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Hierarchical Regression for Multiple Comparisons in a Case-Control Study of Occupational Risks for Lung Cancer  [PDF]
Marine Corbin, Lorenzo Richiardi, Roel Vermeulen, Hans Kromhout, Franco Merletti, Susan Peters, Lorenzo Simonato, Kyle Steenland, Neil Pearce, Milena Maule
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0038944
Abstract: Background Occupational studies often involve multiple comparisons and therefore suffer from false positive findings. Semi-Bayes adjustment methods have sometimes been used to address this issue. Hierarchical regression is a more general approach, including Semi-Bayes adjustment as a special case, that aims at improving the validity of standard maximum-likelihood estimates in the presence of multiple comparisons by incorporating similarities between the exposures of interest in a second-stage model. Methodology/Principal Findings We re-analysed data from an occupational case-control study of lung cancer, applying hierarchical regression. In the second-stage model, we included the exposure to three known lung carcinogens (asbestos, chromium and silica) for each occupation, under the assumption that occupations entailing similar carcinogenic exposures are associated with similar risks of lung cancer. Hierarchical regression estimates had smaller confidence intervals than maximum-likelihood estimates. The shrinkage toward the null was stronger for extreme, less stable estimates (e.g., “specialised farmers”: maximum-likelihood OR: 3.44, 95%CI 0.90–13.17; hierarchical regression OR: 1.53, 95%CI 0.63–3.68). Unlike Semi-Bayes adjustment toward the global mean, hierarchical regression did not shrink all the ORs towards the null (e.g., “Metal smelting, converting and refining furnacemen”: maximum-likelihood OR: 1.07, Semi-Bayes OR: 1.06, hierarchical regression OR: 1.26). Conclusions/Significance Hierarchical regression could be a valuable tool in occupational studies in which disease risk is estimated for a large amount of occupations when we have information available on the key carcinogenic exposures involved in each occupation. With the constant progress in exposure assessment methods in occupational settings and the availability of Job Exposure Matrices, it should become easier to apply this approach.
Using Multiple Regression Analysis in Modelling the Role of Hospitality Industry in Cross River State
EI Eja, AO Ajake, JE Otu, BN Ndomah
African Research Review , 2011,
Abstract: The overwhelming and rapid growth of hospitality (hotel) industry is of great concern especially its role in the economy of Cross River State. This paper seeks to evaluate the contribution of hotel industry in each of the socioeconomic variables such as employment, revenue generation, urban development, tourism development and local economy development using multiple regression analysis. The result shows that 0.2, indicating 21.50% of the total variations in hospitality (hotel) industry have influenced socioeconomic development of Cross River State. The overall fit of the regression was 6.65 statistically significant at 1% confident coefficient level while 0.275 showed auto correction insignificant among the errors. However, findings have shown that even though much has not been felt from the hospitality (hotel) industry. The analysis shows that the industry has much to offer socioeconomically to the growth of Cross River State if properly managed.
An Application of the ABS Algorithm for Modeling Multiple Regression on Massive Data, Predicting the Most Influencing Factors  [PDF]
Soniya Lalwani, M. Krishna Mohan, Pooran Singh Solanki, Sorabh Singhal, Sandeep Mathur, Emilio Spedicato
Applied Mathematics (AM) , 2013, DOI: 10.4236/am.2013.46126
Abstract: Linear Least Square (LLS) is an approach for modeling regression analysis, applied for prediction and quantification of the strength of relationship between dependent and independent variables. There are a number of methods for solving the LLS problem but as soon as the data size increases and system becomes ill conditioned, the classical methods become complex at time and space with decreasing level of accuracy. Proposed work is based on prediction and quantification of the strength of relationship between sugar fasting and Post-Prandial (PP) sugar with 73 factors that affect diabetes. Due to the large number of independent variables, presented problem of diabetes prediction also presented similar complexities. ABS method is an approach proven better than other classical approaches for LLS problems. ABS algorithm has been applied for solving LLS problem. Hence, separate regression equations were obtained for sugar fasting and PP severity.
