Mean Regression Coefficient Research Articles

On Testing the Equality of Mean and Quantile Effects

Abstract This paper proposes tests for equality of the mean regression (MR) and quantile regression (QR) coefficients. The tests are based on the asymptotic joint distribution of the ordinary least squares and QR estimators. First, we formally derive the asymptotic joint distribution of these estimators. Second, we propose a Wald test for equality of the MR and QR coefficients considering a single fixed quantile, and also describe a more general test using multiple quantiles simultaneously. A very salient feature of these tests is that they produce asymptotically distribution-free nature of inference. In addition, we suggest a sup-type test for equality of the coefficients uniformly over a range of quantiles. For the estimation of the variance-covariance matrix, the use sample counterparts and bootstrap methods. An important attribute of the proposed tests is that they can be used as a heteroskedasticity test. Monte Carlo studies are conducted to evaluate the finite sample properties of the tests in terms of size and power. Finally, we briefly illustrate the implementation of the tests using Engel data.

Determinants of girls performance in science, mathematics and technology subjects in public secondary schools in Kenya

There has been incessant low academic performance in Science, Mathematics and Technology (SMT) subjects especially among girls at form four level in Kitui Central District over the years. The aim of this study was to investigate the determinants of girls’ performance in SMT subjects in public secondary schools. Using ex-post-facto survey research design, the study population covered all the six girls’ secondary schools in the district which also represented sample schools from which data was collected. Semi-structured questionnaires and document analysis sheets were used as instruments of data collection. The primary data collected was analysed by use of Statistical Package for Social Sciences (SPSS) version 17. Descriptive and inferential statistics were derived viz mean scores, percentages, correlation and regression coefficients. The Chi-square and one way ANOVA test statistics was used in hypothesis testing. Correlation and regression analysis were generated at 0.05 (2-tailed) and 0.001(2-tailed) significance levels. The findings from the study explained why form four girls in Kitui County were not doing well in SMT subjects. The outcome of the study is expected to influence policy and decision-making on girls’ better performance in the subjects. Different approaches were recommended based on the findings to improve girls’ performance in SMT subjects. Key words: determinants, girls’ performance, science, mathematics, technology, public secondary schools, Kenya.

Robust estimation for functional coefficient regression models with spatial data

A global smoothing procedure is developed using B-spline function approximation for estimating the unknown functions of a functional coefficient regression model with spatial data. A general formulation is used to treat mean regression, median regression, quantile regression and robust mean regression in one setting. The global convergence rates of the estimators of unknown coefficient functions are established. Various applications of the main results, including estimating conditional quantile coefficient functions and robustifying the mean regression coefficient functions are given. Finite sample properties of our procedures are studied through Monte Carlo simulations. A housing data example is used to illustrate the proposed methodology.

Effect of class size on girls' academic performance in Science, Mathematics and Technology subjects

There has been unsatisfactory academic performance in Science, Mathematics and Technology (SMT) subjects especially among girls over the years. The strategy of free basic education which aims at attaining education for all goals has led to enrolment of more girls in school resulting to overcrowded classrooms, limited resources and decreased quality of education. The aim of this study was to investigate the effect of class size on girls’ performance in SMT subjects. Data was collected using semi-structured questionnaires and document analysis. Ex-post-facto research design was adopted. Six girls’ secondary schools were purposively selected. The study comprised six head teachers, 30 SMT teachers and 416 girls. Data was presented in charts and tables and analysed using mean scores, percentages, correlation and regression coefficients. The study found that students in small class sizes performed better than those in large classes. Interposition strategies were proposed to improve girls’ performance in SMT subjects.

Longitudinal Association Analysis of Quantitative Traits

Longitudinal genetic studies provide a valuable resource for exploring key genetic and environmental factors that affect complex traits over time. Genetic analysis of longitudinal data that incorporate temporal variations is important for understanding genetic architecture and biological variations of common complex diseases. Although they are important, there is a paucity of statistical methods to analyze longitudinal human genetic data. In this article, longitudinal methods are developed for temporal association mapping to analyze population longitudinal data. Both parametric and nonparametric models are proposed. The models can be applied to multiple diallelic genetic markers such as single-nucleotide polymorphisms and multiallelic markers such as microsatellites. By analytical formulae, we show that the models take both the linkage disequilibrium and temporal trends into account simultaneously. Variance-covariance structure is constructed to model the single measurement variation and multiple measurement correlations of an individual based on the theory of stochastic processes. Novel penalized spline models are used to estimate the time-dependent mean functions and regression coefficients. The methods were applied to analyze Framingham Heart Study data of Genetic Analysis Workshop (GAW) 13 and GAW 16. The temporal trends and genetic effects of the systolic blood pressure are successfully detected by the proposed approaches. Simulation studies were performed to find out that the nonparametric penalized linear model is the best choice in fitting real data. The research sheds light on the important area of longitudinal genetic analysis, and it provides a basis for future methodological investigations and practical applications.

