Multinomial Logistic Regression Method Research Articles

Care for the Elderly in Slovenia: A Combination of Informal and Formal Care

Most care provided to the elderly living at home comes from informal caregivers: family members, friends and neighbours. With the development of community services such as community healthcare, personal lifeline systems for the elderly or the panic button, home care and similar, informal care is enhanced by formal community forms of care. The data from the SHARE (Survey of Health, Ageing and Retirement in Europe) survey was used to estimate the number of people (over 65 years old) who receive an individual type of care (no care, only informal care, only formal care, a combination of the two), as well as the number of people who need care, but fail to receive it. The multinomial logistic regression method was also used to evaluate the factors that influence the type of care. Similar to other European countries, the need and the availability of informal caregivers have the strongest influence on the type of care, whereas the distribution of types of care mostly resembles Mediterranean countries.

Relationship of Socio Economic Factors (SEF) and Energy Conservation Behavior in India: Empirical Study

<div><p><em>In this research paper focus of the researcher is to study the effects of various socio economic factors on energy conservation level. This study discusses the energy conservation behaviors of rural and urban households and existence of barriers towards energy efficient appliances and effects of socio economic factors on energy related behaviors in the households located in Ahmednagar District, sample size was 567 households. The survey was conducted with help of structured questionnaire, and telephonic replies. Our result indicates that residence, education, religion and ownership of households are the major factors that have considerable effect on level of energy conservation behavior. We have tested hypothesis about all other factors and their relationships, an attempt has been made to predict the level of energy conservation by using multinomial logistic regression method. Particularly we want to find solution for questions like ‘how socio economic factors play its role in determining energy demands?’ ‘What are the barriers for energy conservation?’ ‘Can we predict energy conservation behavior?’.</em></p></div>

Association between gross motor function and nutritional status in children with cerebral palsy: a cross-sectional study from Colombia.

To determine the association between gross motor function and nutritional status in children with cerebral palsy (CP) residing in an urban area in a developing country. We conducted a cross-sectional study in 177 children (ages 2-12y, 59.3% male) with a diagnosis of CP who were attending rehabilitation centres in Bucaramanga, Colombia (2012-2013). A physiotherapist evaluated patients using the Gross Motor Function Classification System (GMFCS, levels I to V). Nutritional status was evaluated by nutritionists and classified according to the World Health Organization growth charts. We used linear and multinomial logistic regression methods to determine the associations. There were 39.5%, 6.8%, 5.6%, 16.4%, and 31.6% patients classified in levels I to V respectively. The mean adjusted differences for weight-for-age, height-for-age, BMI-for-age, and height-for-weight z-scores were significantly larger for children classified in levels II to V compared with those in level I. The children classified in levels IV and V were more likely to have malnutrition (adjusted odds ratio [OR] 5.64; 95% confidence interval [CI] 2.27-14.0) and stunting (OR 8.42; 95% CI 2.90-24.4) than those classified in GMFCS levels I to III. Stunting and malnutrition are prevalent conditions among paediatric patients with CP, and both are directly associated with higher levels of gross motor dysfunction.

Gender Differentials in Self-Rated Health and Self-Reported Disability among Adults in India.

BackgroundThe extant literature on gender differentials in health in developed countries suggests that women outlive men at all ages, but women report poorer health than men. It is well established that Indian women live longer than men, but few studies have been conducted to understand the gender dimension in self-rated health and self-reported disability. The present study investigates gender differentials in self-rated health (SRH) and self-reported disability (SRD) among adults in India, using a nationally representative data.MethodsUsing data on 10,736 respondents aged 18 and older in the 2007 WHO Study on Global Ageing and Adult Health in India, prevalence estimates of SRH are calculated separately for men and women by socio-economic and demographic characteristics. The association of SRH with gender is tested using a multinomial logistic regression method. SRD is assessed using 20 activities of daily living (ADL). Further, gender differences in total life expectancy (TLE), disability life expectancy (DLE) and the proportion of life spent with a disability at various adult ages are measured.ResultsThe relative risk of reporting poor health by women was significantly higher than men (relative risk ratio: 1.660; 95% confidence Interval (CI): 1.430–1.927) after adjusting for socio-economic and demographic characteristics. Women reported higher prevalence of severe and extreme disability than men in 14 measures out of a total20 ADL measures. Women aged less than 60 years reported two times more than men in SRD ≥ 5 ADLs. Finally, both DLE and proportion of life spent with a disability were substantially higher for women irrespective of their ages.ConclusionIndian women live longer but report poorer health than men. A substantial gender differential is found in self-reported disability. This makes for an urgent call to health researchers and policy makers for gender-sensitive programs.