Comparing Lung Cancer Risks in Sweden, USA, and Japan  [PDF]
?rjan Hallberg,Olle Johansson
ISRN Oncology , 2012, DOI: 10.5402/2012/687298
Abstract: Objective. To develop a conceptual model for lung cancer rates to describe and quantify observed differences between Sweden and USA contra Japan. Method. A two-parameter lognormal distribution was used to describe the lung cancer rates over time after a 1-year period of smoking. Based on that risk function in combination with smoking prevalence, the calculated age-standardized rates were adjusted to fit reported data from Japan, Sweden, and the USA by parameter variation. Results. The risk of lung cancer is less in Japan than in Sweden and in the USA at the same smoking prevalence and intensity. Calculated age-specific rates did also fit well to reported rates without further parameter adjustments. Conclusions. This new type of cancer model appears to have high degree of predictive value. It is recommended that data from more countries are studied to identify important life-style factors related to lung cancer. 1. Introduction Prior to 1955, the relationship between smoking tobacco and lung cancer was not recognised as it is today. A 1955 Swedish encyclopaedia describes nervous problems, stomach problems, and throat problems, but not lung cancer, as possible results of heavy smoking [1]. However, after 1955, lung cancer mortality began to suddenly increase in Sweden, and older people in particular were subjected to a fast increasing risk of lung cancer. The aim of this study is to develop a conceptual model for predicting lung cancer rates. Such means may be useful in promoting successful preventive work against lung cancer. In cancer epidemiology, statistical models use linear and logistic regression to evaluate relationships between risk factors and cancer incidence. Less frequently used are biomathematical models translating a hypothesis about the biological process into mathematical terms [2]. There are differences between countries worldwide regarding lung cancer even if they have similar cigarette consumption per inhabitant and year [3]. This indicates that there are other causative factors, as well as different genetics, still not considered or yet identified in epidemiological studies of lung cancer. The model developed for this study was based on a similar model, successfully used for estimating melanoma rates over time, taking reduced repair efficiency of sun-induced skin damages into consideration [4]. In the current study, the model developed for lung cancer had to account for varying smoking prevalence over time. The main finding from this study is that the model with the parameters used was capable of predicting age-specific incidence for
Categorical Regression Models with Optimal Scaling for Predicting Indoor Air Pollution Concentrations inside Kitchens in Nepalese Households  [PDF]
Srijan Lal Shrestha
Nepal Journal of Science and Technology , 2009, DOI: 10.3126/njst.v10i0.2962
Abstract: Indoor air pollution from biomass fuels is considered as a potential environmental risk factor in developing countries of the world. Exposure to these fuels have been associated to many respiratory and other ailments such as acute lower respiratory infection, chronic obstructive pulmonary disease, asthma, lung cancer, cataract, adverse pregnancy outcomes, etc. The use of biomass fuels is found to be nearly zero in the developed countries but widespread in the developing countries including Nepal. Women and children are the most vulnerable group since they spend a lot of time inside smoky kitchens with biomass fuel burning, inefficient stove and poor ventilation particularly in rural households of Nepal. Measurements of indoor air pollution through monitoring equipment such as high volume sampler, laser dust monitor, etc are expensive, thus not affordable and practicable to use them frequently. In this context, it becomes imperative to use statistical models instead for predicting air pollution concentrations in household kitchens. The present paper has attempted to contribute in this regard by developing some statistical models specifically categorical regression models with optimal scaling for predicting indoor particulate air pollution and carbon monoxide concentrations based upon a cross-sectional survey data of Nepalese households. The common factors found significant for prediction are fuel type, ventilation situation and house types. The highest estimated levels are found to be for those using solid biomass fuels with poor ventilation and Kachhi houses. The estimated PM 10 and CO levels are found to be 3024 μg/m 3 and 24115 μg/m 3 inside kitchen at cooking time which are 5.2 and 40.40 times higher than the lowest predicted values for those using LPG / biogas and living in Pakki houses with improved ventilation, respectively. Key words: Biomass fuel; Categorical regression; Indoor air pollution; Optimal scaling; Respiratory ailments DOI: 10.3126/njst.v10i0.2962 Nepal Journal of Science and Technology Vol. 10, 2009 Page: 205-211 ?
