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In the original analysis, a standard regression model was used with time as a continuous variable. We contrast the results from this standard regression analysis with those from segmented regression analysis. We discuss the limitations of the former and advantages of the latter, as well as the challenges of using segmented regression in analysing complex quality improvement interventions. Based on the estimated change in intercept and slope from pre- to post-intervention using segmented regression , we found insufficient evidence of a statistically significant effect on quality of care for stroke, although potential clinically important effects for AMI cannot be ruled out.
Segmented regression analysis is the recommended approach for analysing data from an interrupted time series study. Several modifications to the basic segmented regression analysis approach are available to deal with challenges arising in the evaluation of complex quality improvement interventions. Multiple regression and use-availability analyses are two methods for examining habitat selection. Use-availability analysis is commonly used to evaluate macrohabitat selection whereas multiple regression analysis can be used to determine microhabitat selection.
In education and the social sciences, problems of interest to researchers and users of research often involve variables that do not meet the assumptions of regression in the area of an equal interval scale relative to a zero point. Various coding schemes exist that allow the use of regression while still answering the researcher's questions of…. Systematic analysis of factors associated with progression and regression of ulcerative colitis in patients.
Studies that systematically assess change in ulcerative colitis UC extent over time in adult patients are scarce. To assess changes in disease extent over time and to evaluate clinical parameters associated with this change. Data from the Swiss IBD cohort study were analysed. We used logistic regression modelling to identify factors associated with a change in disease extent.
A total of UC patients At diagnosis, UC patients presented with the following disease extent: proctitis [ patients During a median disease duration of 9  years, progression and regression was documented in patients In addition, patients The following factors were identified to be associated with disease progression: treatment with systemic glucocorticoids [odds ratio OR 1. No specific factors were found to be associated with disease regression.
Over a median disease duration of 9  years, about two-thirds of UC patients maintained the initial disease extent; the remaining one-third had experienced either progression or regression of the disease extent. Bayesian regression analyses of radiation modality effects on pericardial and pleural effusion and survival in esophageal cancer.
To evaluate radiation modality effects on pericardial effusion PCE , pleural effusion PE and survival in esophageal cancer EC patients. Bayesian semi-competing risks SCR regression models were fit to assess effects of radiation modality and prognostic covariates on the risks of PCE and PE, and death either with or without these preceding events. Bayesian piecewise exponential regression models were fit for overall survival, the time to PCE or death, and the time to PE or death.
All models included propensity score as a covariate to correct for potential selection bias. The respective probabilities of a patient being alive without either PCE or PE at 3-years and 5-years were 0. Birthweight is one of the most important predicting indicators of the health status in adulthood.
Having a balanced birthweight is one of the priorities of the health system in most of the industrial and developed countries. This indicator is used to assess the growth and health status of the infants. The aim of this study was to assess the birthweight of the neonates by using quantile regression in Zanjan province. Data were analyzed using multiple linear regressions andquantile regression method and SAS 9.
The mean age of the mothers was Five hundred and seventy-three patients 6. In all quantiles, gestational age of neonates p 0. This study revealed the results of multiple linear regression and quantile regression were not identical. We strictly recommend the use of quantile regression when an asymmetric response variable or data with outliers is available. Introduction: Birthweight is one of the most important predicting indicators of the health status in adulthood. Conclusion: This study revealed the results of multiple linear regression and quantile regression were not identical.
Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling SEM. Two empirical studies are presented to illustrate the….
The year US presidential election between Al Gore and George Bush has been the most intriguing and controversial one in American history. The state of Florida was the trigger for the controversy, mainly, due to the use of the misleading "butterfly ballot". Using prediction or confidence intervals for least squares regression lines…. Atherosclerosis has major morbidity and mortality implications globally. While it has often been considered an irreversible degenerative process, recent evidence provides compelling proof that atherosclerosis can be reversed.
Plaque regression is however difficult to appraise and quantify, with competing diagnostic methods available. Given the potential of evidence synthesis to provide clinical guidance, we aimed to review recent meta- analyses on diagnostic methods for atherosclerotic plaque regression. They reported on atherosclerotic plaque regression appraised with carotid duplex ultrasound, coronary computed tomography, carotid magnetic resonance, coronary intravascular ultrasound, and coronary optical coherence tomography.
