTS: The methodology under study is expected to be pertinent to a wide variety of diet and chronic disease association studies in the general population. Ongoing work focuses on statistical methods developed using computer simulations motivated by studies of dietary fat in relation to breast and colon cancer among post-menopausal women, and ongoing pilot studies to be described in detail elsewhere, involving post-menopausal women living in the Seattle area. RESULTS AND CONCLUSION: A varied research programme appears to be needed to make progress in the challenging diet and chronic disease research area. Such progress may include aggregate studies of diet and chronic disease that include sample surveys in diverse population groups world-wide, analytical epidemiological studies that use nutrient biomarker data to calibrate self-report nutrient consumption estimates, and randomised controlled intervention trials that arise from an enhanced infrastructure for intervention development. New innovative designs, models and methodologies are needed for each such research setting.