Using Multiple Linear Regression and Artificial Neural Network Techniques for Predicting CCR5 Binding Affinity of Substituted 1-(3, 3-Diphenylpropyl)-Piperidinyl Amides and Ureas  [PDF]
Rokaya Mouhibi, Mohamed Zahouily, Khalid El Akri, Na?ma Hanafi
Open Journal of Medicinal Chemistry (OJMC) , 2013, DOI: 10.4236/ojmc.2013.31002

Quantitative structure–activity relationship (QSAR) models were developed to predict for CCR5 binding affinity of substituted 1-(3, 3-diphenylpropyl)-piperidinyl amides and ureas using multiple linear regression (MLR) and artificial neural network (ANN) techniques. A model with four descriptors, including Hydrogen-bonding donors HBD(R7), the partition coefficient between n-octanol and water logP and logP(R1) and Molecular weight MW(R7), showed good statistics both in the regression and artificial neural network with a configuration of (4-3-1) by using Bayesian and Leven-berg-Marquardt Methods. Comparison of the descriptor’s contribution obtained in MLR and ANN analysis shows that the contribution of some of the descriptors to activity may be non-linear.

Predicting the future of car manufacturing industry using Na ve Bayse Classifier  [PDF]
Sukhmeet Kaur,Kiran Jyoti
International Journal for Science and Emerging Technologies with Latest Trends , 2012,
Abstract: Data mining is a process that discovers interesting information from the hidden data which can either be used for future prediction and/or intelligently summarizing the details of the data. The applications of data mining in the field of predicting the future of any car manufacturing industry is gaining a lot of research interest now days. There are a number of research workers interested in this field. Among various data mining techniques regression, decision trees and na ve bayse algorithm is used for the prediction purposes. In this paper, na ve bayse algorithm is used for the prediction of future of number of cars which is useful for the car manufacturing industry. The prediction results are compared with the actual and real world values in order to validate the results obtained using na ve bayes alogorithm.
Classification and Regression Tree Analysis of Clinical Patterns that Predict Survival in 127 Chinese Patients with Advanced Non-small Cell Lung Cancer Treated by Gefitinib Who Failed to Previous Chemotherapy  [cached]
Ziping WANG,Jihong GUO,Yan WANG,Yutao LIU
Chinese Journal of Lung Cancer , 2011, DOI: 10.3779/j.issn.1009-3419.2011.09.04
Abstract: Background and objective It has been proven that gefitinib produces only 10%-20% tumor regression in heavily pretreated, unselected non-small cell lung cancer (NSCLC) patients as the second- and third-line setting. Asian, female, nonsmokers and adenocarcinoma are favorable factors; however, it is difficult to find a patient satisfying all the above clinical characteristics. The aim of this study is to identify novel predicting factors, and to explore the interactions between clinical variables and their impact on the survival of Chinese patients with advanced NSCLC who were heavily treated with gefitinib in the second- or third-line setting. Methods The clinical and follow-up data of 127 advanced NSCLC patients referred to the Cancer Hospital & Institute, Chinese Academy of Medical Sciences from March 2005 to March 2010 were analyzed. Multivariate analysis of progression-free survival (PFS) was performed using recursive partitioning, which is referred to as the classification and regression tree (CART) analysis. Results The median PFS of 127 eligible consecutive advanced NSCLC patients was 8.0 months (95%CI: 5.8-10.2). CART was performed with an initial split on first-line chemotherapy outcomes and a second split on patients’ age. Three terminal subgroups were formed. The median PFS of the three subsets ranged from 1.0 month (95%CI: 0.8-1.2) for those with progressive disease outcome after the first-line chemotherapy subgroup, 10 months (95%CI: 7.0-13.0) in patients with a partial response or stable disease in first-line chemotherapy and age <70, and 22.0 months for patients obtaining a partial response or stable disease in first-line chemotherapy at age 70-81 (95%CI: 3.8-40.1). Conclusion Partial response, stable disease in first-line chemotherapy and age ≥ 70 are closely correlated with long-term survival treated by gefitinib as a second- or third-line setting in advanced NSCLC. CART can be used to identify previously unappreciated patient subsets and is a useful method for dissecting complex clinical situations. Moreover, CART can be used to identify homogeneous patient populations in clinical practice and future clinical trials.