Significant interactions were found with concomitant changes in low density lipoprotein cholesterol, high density lipoprotein cholesterol, and C-reactive protein levels, and with ethnicity. Atherosclerotic plaque regression and conversion to a stable phenotype is possible with intensive medical therapy and can be demonstrated in patients using a variety of non-invasive and invasive imaging modalities. It is shown how partial covariance, part and partial correlation, and regression weights can be estimated and tested for significance by means of a factor analytic model.
Comparable partial covariance, correlations, and regression weights have identical significance tests. Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. The magnitude of the effect of clustering provides a measure of the general contextual effect. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research.
The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure.
Genetic analyses of stillbirth in relation to litter size using random regression models. Data were collected from 4 purebred Duroc nucleus farms between and Two data sets with 6, litters for the first parity P1 and 6, litters for the second to fifth parity P with a total of 8, and 5, animals in the pedigree were analyzed separately. Number of stillborns was studied as a trait on sow level.
Fixed effects were contemporary groups farm-year-season and fixed cubic regression coefficients on LS with Legendre polynomials. Models for P included the fixed effect of parity. Random effects were additive genetic effects for both data sets with permanent environmental effects included for P For P1, the order of polynomial, the number of knots, and the number of intervals used for respective models were quadratic, 3, and 3, respectively.
For P, the same parameters were linear, 2, and 2, respectively. Heterogeneous residual variances were considered in the models. For P1, average estimates of genetic correlations between LS 5 to 9, 5 to 13, and 9 to 13 were 0. The negative correlations between the 2 extreme LS might possibly indicate different genetic bases on incidence of stillbirth. Risk factors for pedicled flap necrosis in hand soft tissue reconstruction: a multivariate logistic regression analysis.
Few clinical retrospective studies have reported the risk factors of pedicled flap necrosis in hand soft tissue reconstruction. The aim of this study was to identify non-technical risk factors associated with pedicled flap perioperative necrosis in hand soft tissue reconstruction via a multivariate logistic regression analysis.
For patients with hand soft tissue reconstruction, we carefully reviewed hospital records and identified patients who met the inclusion criteria. The characteristics of these patients, flap transfer procedures and postoperative complications were recorded. Eleven predictors were identified. The correlations between pedicled flap necrosis and risk factors were analysed using a logistic regression model.
Of skin flaps, flaps survived completely without any complications. The pedicled flap necrosis rate in hands was Soft tissue defects in fingers were noted in Soft tissue defect site, flap size and postoperative wound infection were risk factors associated with pedicled flap necrosis in hand soft tissue defect reconstruction. Gene regulatory network inference from multifactorial perturbation data using both regression and correlation analyses. An important problem in systems biology is to reconstruct gene regulatory networks GRNs from experimental data and other a priori information.
The DREAM project offers some types of experimental data, such as knockout data, knockdown data, time series data, etc. Among them, multifactorial perturbation data are easier and less expensive to obtain than other types of experimental data and are thus more common in practice. The GRN inference problem among [Formula: see text] genes is decomposed into [Formula: see text] different regression problems.
In each of the regression problems, the expression level of a target gene is predicted solely from the expression level of a potential regulation gene. For different potential regulation genes, different weights for a specific target gene are constructed by using the sum of squared residuals and the Pearson correlation coefficient. Then these weights are normalized to reflect effort differences of regulating distinct genes. By appropriately choosing the parameters of the power law, we constructe a integer programming problem.
By solving this problem, direct regulation genes for an arbitrary gene can be estimated. And, the normalized weight of a gene is modified, on the basis of the estimation results about the existence of direct regulations to it. These normalized and modified weights are used in queuing the possibility of the existence of a corresponding direct regulation.
Computation results with the DREAM4 In Silico Size Multifactorial subchallenge show that estimation performances of the suggested algorithm can even outperform the best team. Furthermore, the high precision of the obtained most reliable predictions shows the suggested algorithm may be helpful in guiding. According to the principle and method of drop-weight impact test, the impact resistance of concrete was measured using self-designed U-shape specimens and a newly designed drop-weight impact test apparatus.