Permeability characteristics of pumice-bentonite mixtures in comparison with sand-bentonite mixtures

Environmental pollution is a significant concern around the landfill sites, waste repositories and other similar wastewater storage and treatment basin and ponds. Pumice is an abundant natural material in Kayseri, Turkey and therefore, permeability characteristics of pumice-bentonite mixtures were investigated in comparison with commonly used sand-bentonite mixtures. Four different bentonite contents (15, 20, 25 and 30%) and three grain size ranges (2.00 to 1.00; 1.00 to 0.50; 0.50 to 0.25 mm) of pumice and sand were used to determine the hydraulic conductivity of mixtures. Since it takes long time to determine the hydraulic conductivity of fine grained mixtures, empirical equations were developed between permeability and easily-determined geotechnical characteristics such as dry unit weight, moisture content, and swell potential of such mixtures. Pumice-based mixtures yielded identical permeability values for the same bentonite contents and size ranges. Overall evaluation of regression equations with regard to mean absolute error (MAE) and regression coefficients showed that while dry unit weight yielded the least MAE and the highest R2 values for pumice-based mixtures, swelling potential yielded the best estimations for sand-based mixtures. Bentonite content of ≥ 30% is recommended for both pumice and sand-based mixtures with grain sizes between 2.00 and 0.50 mm and ≥25% for mixtures with grain sizes less than 0.50 mm. Key words: Pumice, bentonite, mixtures, permeability.

Kinetics of Bacillus coagulans spore inactivation in tomato juice by combined pressure–heat treatment

The efficacy of combined pressure–heat treatment was evaluated for the inactivation of Bacillus coagulans spores in tomato juice. A spore crop of B. coagulans 185A was prepared on nutrient agar plus 500mgL−1 dextrose and 3mgL−1 MnSO4·H2O, incubated at 50°C for 7d. Spores were suspended (1.7×108CFUmL−1) in tomato juice (pH 4.23) and treated at 600MPa and process temperatures of 75, 85, 95, 100 and 105°C, or 0.1MPa and process temperatures of 100 and 105°C for ≤15min. The inactivation data was fitted to estimate kinetic inactivation parameters using selected linear and non-linear models. The models were evaluated using the mean square error, regression coefficient and accuracy factor. Depending upon the process temperature, a combined pressure–heat treatment reduced the spore population by 1.5–3.4 logs during the 30s pressure come-up time. Pressure holding times of 10, 7, 4 and 3min and 40s were sufficient to inactivate the spores to below the detection limit (<10CFUmL−1) at 600MPa and 75, 85, 95, 100 and 105°C, respectively with no microbial growth during subsequent storage of samples at 37°C. In contrast, the application of heat alone needed holding times of 13 and 4min, at 100 and 105°C, respectively to inactivate the spores to undetectable level with no growth during storage. The D-values of spores inactivated by combined pressure–heat treatment ranged from 0.19 (600MPa, 105°C) to 2.10min (600MPa, 75°C), while they were 1.66 and 0.59min at 100 and 105°C, respectively under atmospheric conditions. Kinetic model analysis indicated relatively good fits of linear and Weibull models to the pressure–heat inactivation data at process temperatures of 75–95°C. The log-logistic model provided best fitting at process temperatures of 95–105°C. This study suggests that combined pressure–heat treatment may be used as a viable alternative to inactivate B. coagulans spores in acidic food products, like tomato juice.

