SAS Algorithm Research Articles

Fuzzy regression model with Bayesian approach and its application to public health data

The application of the Bayesian Linear Regression (BLR) and Fuzzy Bayesian Linear Regression method through the SAS algorithm is the focus of this paper. As an alternative method of data analysis in biostatistics, this modified method can be used. This modified method includes a bootstrapping technique, residual normality checking and some Bayesian Linear Regression Modeling (BLR) enhancement through Fuzzy Bayesian Linear Regression. We illustrated the application of the algorithm for Bayesian Linear Regression (BLR) and Fuzzy Bayesian Linear Regression in this paper.

Application of SAS Algorithm in Responsive Submarine-searching Path Planning

Whether the planned path is optimal or not is an important condition for the completion of the responsive submarine-searching task, which depends on the applicability of the planning algorithm. According to the requirements of responsive submarine-searching mission of the anti-submarine aircraft, SAS algorithm used in responsive submarine-searching path planning is analysed in this paper, the relevant constraints are given, and the simulation calculation is carried out by using SAS algorithm. The results show that SAS algorithm has excellent performance in operation speed and planning effect, and it can better meet the training requirements.

Modeling for exponential growth and decay methodology in biometry using SAS syntax

This paper provided an alternative method for exponential growth modeling as a regression analysis technique through the SAS algorithm. This alternative method is a combination technique (using nonlinear model bootstrap and fuzzy regression) for the small data set and gives the researcher an option to start the analysis, even if there is not enough data set. This method enhances the previous methodology with embedded bootstrapping and fuzzy technique to a nonlinear regression model. This principle aims to propose an alternative method of analysis with better results. In our case, we applied this principle to farm data and compared the results obtained by looking at the average width of the predicted interval.

JMASM 54: A Comparison of Four Different Estimation Approaches for Prognostic Survival Oral Cancer Model

Four types of estimation approaches for prognostic survival oral cancer model building are considered via a SAS algorithm: Efron’s Method, Exact Method, Breslow’s Method, and Discrete Method. Each method is illustrated separately and compared according to their coefficient parameter. An approach is considered by adding a bootstrapping technique for each handling ties method and a complete SAS algorithm is supplied for each proposed method, including methods for handling ties.

The Incidence of Snow Blower-Related Injuries to the Hand in the United States.

Snow blowers represent a highly preventable and increasingly common mechanism of hand injury. This study examines current safety features and their impact on decreasing the incidence of snow blower-related injuries. The National Electronic Injury Surveillance System was queried to look for injuries related to the use of snow throwers or blowers between 2001 and 2016. From all of the injuries related to snow blowers, we collected information on identifying characteristics, location of injury, and type of injury (i.e., avulsion). Chi-squared tests were used for categorical variable comparisons, and Student t tests were used for continuous variable comparisons. Data analysis was performed using SAS statistical software, version 9.3 (SAS Institute, Inc., Cary, NC). The Consumer Product Safety Commission's provided SAS algorithm was used to calculate all national injury estimates and variances. Statistical significance was determined based on P < 0.05. Within the study period, there were 3,550 reported injuries. The extrapolated national incidence was 92,799, with an average annual incidence of 5,800 or 1.9 injuries per 100,000 US population per year. The most commonly injured body part was the finger followed by the hand. Most common types of injuries were fractures, lacerations, and amputations. The incidence of snow blower injuries increased from 2001 to 2016. Unlike with other power tools, Consumer Product Safety Commission-mandated guidelines for safer operation and improvements in equipment have not been successful in producing a decrease in the incidence of snow blower injuries to the upper extremity. Based on this, further equipment modifications are necessary and should be aimed at preventing operators from placing their hand into the exit chute while the machine is still running. Physicians should take an active role in their practice as well as in their professional societies to educate and counsel patients to prevent further injury.

MODIFIED ZERO INFLATED POISSON REGRESSION ANALYSIS AND ITS APPLICATION TO PUBLIC HEALTH DATA

This paper focuses on the programming of zero inflated Poisson regression (ZIPR) with combination of fuzzy regression method through SAS algorithm. This alternative method can be used as a tool of data analysis in various branches of science that include biostatistics, biometry. The method comprises of bootstrapping and nonlinear minimization programming through fuzzy linear regression modeling based on the dependent outcome of zero-inflated Poisson regression with several independent variables.