γ-H2AX: A Novel Prognostic Marker in a Prognosis Prediction Model of Patients with Early Operable Non-Small Cell Lung Cancer  [PDF]
E. Chatzimichail,D. Matthaios,D. Bouros,P. Karakitsos,K. Romanidis,S. Kakolyris,G. Papashinopoulos,A. Rigas
International Journal of Genomics , 2014, DOI: 10.1155/2014/160236
Abstract: Cancer is a leading cause of death worldwide and the prognostic evaluation of cancer patients is of great importance in medical care. The use of artificial neural networks in prediction problems is well established in human medical literature. The aim of the current study was to assess the prognostic value of a series of clinical and molecular variables with the addition of γ-H2AX—a new DNA damage response marker—for the prediction of prognosis in patients with early operable non-small cell lung cancer by comparing the γ-H2AX-based artificial network prediction model with the corresponding LR one. Two prognostic models of 96 patients with 27 input variables were constructed by using the parameter-increasing method in order to compare the predictive accuracy of neural network and logistic regression models. The quality of the models was evaluated by an independent validation data set of 11 patients. Neural networks outperformed logistic regression in predicting the patient’s outcome according to the experimental results. To assess the importance of the two factors p53 and γ-H2AX, models without these two variables were also constructed. JR and accuracy of these models were lower than those of the models using all input variables, suggesting that these biological markers are very important for optimal performance of the models. This study indicates that neural networks may represent a potentially more useful decision support tool than conventional statistical methods for predicting the outcome of patients with non-small cell lung cancer and that some molecular markers, such as γ-H2AX, enhance their predictive ability. 1. Introduction Prediction is one of the most interesting areas where intelligent systems are utilized [1]. In particular, prediction is an attempt to accurately forecast the evolution or outcome of a specific situation, using as input information a concrete set of variables that describe this situation [2]. In medicine, the valid and effective interpretation of medical data and the correct and early diagnosis along with a documented prognostic evaluation of the clinical and pathological data are very important parameters for a better management of the disease [3]. Prediction is a very difficult task because the expert human can hardly process the huge amount of data and usually suffers from absence of good and accurate analysis of these laboratory data [4, 5]. Lung cancer is the most common cause of cancer mortality worldwide for both men and women, causing approximately 1.2 million deaths per year. In the United States, there were 221.000
Principal component and multiple regression analysis predicting ozone concentrations: Case study in summer in Beijing

环境科学学报 , 2010,
Abstract: Data of atmospheric pollutants and meteorological variables in the summer of two stations of Beijing were employed to analysis the relationship between ozone precursors and meteorological parameters and to predict the concentration of ozone in the atmosphere using both multiple linear regression and Principal Component Analysis (PCA) methods. For both sites the pollutants (NO and NO2) and meteorological parameters (air temperature, relative humility) were highly correlated with ozone. We found that simple stepwise regression analysis fails to build accurate regression equations owing to the existence of multicollinearity among the independent variables. A variable selection method combination of PCA and stepwise regression analysis was used to obtain subsets of the predictor variables (ozone precursors and meteorological parameters) to produce a model free of the multicollinearity problem. The formulas were validated and have R2 values of the order of 0.78 (IAPs2007), 0.88 (IRSAs2007) and 0.64 (IAPs2005).
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