Genotype × environmental interactions for analyzing adaptability and stability in different clones of Dalbergia sissoo Roxb

In total 36 superior clones of Dalbergia sissoo Roxb., screened from 300 selections conducted in natural and growing range of India and Nepal, were multiplied using single nodal cuttings and established to evaluate genotypeenvironmental interactions for adaptability and stability at the age of 30 months in three geographical locations in the state of Punjab, India. Clone 124 had maximum adaptability and stability (bi = 1.04) to perform exceedingly well over the locations. Clones 36 and 1 were stable with mean regression coefficient of 0.84 and 1.22, respectively. Nonetheless, clone 41 performed exceedingly well for all the characters to attain maximum population mean, and the performance varied substantially across the locations. Therefore, clone 41 was considered as productive but non-adaptive clone. Though some of the clones were sensitive to sites, 14 clones for height, 16 for collar diameter, 12 for DBH and 7 for volume were relatively un-sensitive with higher regression coefficient. Nonetheless, clone 124 was the most stable with average bi value of 1.04 and productive, which could play an important role in future breeding and commercial deployment of stable and productive planting stock of Dalbergia sissoo.

Comparative methods for handling missing data in large databases: An empirical simulation

Introduction Analysis of large complex survey databases is an important research tool for health services researchers in Surgery. Missing data elements are challenging as reasons they are missing are multi-factorial, especially variables with multiple categories such as race. We simulated missing data for race and analyzed the bias from five missing data methods used in predicting major amputation in patients with critical limb ischemia (CLI). Methods Discharges with fully observed data containing lower extremity revascularization or major amputation and CLI were selected from the 2003-2007 Nationwide Inpatient Sample, a complex survey database (weighted N=684,057). Considering random and non-random missing data schemes, we simulated missing data for race to compare five missing data methods (complete case analysis (CCA), missing indicator variable (MIV), single imputation with mean frequencies, re-weighted estimating equations (RWEEs), and multiple imputation). Bias was determined by comparing mean regression coefficients estimated from 100 simulated datasets (B_miss) to those estimated from the fully observed dataset (B_full), with relative bias calculated as (B_full–B_miss/B_full)*100%. Results Our results demonstrate that RWEEs produce the least biased and MIV the most biased coefficients. CCA, single imputation, and multiple imputation resulted in moderate bias. Sensitivity analysis demonstrated that optimal method choice depends on quantity and type of missing data encountered. Conclusions Missing data is an important analytical topic in research with large databases. RWEEs are flexible and provided the least biased coefficients. The commonly used MIV method introduces the most bias and should be used with caution. We present empirical evidence to guide method selection for handling missing data.

Application of artificial neural network in estimating monthly time series reference evapotranspiration with minimum and maximum temperatures

There are various methods for computing reference evapotranspiration (ETo) using meteorological data. However, such models tend to perform well for predicting ETo close to the mean, but do not keep accurate performance with extreme observations. It is recognized that the Penman–Monteith (PM) model has the best performance when rich data is available to calculate the ETo, which is not frequently available to a certain extent. In case of poor data, such as prediction of futuristic ETo while investigating climate change effect, although there are models other than PM like Hargreaves–Samani (HGS), the universal sustainability of these models are not quit proved. Accordingly, the calculation of ETo still required numerous research to reach accurate estimation of ETo specially when there is lacking for data to utilize PM method. Recently, methods based on artificial intelligence (AI) have been suggested to provide reliable prediction model for several application in engineering. This manuscript employed artificial neural network (ANN) for predicting daily ETo at Rasht city located northern part of Iran using minimum and maximum daily temperatures collected from 1975 to 1988 of the region. A comprehensive data analysis utilizing the daily time series, minimum and maximum temperatures and solar radiation (T min, T max and R s), as input pattern to predict daily ETo at the current month and for the following month is proposed. The employed ANN model was feed forward backpropagation (FFBP) type with Bayesian regulation backpropagation. The mean square error, mean absolute error, mean absolute relative error and regression coefficient are the statistical performance indices used to evaluate the model accuracy. The results showed that the proposed ANN model could successfully be used to predict daily ETo using only maximum and minimum temperatures with significant level of accuracy. In addition, results show that the proposed ANN model outperforms HGS method.