Multinomial Logistic Regression Approach for the Evaluation of Binary Diagnostic Test in Medical Research

Abstract Evaluating the effect of variables on diagnostic measures (sensitivity, specificity, positive, and negative predictive values) is often of interest to clinical researchers. Logistic regression (LR) models can be used to predict diagnostic measures of a screening test. A marginal model framework using generalized estimating equation (GEE) with logit/log link can be used to compare the diagnostic measures between two or more screening tests. These individual modeling approaches to each diagnostic measure ignore the dependency among these measures that might affect the association of covariates with each diagnostic measure. The diagnostic measures are computed using joint distribution of screening test result and reference test result which generates a multinomial response data. Thus, multinomial logistic regression (MLR) is a more appropriate approach to modeling these diagnostic measures. In this study, the validity of LR and GEE approaches as compared to MLR model was assessed for modeling diagnostic measures. All methods provided unbiased estimates of diagnostic measures in the absence of any covariate. LR and GEE methods produced more biased estimates as compared to MLR approach especially for small sample size studies. No bias was obtained in predicting sensitivity measure using MLR method for one screening test. Our proposed MLR method is robust for modeling diagnostic measures of a screening test as opposed to LR method. MLR method and GEE method produced similar estimates of diagnostic measures for comparing two screening tests in large sample size studies. The proposed MLR model for diagnostic measures is simple, and available in common statistical software. Our study demonstrates that MLR method should be preferred as an alternative for modeling diagnostic measures.

Multinomial logistic regression approach for the evaluation of binary diagnostic test in medical research

Evaluating the effect of variables on diagnostic measures (sensitivity, specificity, positive, and negative predictive values) is often of interest to clinical researchers. Logistic regression (LR) models can be used to predict diagnostic measures of a screening test. A marginal model framework using generalized estimating equation (GEE) with logit/log link can be used to compare the diagnostic measures between two or more screening tests. These individual modeling approaches to each diagnostic measure ignore the dependency among these measures that might affect the association of covariates with each diagnostic measure. The diagnostic measures are computed using joint distribution of screening test result and reference test result which generates a multinomial response data. Thus, multinomial logistic regression (MLR) is a more appropriate approach to modeling these diagnostic measures. In this study, the validity of LR and GEE approaches as compared to MLR model was assessed for modeling diagnostic measures. All methods provided unbiased estimates of diagnostic measures in the absence of any covariate. LR and GEE methods produced more biased estimates as compared to MLR approach especially for small sample size studies. No bias was obtained in predicting sensitivity measure using MLR method for one screening test. Our proposed MLR method is robust for modeling diagnostic measures of a screening test as opposed to LR method. MLR method and GEE method produced similar estimates of diagnostic measures for comparing two screening tests in large sample size studies. The proposed MLR model for diagnostic measures is simple, and available in common statistical software. Our study demonstrates that MLR method should be preferred as an alternative for modeling diagnostic measures.