A series of drop-weight impact tests were carried out with four different masses of drop hammers 0. The test results show that the impact resistance results fail to follow a normal distribution. As expected, U-shaped specimens can predetermine the location of the cracks very well. It is also easy to record the cracks propagation during the test. The maximum of coefficient of variation in this study is By regression analysis, the linear relationship between the first-crack and ultimate failure impact resistance is good.
It can suggested that a minimum number of specimens is required to reliably measure the properties of the material based on the observed levels of variation. Key factors contributing to accident severity rate in construction industry in Iran: a regression modelling approach. Construction industry involves the highest risk of occupational accidents and bodily injuries, which range from mild to very severe.
The aim of this cross-sectional study was to identify the factors associated with accident severity rate ASR in the largest Iranian construction companies based on data about occupational accidents recorded from to We also gathered data on safety and health risk management and training systems.
Data were analysed using Pearson's chi-squared coefficient and multiple regression analysis. Median ASR and the interquartile range was Fourteen of the 24 studied factors stood out as most affecting construction accident severity p Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle.
The objective of this work was to estimate covariance functions using random regression models on B-splines functions of animal age, for weights from birth to adult age in Canchim cattle. Data comprised 49, records on females. The model of analysis included fixed effects of contemporary groups, age of dam as quadratic covariable and the population mean trend taken into account by a cubic regression on orthogonal polynomials of animal age.
Residual variances were modelled through a step function with four classes. The direct and maternal additive genetic effects, and animal and maternal permanent environmental effects were included as random effects in the model. A total of seventeen analyses , considering linear, quadratic and cubic B-splines functions and up to seven knots, were carried out.
B-spline functions of the same order were considered for all random effects. Random regression models on B-splines functions were compared to a random regression model on Legendre polynomials and with a multitrait model. In addition, the variance components and genetic parameters estimated for each random regression model were also used as criteria to choose the most adequate model to describe the covariance structure of the data. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most adequate to describe the covariance structure of the data.
Random regression models using B-spline functions as base functions fitted the data better than Legendre polynomials, especially at mature ages, but higher number of parameters need to be estimated with B-splines functions. Preoperative predictive factors of aneurysmal regression using the reporting standards for endovascular aortic aneurysm repair.
Aneurysmal regression is a reliable marker for long-lasting success after endovascular aneurysm repair EVAR. From patients treated by EVAR between and , completed computed tomography angiographies and duplex scan follow-up images were available. Anatomic parameters were graded according to the relevant severity grades. A severity score was calculated at the aortic neck, the abdominal aortic aneurysm, and the iliac arteries.
Clinical and demographic factors were studied. Aneurysmal regression occurred in 66 patients Two multivariate analyses were done: one considered the scores and the other the variables included in the scores. In the second, group A patients were younger P Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis. The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology.
For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses , we propose a new methodology-sparse multivariate factor analysis regression model smFARM , in which correlations of response variables are assumed to follow a factor analysis model with latent factors.
The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease.
We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q This retrospective cohort study provides a descriptive analysis of a population that frequently uses an urban emergency medical service EMS and identifies factors that contribute to use among all frequent users. For purposes of this study we divided frequent users into the following groups: low- frequent users 4 EMS transports in , medium-frequent users 5 to 6 EMS transports in , high-frequent users 7 to 10 EMS transports in and super-frequent users 11 or more EMS transports in Overall, we identified individuals as frequent users.
For all groups of EMS frequent users i. Objective Physician income is generally high, but quite variable; hence, physicians have divergent perspectives regarding health policy initiatives and market reforms that could affect their incomes. We investigated factors underlying the distribution of income within the physician population. We employed least square and quantile regression models to examine factors associated with physician incomes at the mean and at various points of the income distribution, respectively.
We accounted for the complex survey design for the CTS-PS data using appropriate weighted procedures and explored endogeneity using an instrumental variables method. Principal Findings We detected widespread and subtle effects of many variables on physician incomes at different points 10th, 25th, 75th, and 90th percentiles in the distribution that were undetected when employing regression estimations focusing on only the means or medians.
Our findings show that the effects of managed care penetration are demonstrable at the mean of specialist incomes, but are more pronounced at higher levels. Conversely, a gender gap in earnings occurs at all levels of income of both PCPs and specialists, but is more pronounced at lower income levels.