Cardiac Orienting to Auditory Stimulation in the Fetus

The objective of this study was to longitudinally evaluate the cardiac response to auditory stimulation in fetuses born during their 28th gestational week. A longitudinal, within-subjects design allowed for interpretations of the cardiac response tracked from 28 to 38 weeks gestational age (GA). All mothers recited a short passage from 28 to 34 weeks GA, and their fetuses were tested at 28, 32, 33, and 34 weeks GA. Following discontinuation of maternal recitation at 34 weeks GA, testing continued at 36 and 38 weeks GA. Experimental subjects were tested with a recording of a female stranger speaking the assigned passage and control subjects tested with a novel passage. The cardiac response was evaluated visually and statistically based on the magnitude and duration of the changes in heart rate. Visually, the cardiac response transitioned from a minimal magnitude (<5 beats per minute) with short duration (<5 seconds) cardiac deceleration in both experimental and control subjects during testing from 28 to 38 weeks GA and was confirmed statistically. For all experimental subjects, however, a long duration or sustained (>5 seconds) cardiac deceleration of greater magnitude (>5 beats per minute) was detected during 34-, 36-, or 38-week test session and was confirmed using a computational algorithm in SAS. Further investigation into additional forms of auditory stimulation at different developmental time periods is needed.

Statistical Modeling via Bootstrapping and Weighted Techniques Based on Variances

Multiple logistic regression is a methodology of handling dependent variables with a binary outcome. This method is becoming increasingly widespread as a statistical technique that represents a discrete probability model. Many studies have focused on the application but less on the methodology building. This study aims to provide an applied method for multiple logistic regression which is called modified Bayesian logistic regression modeling as an alternative technique for logistic regression analysis that focuses on a combination of the bootstrap method using SAS macro and weighted techniques based on variances using SAS algorithm. Data on oral cancer were applied to illustrate a real scenario of oral health data. This data will be applied to the multiple logistic regression algorithm and modified Bayesian logistic regression. Results from both cases are strongly supported by clinical studies. Through the proposed algorithm, the researcher will have an option whether to analyze the data with the usual or an alternative method. Final results indicate that the modified procedure can provide more efficient results especially for the case which involves statistical inferences.

Statistical Modeling via Bootstrapping and Weighted Techniques Based on Variances

Multiple logistic regression is a methodology of handling dependent variables with a binary outcome. This method is becoming increasingly widespread as a statistical technique that represents a discrete probability model. Many studies have focused on the application but less on the methodology building. This study aims to provide an applied method for multiple logistic regression which is called modified Bayesian logistic regression modeling as an alternative technique for logistic regression analysis that focuses on a combination of the bootstrap method using SAS macro and weighted techniques based on variances using SAS algorithm. Data on oral cancer were applied to illustrate a real scenario of oral health data. This data will be applied to the multiple logistic regression algorithm and modified Bayesian logistic regression. Results from both cases are strongly supported by clinical studies. Through the proposed algorithm, the researcher will have an option whether to analyze the data with the usual or an alternative method. Final results indicate that the modified procedure can provide more efficient results especially for the case which involves statistical inferences.

Forecasting Parameter Estimates: A Modeling Approach Using Exponential and Linear Regression

This paper supplies a calculation method for the parameter estimates of an exponential equation through SAS algorithm. The aim of this paper is to investigate the efficiency of the gained parameter estimates through the forecasting performance. The proposed calculation method can provide a very useful technique to develop an exponential equation with better accuracy performance. This research paper illustrates a sample of the data obtained from the established study, which characterize the proliferative capacity of mesenchymal stem cells. This paper also provides the specific algorithm for the parameter estimates.

Genome-wide association studies for small intestine length in an F2 population of chickens

Small intestine length is an important physiological index that is effected by nutrient intake and thus plays roles in growth and egg-laying in chickens. Although there are some studies about small intestine length, little information is available regarding the genetic architecture of small intestine. The current study was conducted to investigate the genetic architecture of small intestine length. A total of 1435 F2 hens from a White Leghorn and Dongxiang reciprocal cross were phenotyped for the duodenum lengths (DL), jejunum length (JL) and ileum length (IL), and genotyped using a chicken 600 K single nucleotide polymorphism (SNP) genotyping array. SNP-based heritability estimation was performed by SAS algorithm and univariate genome-wide association studies (GWAS) were performed by GEMMA, a genome-wide efficient mixed-model association algorithm. The JL and IL exhibited high SNP-based heritability estimation (0.43 and 0.49, respectively), while the heritability estimation was moderate for the DL (0.36). Three independent univariate genome-wide screens for these small intestine lengths identified 202, 298 and 119 SNPs that were significantly associated with the DL, JL and IL, respectively. The significant genomic regions indicated that ∼170 Mb on GGA1 is an important region for these small intestine lengths. In this region, 78 SNPs were associated with them, of which 4 were involved in cell proliferation and development, corresponding to RB1 (rs313207223), CKAP2 (rs312737959) and SIAH3 (rs312771221, rs15494052) genes. Small intestine length exhibited good SNP-based heritability estimation and the GWAS results indicated that an important genomic region was located on GGA1.

Development and validation of a structured query language implementation of the Elixhauser comorbidity index.