Predicting Unit Testing Effort Levels of Classes: An Exploratory Study based on Multinomial Logistic Regression Modeling

The study aims at investigating empirically the ability of a Quality Assurance Indicator (Qi), a metric that we proposed in a previous work, to predict different levels of unit testing effort of classes in object-oriented systems. To capture the unit testing effort of classes, we used four metrics to quantify various perspectives related to the code of corresponding unit test cases. Classes were classified, according to the involved unit testing effort, in five categories (levels). We collected data from two open source Java software systems (ANT and JFREECHART) for which JUnit test cases exist. In order to explore the ability of the Qi metric to predict different levels of the unit testing effort of classes, we decided to explore the possibility of using the Multinomial Logistic Regression (MLR) method. The performance of the Qi metric has been compared to the performance of three well-known source code metrics related respectively to size, complexity and coupling. Results suggest that the MLR model based on the Qi metric is able to accurately predict different levels of the unit testing effort of classes.

Spectral–Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization

Remotely sensed hyperspectral image classification is a very challenging task. This is due to many different aspects, such as the presence of mixed pixels in the data or the limited information available a priori. This has fostered the need to develop techniques able to exploit the rich spatial and spectral information present in the scenes while, at the same time, dealing with mixed pixels and limited training samples. In this paper, we present a new spectral–spatial classifier for hyperspectral data that specifically addresses the issue of mixed pixel characterization. In our presented approach, the spectral information is characterized both locally and globally, which represents an innovation with regard to previous approaches for probabilistic classification of hyperspectral data. Specifically, we use a subspace-based multinomial logistic regression method for learning the posterior probabilities and a pixel-based probabilistic support vector machine classifier as an indicator to locally determine the number of mixed components that participate in each pixel. The information provided by local and global probabilities is then fused and interpreted in order to characterize mixed pixels. Finally, spatial information is characterized by including a Markov random field (MRF) regularizer. Our experimental results, conducted using both synthetic and real hyperspectral images, indicate that the proposed classifier leads to state-of-the-art performance when compared with other approaches, particularly in scenarios in which very limited training samples are available.

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

Quasi-Markov Model for Extrapolating Observed Discrete Distributions

We present a quasi-Markov model of population dynamics calibrated to repeated cross-sectional data. While sharing the attractive features of classic Markov models, it avoids the practical problems associated with longitudinal data, and --- due to its focus on population transfer rates between discrete states --- it is particularly well adapted to microsimulation modelling. The proposed method of calculating the model estimation error based on information theory aims at recovering the maximum amount of information contained in the data. The application of the model to data on BMI in the UK and marijuana use in the USA produces reasonable extrapolations and confidence bounds, yielding important observations about the analysed variables. The results and properties of the model are compared with the commonly used multinomial logistic regression method. The thorough cross-validation of the models supports the advantages of using the proposed new approach.

An objective methodology for potential vegetation reconstruction constrained by climate

Reconstructions of modern Potential Natural Vegetation (PNV) are widely used in climate modelling and vegetation survey as a starting point for studies (historical changes of land-use, past or future vegetation distribution modelling, etc.). A PNV distribution is often related to vegetation models, which are based on empirical relationships between vegetation (or pollen data in paleoecological studies) and climate. Vegetation models are used to directly simulate a PNV distribution or to correct vegetation types derived from remotely-sensed observations in human-impacted regions. Consequently, these methods are quite subjective and include biases from models. This article proposes a new approach to build a high-resolution PNV map using a statistical model.As vegetation is a nominal variable, our method consists in applying a multinomial logistic regression (MLR). MLR build statistical relationships between BIOME 6000 data covering Europe and several climatological variables from the Climate Research Unit (CRU).The PNV reconstructed by MLR appears similar to those reconstructed from remotely-sensed data or simulated by a vegetation model (BIOME 4) except in southern Europe with the establishment of warm-temperate forests. MLR produces a realistic PNV distribution, which is the closest to BIOME 6000 data and provides the vegetation distribution in each grid-cell of our map. Moreover, MLR allows us to compute an uncertainty index that appears as a convenient tool to highlight the regions lacking some data toimprove the PNV distribution. The MLR method does not suffer any dynamic biases or subjective corrections and is a fast and objective alternative to the other methods. MLR provides an independent reference for vegetation models that is entirely based on vegetation and climatological data.