Conclusions The quantile regression technique offers an analytical tool to evaluate policy effects beyond the means. A longitudinal application of this approach may enable health policy makers to identify winners and losers among segments of the physician workforce and assess how market dynamics and health policy initiatives affect the overall physician income distribution over various time intervals.
Factors associated with the income distribution of full-time physicians: a quantile regression approach. Physician income is generally high, but quite variable; hence, physicians have divergent perspectives regarding health policy initiatives and market reforms that could affect their incomes. We conducted separate analyses for primary care physicians PCPs and specialists. We detected widespread and subtle effects of many variables on physician incomes at different points 10th, 25th, 75th, and 90th percentiles in the distribution that were undetected when employing regression estimations focusing on only the means or medians.
The quantile regression technique offers an analytical tool to evaluate policy effects beyond the means. For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among men aged followed for 9 years. Groups with mortality between 5 and 93 per were considered.
Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis.
When including the same set of variables in non-stepwise analyses , all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used. Background: Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes.
In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases e. However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges.
Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta- Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM.
Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Findings: Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes i. These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms.
Conclusion: The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases.
A systematic review of methodology: time series regression analysis for environmental factors and infectious diseases. Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. Following the Preferred Reporting Items for Systematic Reviews and Meta- Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM.
Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes i. The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Objectives Lactational mastitis frequently leads to a premature abandonment of breastfeeding; its development has been associated with several risk factors.
This study aims to use a decision tree DT approach to establish the main risk factors involved in mastitis and to compare its performance for predicting this condition with a stepwise logistic regression LR model. Methods Data from cases breastfeeding women with mastitis and controls were collected by a questionnaire about risk factors related to medical history of mother and infant, pregnancy, delivery, postpartum, and breastfeeding practices.
Sensitivity, specificity and accuracy of both models were calculated. Results Cracked nipples, antibiotics and antifungal drugs during breastfeeding, infant age, breast pumps, familial history of mastitis and throat infection were significant risk factors associated with mastitis in both analyses. Bottle-feeding and milk supply were related to mastitis for certain subgroups in the DT model.
The LR model had better classification accuracy and sensitivity than the DT model, but the last one presented better specificity at the optimal threshold of each curve. Conclusions The DT and LR models constitute useful and complementary analytical tools to assess the risk of lactational infectious mastitis. The DT approach identifies high-risk subpopulations that need specific mastitis prevention programs and, therefore, it could be used to make the most of public health resources.
Background Previous reviews on risk and protective factors for violence in psychosis have produced contrasting findings. There is therefore a need to clarify the direction and strength of association of risk and protective factors for violent outcomes in individuals with psychosis.
Studies were identified that reported factors associated with violence in adults diagnosed, using DSM or ICD criteria, with schizophrenia and other psychoses. We considered non-English language studies and dissertations. Risk and protective factors were meta- analysed if reported in three or more primary studies. Meta- regression examined sources of heterogeneity. A novel meta-epidemiological approach was used to group similar risk factors into one of 10 domains.
Sub-group analyses were then used to investigate whether risk domains differed for studies reporting severe violence rather than aggression or hostility and studies based in inpatient rather than outpatient settings. Findings There were eligible studies reporting on 45, individuals, 8, A total of 39, Dynamic or modifiable risk factors included hostile behaviour, recent drug misuse, non-adherence with psychological therapies p values factors , the strongest of which were criminal history factors.
When restricting outcomes to severe violence, these associations did not change materially. In studies investigating inpatient violence, associations differed in strength but not direction. Logistic regression analysis of risk factors for prolonged pulmonary recovery in children from aspirated foreign body. Foreign body aspiration is a life-threatening emergency for children. Fried chicken is commonly available all over the world, but no cases have previously been reported addressing this food as a tracheobronchial foreign body.
We report an extremely rare case of tracheobronchial aspiration of fried chicken complicated by severe bronchitis and postoperative atelectasis. To clarify predisposing factors related to bronchopulmonary complications, we also reviewed paediatric cases of tracheobronchial foreign bodies treated in our department over the past 14 years.