A Conserved Aspartate Determines Pore Properties of Anion Channels Associated with Excitatory Amino Acid Transporter 4 (EAAT4)

Excitatory amino acid transporter (EAAT) glutamate transporters function not only as secondary active glutamate transporters but also as anion channels. Recently, a conserved aspartic acid (Asp(112)) within the intracellular loop near to the end of transmembrane domain 2 was proposed as a major determinant of substrate-dependent gating of the anion channel associated with the glial glutamate transporter EAAT1. We studied the corresponding mutation (D117A) in another EAAT isoform, EAAT4, using heterologous expression in mammalian cells, whole cell patch clamp, and noise analysis. In EAAT4, D117A modifies unitary conductances, relative anion permeabilities, as well as gating of associated anion channels. EAAT4 anion channel gating is characterized by two voltage-dependent gating processes with inverse voltage dependence. In wild type EAAT4, external l-glutamate modifies the voltage dependence as well as the minimum open probabilities of both gates, resulting in concentration-dependent changes of the number of open channels. Not only transport substrates but also anions affect wild type EAAT4 channel gating. External anions increase the open probability and slow down relaxation constants of one gating process that is activated by depolarization. D117A abolishes the anion and glutamate dependence of EAAT4 anion currents and shifts the voltage dependence of EAAT4 anion channel activation by more than 200 mV to more positive potentials. D117A is the first reported mutation that changes the unitary conductance of an EAAT anion channel. The finding that mutating a pore-forming residue modifies gating illustrates the close linkage between pore conformation and voltage- and substrate-dependent gating in EAAT4 anion channels.

Comparison of FFNN and ANFIS models for estimating groundwater level

Prediction of water level is an important task for groundwater planning and management when the water balance consistently tends toward negative values. In Maheshwaram watershed situated in the Ranga Reddy District of Andhra Pradesh, groundwater is overexploited, and groundwater resources management requires complete understanding of the dynamic nature of groundwater flow. Yet, the dynamic nature of groundwater flow is continually changing in response to human and climatic stresses, and the groundwater system is too intricate, involving many nonlinear and uncertain factors. Artificial neural network (ANN) models are introduced into groundwater science as a powerful, flexible, statistical modeling technique to address complex pattern recognition problems. This study presents the comparison of two methods, i.e., feed-forward neural network (FFNN) trained with Levenberg–Marquardt (LM) algorithm compared with a fuzzy logic adaptive network-based fuzzy inference system (ANFIS) model for better accuracy of the estimation of the groundwater levels of the Maheshwaram watershed. The statistical indices used in the analysis were the root mean square error (RMSE), regression coefficient (R2) and error variation (EV).The results show that FFNN-LM and ANFIS models provide better accuracy (RMSE = 4.45 and 4.94, respectively, R2 is 93% for both models) for estimating groundwater levels well in advance for the above location.

Pedotransfer functions related to spatial variability of water retention attributes for lowland soils

The estimation of non available soil variables through the knowledge of other related measured variables can be achieved through pedotransfer functions (PTF) mainly saving time and reducing cost. Great differences among soils, however, can yield non desirable results when applying this method. This study discusses the application of developed PTFs by several authors using a variety of soils of different characteristics, to evaluate soil water contents of two Brazilian lowland soils. Comparisons are made between PTF evaluated data and field measured data, using statistical and geostatistical tools, like mean error, root mean square error, semivariogram, cross-validation, and regression coefficient. The eight tested PTFs to evaluate gravimetric soil water contents (Ug) at the tensions of 33 kPa and 1,500 kPa presented a tendency to overestimate Ug 33 kPa and underestimate Ug1,500 kPa. The PTFs were ranked according to their performance and also with respect to their potential in describing the structure of the spatial variability of the set of measured values. Although none of the PTFs have changed the distribution pattern of the data, all resulted in mean and variance statistically different from those observed for all measured values. The PTFs that presented the best predictive values of Ug33 kPa and Ug1,500 kPa were not the same that had the best performance to reproduce the structure of spatial variability of these variables.

Adjustment for Missingness Using Auxiliary Information in Semiparametric Regression

In this article, we study the estimation of mean response and regression coefficient in semiparametric regression problems when response variable is subject to nonrandom missingness. When the missingness is independent of the response conditional on high-dimensional auxiliary information, the parametric approach may misspecify the relationship between covariates and response while the nonparametric approach is infeasible because of the curse of dimensionality. To overcome this, we study a model-based approach to condense the auxiliary information and estimate the parameters of interest nonparametrically on the condensed covariate space. Our estimators possess the double robustness property, i.e., they are consistent whenever the model for the response given auxiliary covariates or the model for the missingness given auxiliary covariate is correct. We conduct a number of simulations to compare the numerical performance between our estimators and other existing estimators in the current missing data literature, including the propensity score approach and the inverse probability weighted estimating equation. A set of real data is used to illustrate our approach.