Measuring decision weights in recognition experiments with multiple response alternatives: Comparing the correlation and multinomial-logistic-regression methods

Psychophysical "reverse-correlation" methods allow researchers to gain insight into the perceptual representations and decision weighting strategies of individual subjects in perceptual tasks. Although these methods have gained momentum, until recently their development was limited to experiments involving only two response categories. Recently, two approaches for estimating decision weights in m-alternative experiments have been put forward. One approach extends the two-category correlation method to m > 2 alternatives; the second uses multinomial logistic regression (MLR). In this article, the relative merits of the two methods are discussed, and the issues of convergence and statistical efficiency of the methods are evaluated quantitatively using Monte Carlo simulations. The results indicate that, for a range of values of the number of trials, the estimated weighting patterns are closer to their asymptotic values for the correlation method than for the MLR method. Moreover, for the MLR method, weight estimates for different stimulus components can exhibit strong correlations, making the analysis and interpretation of measured weighting patterns less straightforward than for the correlation method. These and other advantages of the correlation method, which include computational simplicity and a close relationship to other well-established psychophysical reverse-correlation methods, make it an attractive tool to uncover decision strategies in m-alternative experiments.

Evaluating risk factors for endemic human Salmonella Enteritidis infections with different phage types in Ontario, Canada using multinomial logistic regression and a case-case study approach

BackgroundIdentifying risk factors for Salmonella Enteritidis (SE) infections in Ontario will assist public health authorities to design effective control and prevention programs to reduce the burden of SE infections. Our research objective was to identify risk factors for acquiring SE infections with various phage types (PT) in Ontario, Canada. We hypothesized that certain PTs (e.g., PT8 and PT13a) have specific risk factors for infection.MethodsOur study included endemic SE cases with various PTs whose isolates were submitted to the Public Health Laboratory-Toronto from January 20th to August 12th, 2011. Cases were interviewed using a standardized questionnaire that included questions pertaining to demographics, travel history, clinical symptoms, contact with animals, and food exposures. A multinomial logistic regression method using the Generalized Linear Latent and Mixed Model procedure and a case-case study design were used to identify risk factors for acquiring SE infections with various PTs in Ontario, Canada. In the multinomial logistic regression model, the outcome variable had three categories representing human infections caused by SE PT8, PT13a, and all other SE PTs (i.e., non-PT8/non-PT13a) as a referent category to which the other two categories were compared.ResultsIn the multivariable model, SE PT8 was positively associated with contact with dogs (OR=2.17, 95% CI 1.01-4.68) and negatively associated with pepper consumption (OR=0.35, 95% CI 0.13-0.94), after adjusting for age categories and gender, and using exposure periods and health regions as random effects to account for clustering.ConclusionsOur study findings offer interesting hypotheses about the role of phage type-specific risk factors. Multinomial logistic regression analysis and the case-case study approach are novel methodologies to evaluate associations among SE infections with different PTs and various risk factors.

Positive Family Social Support: Counteracting Negative Effects of Mental Illness and Substance Abuse to Reduce Jail Ex-inmate Recidivism Rates

The incarceration and recidivism rates in United States jails are marked by high mental health and substance use problems and racial disparity. Nearly 9 million people cycle through approximately 3,500 jails. For this longitudinal study to identify factors predicting recidivism, data came from a study of Allegheny County Jail inmates (N = 301) that concluded in 2008. Eligible participants were adult men, enrolled in jail collaborative services at recruitment, and were 30 days from release. Examinations were based on multinomial logistic regression methods; positive family social support was found to reduce the effect of factors known to predict higher recidivism rates: substance abuse, Black race, and younger age. Negative perceptions of the helpfulness and support of community-based services were counteracted by positive family social support. Policy and practice implications are discussed.