Comorbidity adjustment is often performed during outcomes and health care resource utilization research. Our goal was to develop an efficient algorithm in structured query language (SQL) to determine the Elixhauser comorbidity index. We wrote an SQL algorithm to calculate the Elixhauser comorbidities from Diagnosis Related Group and International Classification of Diseases (ICD) codes. Validation was by comparison to expected comorbidities from combinations of these codes and to the 2013 Nationwide Readmissions Database (NRD). The SQL algorithm matched perfectly with expected comorbidities for all combinations of ICD-9 or ICD-10, and Diagnosis Related Groups. Of 13 585 859 evaluable NRD records, the algorithm matched 100% of the listed comorbidities. Processing time was ∼0.05 ms/record. The SQL Elixhauser code was efficient and computationally identical to the SAS algorithm used for the NRD. This algorithm may be useful where preprocessing of large datasets in a relational database environment and comorbidity determination is desired before statistical analysis. A validated SQL procedure to calculate Elixhauser comorbidities and the van Walraven index from ICD-9 or ICD-10 discharge diagnosis codes has been published.

Service level agreement based adaptive Grid superscheduling

Grid computing brings heterogeneity and decentralization to the world of science and technology. It leverages every bit of idle computing resources and provides a straightforward middleware for integrating cross-domain scientific devices and legacy systems. In a super big Grid, job scheduling is challenging specifically when it needs to have access to vast amount of resources. The process of mapping jobs onto Grid resources requires significant consideration in terms of Grid architecture design, consumer demands and provider revenues. In this paper, we simultaneously utilize the legacy architecture of superscheduling, forwarding strategy, service level, success rate, and service pricing strategies and finally propose a service level agreement based on adaptive superscheduling (SAS) algorithm. SAS algorithm presents unified connectivity via efficient diffusion of jobs through the Grid infrastructure that is fueled from the previous scheduling events across the Grid. Moreover, by enforcing the service level agreement terms from a rich set of ask and bid prices, system performance, and load statistics, SAS successfully boosts revenue and utilization statistics. We perform an extensive experimental analysis for different Grid scales. Based on our experimental result, the SAS algorithm maximizes revenue while guarantees quality of service. More specifically, the quality of service is achieved through a high ratio of completed jobs and remarkable utilization of resources.

Determinants of Physical Activity in America: A First Characterization of Physical Activity Profile Using the National Health and Nutrition Examination Survey (NHANES)

To develop and implement methodologies for characterizing accelerometry-derived patterns of physical activity (PA) in the United States in relation to demographics, anthropometrics, behaviors, and comorbidities using the National Health and Nutrition Examination Survey (NHANES) dataset. Retrospective analysis of nationally representative database. Computer-generated modeling in silico. A total of 6329 adults in the United States from the NHANES 2003-2004 database. To discover subtle multivariate signal in the dynamic and noisy accelerometry data, we developed a novel approach, termed discretized multiple adaptive regression and implemented the algorithm in SAS 9.2 (SAS Institute, Cary, NC). Demographic, anthropometric, comorbidity, and behavioral variables. The intensity of PA decreased with both increased age and increased body mass index. Both greater education and greater income correlate with increased activity over short durations and reduced activity intensity over long durations. Numerous predictors demonstrated effects within activity ranges that may be masked by use of the standard activity intensity intervals. These include age, one of the most robust variables, where we discovered decreasing activities inside the moderate activity range. It also includes gender, where women compared with men have increased proportions of active times up to the center of light activity range, and income greater than $45,000, where a complex effect is seen with little correspondence to existing cut-points. The results presented in this study suggest that the method of multiple regression and heat map visualization can generate insights otherwise hidden in large datasets such as NHANES. A review of the provided heat maps reveals the trends discussed previously involving demographic, anthropometric, comorbidity, and behavioral variables. It also demonstrates the power of accelerometry to expose alterations in PA. Ultimately, this study provides a US population-based norm to use in future studies of PA.

A cluster randomized trial of utilizing a local change team approach to improve the delivery of HIV services in correctional settings: study protocol.