We retrospectively reviewed a total of 77 cases of tracheobronchial foreign bodies from to The main outcome measure was duration of hospitalisation, reflecting postoperative therapy. Logistic regression analyses were conducted to determine risk factors for longer hospitalisation. Age, sex, and interval between the aspiration episode and bronchoscopy were not significantly associated with longer hospitalisation.
Regarding kinds of foreign bodies, higher rates of longer hospitalisation were noted for patients who had aspirated peanut or animal material, as compared to patients who had aspirated non-organic material odds ratio, 5. In terms of predicting the risk of pulmonary complications, the type of foreign body aspirated offers a more meaningful factor than the interval between aspiration and operation.
Specifically, peanuts or animal material containing oils appear to be associated with a more prolonged pulmonary recovery even after retrieval of the foreign body. Analyses of non-fatal accidents in an opencast mine by logistic regression model - a case study. Accidents cause major damage for both workers and enterprises in the mining industry.
To reduce the number of occupational accidents, these incidents should be properly registered and carefully analysed. A total of occupational accidents were analysed for this study. The accident records were categorized into seven groups: area, reason, occupation, part of body, age, shift hour and lost days.
The SPSS package program was used in this study for logistic regression analyses , which predicted the probability of accidents resulting in greater or less than 3 lost workdays for non-fatal injuries. Social facilities-area of surface installations, workshops and opencast mining areas are the areas with the highest probability for accidents with greater than 3 lost workdays for non-fatal injuries, while the reasons with the highest probability for these types of accidents are transporting and manual handling.
The four methods are compared with each other and…. Ultrasound-enhanced bioscouring of greige cotton: regression analysis of process factors. Process factors of enzyme concentration, time, power and frequency were investigated for ultrasound-enhanced bioscouring of greige cotton.
A fractional factorial experimental design and subsequent regression analysis of the process factors were employed to determine the significance of each factor a Schistosoma mansoni reinfection: Analysis of risk factors by classification and regression tree CART modeling. Praziquantel PZQ is an effective chemotherapy for schistosomiasis mansoni and a mainstay for its control and potential elimination.
However, it does not prevent against reinfection, which can occur rapidly in areas with active transmission. A guide to ranking the risk factors for Schistosoma mansoni reinfection would greatly contribute to prioritizing resources and focusing prevention and control measures to prevent rapid reinfection.
The objective of the current study was to explore the relationship among the socioeconomic, demographic, and epidemiological factors that can influence reinfection by S. Parasitological, socioeconomic, demographic, and water contact information were surveyed in S. Eligible individuals were treated with PZQ until they were determined to be negative by the absence of S. These individuals were surveyed again 12 months from the date of successful treatment with PZQ.
A classification and regression tree modeling CART was then used to explore the relationship between socioeconomic, demographic, and epidemiological variables and their reinfection status. The most important risk factor identified for S. Additional analyses , excluding heavy infection status, showed that lower socioeconomic status and a lower level of education of the household head were also most important risk factors for S. Our results provide an important contribution toward the control and possible elimination of schistosomiasis by identifying three major risk factors that can be used for targeted treatment and monitoring of reinfection.
We suggest that control measures that target heavily infected. Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions. Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods.
The study is based on a sample of subjects males and females from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions.
Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation.
Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change. Defining transition rules is an important issue in cellular automaton CA -based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change.
Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors , three logistic regression -based CA models were built to simulate dynamic land use change in Shanghai, China, from to For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change.
Simulations using three CA models with multicollinearity eliminated performed better with accuracy improvements about 3. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors , leading to better land use change models that are devoid of multicollinearity.
We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes. Purpose of the Study: A novel logistic regression tree-based method was applied to identify fall risk factors and possible interaction effects of those risk factors. However, despite adopting similar risk assessment protocols, estimates from authoritative agencies over the last 6 years have been diverse.
This may have arisen from diverse approaches to data analysis. Modelling strategies for pooling of individual subject data from cognate vitamin D randomized controlled trials RCTs are likely to provide the most appropriate DRV estimates. Thus, the objective of the present work was to undertake the first-ever individual participant data IPD -level meta- regression , which is increasingly recognized as best practice, from seven winter-based RCTs with participants ranging in age from 4 to 90 years of the vitamin D intake—serum hydroxyvitamin D 25 OH D dose-response.