Permanent disability classification by combining evolutionary Generalized Radial Basis Function and logistic regression methods

Recently, a novelty multinomial logistic regression method where the initial covariate space is increased by adding the nonlinear transformations of the input variables given by Gaussian Radial Basis Functions (RBFs) obtained by an evolutionary algorithm was proposed. However, there still exist some problems with the standard Gaussian RBF, for example, the approximation of constant valued functions or the approximation of high dimensionality associated to some real problems. In order to face these problems, we propose the use of the generalized Gaussian RBF (GRBF) instead of the standard Gaussian RBF. Our approach has been validated with a real problem of disability classification, to evaluate its effectiveness. Experimental results show that this approach is able to achieve good generalization performance.

Corrigenda

BJU InternationalVolume 108, Issue 7 p. 1232-1232 Free Access Corrigenda This article corrects the following: Prediction of patient-specific risk and percentile cohort risk of pathological stage outcome using continuous prostate-specific antigen measurement, clinical stage and biopsy Gleason score Ying Huang, Sumit Isharwal, Alexander Haese, Felix K.H. Chun, Danil V. Makarov, Ziding Feng, Misop Han, Elizabeth Humphreys, Jonathan I. Epstein, Alan W. Partin, Robert W. Veltri, Volume 107Issue 10BJU International pages: 1562-1569 First Published online: September 28, 2010 First published: 13 September 2011 https://doi.org/10.1111/j.1464-410X.2011.10636.xAboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat In 1, the following errors appeared in the print issue BJU 107:10. On page 1562, inside the box “What's known on the subject? and What does the study add?”, the second line of the first paragraph, “(multinomial logistic regression method)”, should not have been published. The last sentence of the first paragraph, “applied the ‘predictiveness curve’ statistical modeling concept to the Partin Tables and then created a new Partin Nomogram using total PSA (tPSA) as a continuous variable” should have appeared as “created a new Partin Nomogram using total PSA (tPSA) as a continuous variable, and then generated the corresponding ‘predictiveness curve’.” On the 5th row up from the bottom of the box, “Fig. 2” should have appeared as “Fig. 4”. On page 1564, in Fig. 1, in the second row, “SV” should have appeared as “SV+”, and “LN” should have appeared as “LN+” ; in Table 2 on the same page, “SN+” should have appeared as “SV+”, and “LV+” should have appeared as “LN+”. On page 1566, in the first line of the third paragraph, “operator” should have appeared as “operating”; on the third line of the third paragraph on the same page, the sentence “For the JHH cohort and the UCHE cohort, the model with continuous PSA or discrete PSA appear to have similar capacity to classify a subject into its correct pathological stage.” should have been inserted after “… are shown in the Appendix.” On page 1567, in the first column, in the last line of the first paragraph, “clinical stage” should have appeared as “pathological stage”; in the second line up from the bottom of the second paragraph on the same page, “OC < pL (>pH)” should have appeared as “OC > pH (< pL)”. On page 1567, in the second column, in the first line of the first paragraph, “Fig. 3” should have appeared as “Fig. 4”. On page 1569, in the APPENDIX, “Receiver operator characteristic curves” should have appeared as “The receiver operating characteristic curves”. REFERENCE 1 Huang Y, Isharwal S, Haese A, Chun FKH, Makarov DV, Feng Z, Han M, Humphreys E, Epstein JI, Partin AW, Veltri RW. Prediction of patient-specific risk and percentile cohort risk of pathological stage outcome using continuous prostate-specific antigen measurement, clinical stage and biopsy Gleason score. BJU Int 2011; 107: 1562– 9. Wiley Online LibraryCASPubMedWeb of Science®Google Scholar Volume108, Issue7October 2011Pages 1232-1232 ReferencesRelatedInformation

Evaluation of the Risk Factors for Shedding Salmonella with or without Antimicrobial Resistance in Swine Using Multinomial Regression Method