Study of Genotype × Environment Interaction for Chickpea Yield in Iran

Plant breeders aim to select genotypes with stable phenotypes across environments. Multienvironment trials (METs) including 3 yr and six locations for 17 genotypes of chickpea (Cicer arietinum L.) were performed in Iran. Bartlett's test of homogeneity of variances was not significant and so the combined analysis was done. This analysis indicated that the environment (E), genotype (G), and the genotype by environment (GE) interaction were significant. Thus, stability analysis was necessary, and so additive main effects and multiplicative interaction (AMMI) stability parameters and some regression methods were used for a stability analysis. According to regression method, the high‐yielding genotype, G8, was more responsive (bi &gt; 1) to improved environmental conditions than the other genotypes. Stability parameters of Tai indicated that genotypes G3 and G17 and stability statistics of AMMI analyses showed that G2 and G3 were the most stable genotypes. The first and the second factors (F1 and F2) of factor analysis explained 73.84% total variance of stability parameters, and F1 separated the Di2 and αi from the other parameters. The F2 separated static and dynamic concepts of stability, in which the mean yield, regression coefficient, EVF, SIPCF, and AMGEF parameters had a dynamic concept of stability, and the other remaining parameters had static concept of stability. Finally, according to all stability statistics and regarding the high mean yield, genotype G8 (Flip 93‐93) was recommended for national release in Iran, as it adequately demonstrated wide adaptation across environments.

Fluoroquinoloneantibiotics determination in piggeries environmental waters

To trace the contamination and evaluate the environmental impact of piggeries, the occurrence of norfloxacin (NOR), ciprofloxacin (CIPRO), enrofloxacin (ENRO) and sarafloxacin (SARA) residues were evaluated in water for swine consumption, piggeries' wastewaters, drainage waters and a river receiving piggery effluents. Water samples were acidified before being percolated through Oasis HLB cartridges or through a tandem system consisting of strong anion-exchange and Oasis HLB cartridges. A liquid chromatography with fluorescence detection (LC-FD) method developed in previous studies, based on the use of a monolithic column, was successfully applied. NOR, CIPRO, ENRO and SARA had good linear responses, with mean regression coefficients higher than 0.9989. The limits of quantification (LOQ) for waters for swine consumption, and a river receiving piggery effluents, were 0.025 ug L(-1) for NOR, CIPRO and ENRO and 0.05 ug L(-1) for SARA. For wastewaters and drainage waters, the LOQs were 0.05 ug L(-1) for CIPRO and ENRO and 0.1 ug L(-1) for SARA. Recoveries obtained for spiked samples ranged from 70.8 to 89.1%, and precision data were under 12.7%. The method was successfully applied to 18 environmental water samples, collected from piggeries in Alentejo, Portugal, and the first Portuguese data are presented. ENRO was detected in a total of 8 samples, at concentrations ranging between 0.31 microg L(-1) and 63 microg L(-1). CIPRO, its degradation product, and SARA were present in 2 samples, in the ranges 0.48-0.83 microg L(-1) and 1.8-14 microg L(-1), respectively. No fluoroquinolones were detected in the waters for swine consumption or in the river water samples.

INACTIVATION KINETICS AND REDUCTION OF BACILLUS COAGULANS SPORE BY THE COMBINATION OF HIGH PRESSURE AND MODERATE HEAT

ABSTRACT The combination effect of high pressure (400, 500 and 600 MPa) and moderate heat (70 and 80C) on the inactivation kinetics and reduction of Bacillus coagulans spore in phosphate buffer and ultra‐high temperature (UHT) whole milk was investigated. The pressure come‐up time and corresponding logarithmic reduction of spore inactivation were considered during pressure‐thermal treatment. B. coagulans spore had a much higher resistance to pressure in UHT whole milk than in phosphate buffer. Survival data were modeled using the linear, Weibull and log‐logistic models to obtain relevant kinetic parameters. The tailing phenomenon occurred in all survival curves, indicating the linear model was not adequate for describing these curves. The mean square error and regression coefficient suggested that the log‐logistic model produced best fits to all survival curves, followed by the Weibull model.PRACTICAL APPLICATIONSIt becomes increasingly apparent that high‐pressure treatment combined with moderate heat treatment for low acid and acid products is often required for effective bacterial spores' inactivation. Consequently, the prediction model of microbial survival curves is essential. Bacillus coagulans is a slightly pressure‐resistant and relatively heat‐resistant spoilage bacterium of considerable concern during the processing of acid foods. Spore inactivation effect during the pressure come‐up time is sometimes considerable and should not beignored. The use of mathematical models to predict inactivation for spores could help the food industry further to develop optimum process conditions.