Our IPD-derived estimates of vitamin D intakes required to maintain These first IPD-derived estimates offer improved dietary recommendations for vitamin D because the underpinning modeling captures the between-person variability in response of serum 25 OH D to vitamin D intake. Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea.
SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models DEMs and the precipitation in Jeju Island.
In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found.
According to the factor analysis, eight topographic variables latitude being the exception were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors model 1 to the multiple regression model with the topographic variables model 3 , it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography.
In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island. Longitudinal changes in telomere length and associated genetic parameters in dairy cattle analysed using random regression models. Telomeres cap the ends of linear chromosomes and shorten with age in many organisms.
In humans short telomeres have been linked to morbidity and mortality. With the accumulation of longitudinal datasets the focus shifts from investigating telomere length TL to exploring TL change within individuals over time.
Some studies indicate that the speed of telomere attrition is predictive of future disease. The objectives of the present study were to 1 characterize the change in bovine relative leukocyte TL RLTL across the lifetime in Holstein Friesian dairy cattle, 2 estimate genetic parameters of RLTL over time and 3 investigate the association of differences in individual RLTL profiles with productive lifespan. RLTL measurements were analysed using Legendre polynomials in a random regression model to describe TL profiles and genetic variance over age.
A quadratic Legendre polynomial was fitted to the fixed effect of age in months and to the random effect of the animal identity. Changes in RLTL, heritability and within-trait genetic correlation along the age trajectory were calculated and illustrated. At a population level, the relationship between RLTL and age was described by a positive quadratic function. Individuals varied significantly regarding the direction and amount of RLTL change over life.
The heritability of RLTL ranged from 0. The genetic correlation of RLTL at birth with measurements later in life decreased with the time interval between samplings from near unity to 0. Ilska, Joanna J. Element enrichment factor calculation using grain-size distribution and functional data regression. In environmental geochemistry studies it is common practice to normalize element concentrations in order to remove the effect of grain size.
Linear regression with respect to a particular grain size or conservative element is a widely used method of normalization. In this paper, the utility of functional linear regression , in which the grain-size curve is the independent variable and the concentration of pollutant the dependent variable, is analyzed and applied to detrital sediment. After implementing functional linear regression and classical linear regression models to normalize and calculate enrichment factors , we concluded that the former regression technique has some advantages over the latter.
First, functional linear regression directly considers the grain-size distribution of the samples as the explanatory variable. Second, as the regression coefficients are not constant values but functions depending on the grain size, it is easier to comprehend the relationship between grain size and pollutant concentration. Third, regularization can be introduced into the model in order to establish equilibrium between reliability of the data and smoothness of the solutions.
Predictors of success of external cephalic version and cephalic presentation at birth among women with non-cephalic presentation using logistic regression and classification tree analyses. Among women with a fetus with a non-cephalic presentation, external cephalic version ECV has been shown to reduce the rate of breech presentation at birth and cesarean birth. The purpose of this secondary analysis was to investigate factors associated with a successful ECV procedure and to present this in a clinically useful format.
The outcome of interest was successful ECV, defined as the fetus being in a cephalic presentation immediately following the procedure, as well as at the time of birth. The importance of several factors in predicting successful ECV was investigated using two statistical methods: logistic regression and classification and regression tree CART analyses. Among nulliparas, non-engagement of the presenting part and an easily palpable fetal head were independently associated with success.
These findings were consistent with results of the CART analyses. Regardless of parity, descent of the presenting part was the most discriminating factor in predicting successful ECV and cephalic presentation at birth. The minimal residual QR- factorization algorithm for reliably solving subset regression problems. A new algorithm to solve test subset regression problems is described, called the minimal residual QR factorization algorithm MRQR.
This scheme performs a QR factorization with a new column pivoting strategy. Basically, this strategy is based on the change in the residual of the least squares problem. Furthermore, it is demonstrated that this basic scheme might be extended in a numerically efficient way to combine the advantages of existing numerical procedures, such as the singular value decomposition, with those of more classical statistical procedures, such as stepwise regression. This extension is presented as an advisory expert system that guides the user in solving the subset regression problem.