A multinomial logistic regression method was used to investigate the risk factors for antimicrobial resistance (AMR) in Salmonella isolated from faecal samples collected on 80 Ontario swine farms in Canada. The samples were classified into three groups including Salmonella-negative samples (S-), Salmonella-positive samples without AMR (S+AMR-) and Salmonella-positive samples with AMR (S+AMR+). The samples collected directly from pigs had a greater chance to be positive for Salmonella with AMR compared to those samples collected from the pen floor. The odds of culturing Salmonella with or without AMR was higher if pelleted feed was used compared with mash or liquid feed (P < 0.001). The faecal samples collected on farrow-to-finish farms had a significant lower chance of testing positive for Salmonella with multidrug resistance than the samples from grow-finisher farms (P = 0.004). The chance of culturing Salmonella without AMR on farms with a continuous system was higher than on farms with an all-in/all-out system (P = 0.009). However, there was no significant association between the flow system and recovery of Salmonella with AMR. The larger farms were more likely to be in S+AMR+ group than in S- group (P < 0.001) whereas herd size did not appear as a risk factor for being in S+AMR- group compared with S- group. These findings indicate that although on-farm antimicrobial use is one component of resistance, there might be other farm management factors that also affect the development of emerging resistant bacterial foodborne pathogens on swine farms. Finding different risk factors for shedding Salmonella with or without antimicrobial resistance would help to take the appropriate approach to each group if a control programme were to be implemented or an intervention applied.

The accuracy of clinical symptoms and signs for the diagnosis of serious bacterial infection in young febrile children: prospective cohort study of 15 781 febrile illnesses

Objectives To evaluate current processes by which young children presenting with a febrile illness but suspected of having serious bacterial infection are diagnosed and treated, and to develop and test...

Investigation of farmers' willingness to pay for using recycled water for irrigation in Thessaly region, Greece

The objective of the present study was to investigate farmers' willingness to pay for using recycled water for irrigation purposes in Thessaly region, Greece. The method followed for the above analysis was the contingent valuation method which aims at eliciting individuals' preferences, in monetary terms, for changes in the quantity or quality of nonmarket environmental resources through personal interviewing. Our study was based on data collected from 107 questionnaires, which were answered by farmers in Thessaly region. The region is located in central eastern part of Greece and its main feature is that a relatively high percentage of its population is partly or fully employed in agriculture. Furthermore, Thessaly region suffers from a distinct water shortage in summer period, which is partly attributed to the increased agricultural activity. The group chosen for our study was random and the questionnaires were answered through personal interviews. The questionnaire was divided in 3 sessions. The first one dealt with personal data in order to investigate correlations between socioeconomic characteristics of interviewee and his or her willingness to use recycled water and to pay for it. The second session had as a goal to discover whether farmers are willing to use recycled water for irrigation purposes. Finally, the third session dealt with farmers' willingness to pay for treated wastewater. The data were statistically analyzed with the use of the multinomial logistic regression method. The main results show that Thessaly farmers are willing to pay for recycled water especially during drought spells in the region.

The Log Multinomial Regression Model for Nominal Outcomes with More than Two Attributes

An estimate of the risk or prevalence ratio, adjusted for confounders, can be obtained from a log binomial model (binomial errors, log link) fitted to binary outcome data. We propose a modification of the log binomial model to obtain relative risk estimates for nominal outcomes with more than two attributes (the "log multinomial model"). Extensive data simulations were undertaken to compare the performance of the log multinomial model with that of an expanded data multinomial logistic regression method based on the approach proposed by Schouten et al. (1993) for binary data, and with that of separate fits of a Poisson regression model based on the approach proposed by Zou (2004) and Carter, Lipsitz and Tilley (2005) for binary data. Log multinomial regression resulted in "inadmissable" solutions (out-of-bounds probabilities) exceeding 50% in some data settings. Coefficient estimates by the alternative methods produced out-of-bounds probabilities for the log multinomial model in up to 27% of samples to which a log multinomial model had been successfully fitted. The log multinomial coefficient estimates generally had lesser relative bias and mean squared error than the alternative methods. The practical utility of the log multinomial regression model was demonstrated with a real data example. The log multinomial model offers a practical solution to the problem of obtaining adjusted estimates of the risk ratio in the multinomial setting, but must be used with some care and attention to detail.