SU-FF-J-141: Volumetric Response Analysis During Chemoradiation as Predictive Tool for Optimizing Treatment Strategy in Locally Advanced Unresectable NSCLC

Background In the treatment of locally advanced non‐small cell lungcancer (LANSCLC) there is a trend towards concurrent chemoradiation because of the advantage on local control and survival (Auperin et al, IASLC 2007). Chemoradiation however is burdened by a marked increase in toxicity. This calls for a more patient‐tailored approach, trying to identify those patients that benefit from a more aggressive approach. We studied the feasibility of measuring volumetric changes in the primary tumor on tomotherapy MVCT, correlating with local response to assess its usefulness in reliable, early detection of patients with poor local therapy outcome. Methods Fifteen consecutive patients with stage III, inoperable LANSCLC were treated using helical tomotherapy. They were monitored for acute toxicity and evaluated with daily MVCT imaging. The tumor volume was delineated daily in a standardized way and the volumetric changes were fitted to a negative exponential resulting in a regression coefficient (RC). Local response evaluation was done using PET‐scan. Results The mean volume decrease (±standard deviation) was 77% (±18%). With a mean treatment time of 42 days this treatment schedule resulted in a mean decrease of 1.83%/day. Out of the 13 evaluable patients 7 developed a metabolic complete remission (MCR). The mean RC of the patients with MCR is 0.050 versus a mean RC of 0.023 in non‐responders (p=0.0074). Using a proposed cut‐off value for the RC of 0.03 80% of the non‐responders will be detected correctly while misclassifying 16.4% of patients who eventually achieve a MCR. Conclusions The RC derived from volumetric analysis of daily MVCT is prognostic and predictive for local response in patients treated with chemoradiation for a locally advanced NSCLC. Because of the possible toxicity of chemoradiation, MVCT can be a tool for the implementation of patient‐individualized treatment strategies.

Asymptotic Normality of M-Estimators for Varying Coefficient Models with Longitudinal Data

This article considers a nonparametric varying coefficient regression model with longitudinal observations. The relationship between the dependent variable and the covariates is assumed to be linear at a specific time point, but the coefficients are allowed to change over time. A general formulation is used to treat mean regression, median regression, quantile regression, and robust mean regression in one setting. The local M-estimators of the unknown coefficient functions are obtained by local linear method. The asymptotic distributions of M-estimators of unknown coefficient functions at both interior and boundary points are established. Various applications of the main results, including estimating conditional quantile coefficient functions and robustifying the mean regression coefficient functions are derived. Finite sample properties of our procedures are studied through Monte Carlo simulations.

Volumetric response analysis during chemoradiation as predictive tool for optimizing treatment strategy in locally advanced unresectable NSCLC

Purpose To study the feasibility of measuring volumetric changes in the primary tumor on megavoltage-computed tomography (MVCT) during chemoradiation and to examine the correlation with local response. Patients and methods Fifteen consecutive patients with stage III, inoperable, locally advanced non-small cell lung cancer (NSCLC) were treated in a prospective dose escalation study protocol of concurrent chemoradiation. They were monitored for acute toxicity and evaluated with daily MVCT imaging. The volumetric changes were fitted to a negative exponential resulting in a regression coefficient (RC). Local response evaluation was done with positron emission tomography using the radio-labeled glucose analogue F18 fluorodeoxyglucose (FDG-PET). Results The mean volume decrease (±standard deviation) was 73% (±18%). With a mean treatment time of 42 days this treatment schedule resulted in a mean decrease of 1.74%/day. Of the 13 evaluable patients seven developed a metabolic complete remission (MCR). The mean RC of the patients with MCR is 0.050 versus a mean RC of 0.023 in non-responders ( p = 0.0074). Using a proposed cut-off value for the RC of 0.03 80% of the non-responders will be detected correctly while misclassifying 16.4% of patients who will eventually achieve an MCR. The total cumulative percentage of esophageal grade 3 or more toxicity was 46.7%. Conclusion The RC derived from volumetric analysis of daily MVCT is prognostic and predictive for local response in patients treated with chemoradiation for a locally advanced NSCLC. Because this treatment schedule is toxic in nearly half of the patient population, MVCT is a tool in the implementation of patient-individualized treatment strategies.