The advantages of the new procedure are highlighted by a numerical example. Factor analysis and multiple regression are two major approaches used to look at functional age, which takes account of the extensive variation in the rate of physiological and psychological maturation throughout life. To examine the role of racial or cultural influences on the measurement of functional age, a battery of 12 tests concentrating on…. Factor complexity of crash occurrence: An empirical demonstration using boosted regression trees.
Factor complexity is a characteristic of traffic crashes. This paper proposes a novel method, namely boosted regression trees BRT , to investigate the complex and nonlinear relationships in high-variance traffic crash data. The Taiwanese single-vehicle motorcycle crash data are used to demonstrate the utility of BRT. Traditional logistic regression and classification and regression tree CART models are also used to compare their estimation results and external validities. Both the in-sample cross-validation and out-of-sample validation results show that an increase in tree complexity provides improved, although declining, classification performance, indicating a limited factor complexity of single-vehicle motorcycle crashes.
The effects of crucial variables including geographical, time, and sociodemographic factors explain some fatal crashes. Relatively unique fatal crashes are better approximated by interactive terms, especially combinations of behavioral factors. This study also discusses the implications of the results for devising safety policies. Determining factors influencing survival of breast cancer by fuzzy logistic regression model. Fuzzy logistic regression model can be used for determining influential factors of disease.
This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of The variables such as morphology, grade, age, and treatments surgery, radiotherapy, and chemotherapy were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership MDM.
Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients.
The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas. Logistic regression models of factors influencing the location of bioenergy and biofuels plants. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities.
Availability of "thinnings to a basal area of WALLY A large, principal components regression program with varimax rotation of the factor weight matrix. Written in Fortran IV and MAP, this computer program can handle up to variables, and retain 40 principal components. It can perform simultaneous regression of up to 40 criterion variables upon the varimax rotated factor weight matrix.
The columns and rows of all output matrices are labeled by six-character alphanumeric names. Data input can be from punch cards or Students' academic achievement cannot be separated from the influence of two factors namely internal and external factors. The first factors of the student internal factors consist of intelligence X1 , health X2 , interest X3 , and motivation of students X4. The external factors consist of family environment X5 , school environment X6 , and society environment X7.
Primary data are obtained by distributing questionnaires. Retrieving relevant factors with exploratory SEM and principal-covariate regression : A comparison. Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares OLS regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors.
Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights' estimates unstable i. Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables.
Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. Nucleic Acids Res. Anal Chem. J Cell Biol. J Neurosci. Mol Biol Cell. Mol Cell Biol. Clin Chem. J Immunol. J Exp Med. J Cell Sci. Nucleic Acids Res Suppl, Infect Immun. Am J Physiol Renal Physiol.
J Bacteriol. J Virol. Clin Cancer Res. Cancer Res. J Biomater Appl. Clin Diagn Lab Immunol. Mol Cell Proteomics. Cross-linking of the peptide to free Gbetagamma but not the heterotrimer. Colloids Surf B Biointerfaces. Circ Res. J Biochem. Appl Environ Microbiol. PMID: For Research Use Only. Not for use in diagnostic procedures. Sign in. Account Check Order Status. Chen H. Austin C. Chen Y. Windt W. Adams C. Akin B. Chen Z. Emara M. Gavazzi F. Gorna M.
Gruschke S. Hung C. Janganan T. Jennebach S. Jumper C. Kida Y. Layer G. Liu H. Loers G. Lucker B. Nguyen M. Padrick S. Pech M. Petrie R. Schulz C. Shi Y. Stan T. Watzlich D. Yao Y. Zhang H. Backman C. Batra-Safferling R. Chamow S. Ding B. Harvey S. Joyce J.
Karumuthil-Melethil S. Kilkenny M. Koulov A. Pallaoro A. Park B. Percy A. Richter C. Schmitz N. Solomon S. You H. Sheridan J. Meng F. Niemeyer C. Obara K. Boleij A. Chang T. Deiss K. Fenton R. Grinberg M. Ido K. Lobedanz S. Mamedova A.
Moore B. Ohnishi H. Ozvegy-Laczka C. Steiner H. Symmons M. Weaver M. Weber A. Zhao W. Zhu J. Ahmad R. Alian A. Aryal R. Auclair J. Beck R. Faye A. Fenyk S. Geula S. Gogada R. Goldoni S. Gosink K. Ishmael S. Jansma A.
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