BackgroundPersons held in correctional facilities are at high risk for HIV infection and their prevalence of HIV is substantially higher than in the general population. Thus, the need for proper surveillance and care of this high risk population is a paramount public health issue. This study aims to evaluate an organization-level intervention strategy for improving HIV services for persons in prison or jail.Methods/DesignHIV Services and Treatment Implementation in Corrections (HIV-STIC) is using a cluster randomized trial design to test an organization-level intervention designed to implement improvements in preventing, detecting, and treating HIV for persons under correctional supervision. Matched pairs of prison or jail facilities were randomized using a SAS algorithm. Facility staff members in both Experimental and Control conditions involved in HIV service delivery are recruited to receive training on HIV infection, the HIV services continuum, and relevant web-based resources. Staff members in both conditions are tasked to implement improvements in HIV prevention, testing, or treatment in their facility. In the Control condition facilities, staff participants use existing techniques for implementing improvement in a selected area of HIV services. In contrast, the Experimental condition staff participants work as a Local Change Team (LCT) with external coaching and use a structured process improvement approach to improve a selected part of the HIV services continuum. The intervention period is 10 months during which data are obtained using survey instruments administered to staff members and aggregate services delivery data. The study is being implemented in 13 pairs of correctional facilities across nine states in the US. Experimental sites are hypothesized to show improvements in both staff attitudes toward HIV services and the number and quality of HIV services provided for inmates.DiscussionThe current study examines a range of process and outcome data relevant to the implementation of a Change Team approach across diverse correctional settings in the United States. This initial study represents an important step toward a national best practices approach to implementing change in U.S. correctional settings and could serve as an exemplar for designing similar implementation studies.Electronic supplementary materialThe online version of this article (doi:10.1186/2194-7899-1-8) contains supplementary material, which is available to authorized users.

Application of component-wise rank method to bivariate bioequivalence case

AbstractOne of the most common analyses in pharmaceutical research is bioequivalence test of two drugs. The FDA has endorsed the usage of Schuirmann's two one-sided hypotheses for the analyses in such studies. Generally, however, several measures on the drugs are taken simultaneously such that the data are multivariate. Nandakumar and McKean generalized Schuirmann's procedure to this multivariate setting. For bivariate data, the results can be summarized in a graphical display. These procedures are least-squares-type procedures and hence, are quite sensitive to mild outliers. To counter this sensitivity, Nandakumar and McKean also developed a simple highly efficient and robust analogue to their multivariate least-squares procedure. The robust results can also be displayed graphically, overlaid with the least-squares graphical results. In this paper, a SAS algorithm is presented, which implements these least-squares and robust multivariate tests for bioequivalence, including their graphical summaries.

C-A5-04: A Simple, Accurate SAS Algorithm for Electronic Abstraction of Race from Digitized Progress Notes

Background and Aims: Individual-level race/ethnicity is important for research into causes and consequences of health disparities. For various non-research reasons, it has rarely been collected on enrollees in integrated delivery systems. Individual-level race/ethnicity can be found in medical record documentation. Manual abstraction on large numbers of medical records is costly. We developed a simple SAS algorithm for electronic abstraction of white and African American race from digitized progress notes and evaluated its accuracy by comparing electronically abstracted race with other data sources. Methods: A simple SAS algorithm, based on text search strings (e.g. white male, African American woman), scanned digitized progress notes for provider face-to-face visits from 2005 through July 2009 in Kaiser Permanente Georgia’s (KPG) and Group Health Cooperative’s (GHC) electronic medical record systems. White and African American race was abstracted. If the patient had more than 1 visit with abstracted race, the patient was classified using the earliest visit. Abstracted race was linked at the individual-level to survey datasets with self-reported race (2005 survey of working age adults, 2007 survey of adults with hypertension, 2000–2005 Medicare surveys) and mother’s race on 2000–2006 birth certificates. White and African American race was abstracted from GHC progress notes from 2005 through July 2009 using the same algorithm and compared to self-reported race on health risk appraisals. Accuracy of the SAS algorithm was assessed by overall proportion matching race from the other datasets, Cohen’s kappa, and McNemar’s test. Results: White or African American race was electronically abstracted for 56,261 KPG and 6,427 GHC enrollees. Abstracted race matched race from the other datasets in 97–99% of enrollees. Cohen’s kappas were highly significant (p<0.05), ranging from 0.939 ± 0.013 (N=657 matches with hypertension survey records) to 0.994 ± 0.006 (N=518 matches with Medicare surveys). McNemar’s tests were marginally significant for several datasets; and, misclassification was not systematically biased toward white or African American race. Conclusions: The SAS algorithm was highly accurate in electronically abstracting white and African American race from digitized progress notes of provider visits at KPG and GHC. We are expanding the evaluation to include additional sites and additional race/ ethnic categories (e.g. Asian, Hispanic).

Quantitative structure–property relationships of camptothecins in humans

To develop quantitative structure property relationships (QSPR) for the pharmacokinetics and the susceptibility to BCRP-mediated efflux of ten drugs in the camptothecin family of topoisomerase I inhibitors. Pharmacokinetic parameters (total and lactone clearance, total steady-state volume of distribution, and lactone:total area under the curve ratio) and IC(50) values of cytotoxicity in both BCRP over-expressing and sensitive cell lines were extracted from the literature. Molecular descriptors were generated for both the lactone and carboxylic acid forms of the drugs using SYBYL and ACD/Labs software. A partial least squares algorithm in SAS was used to construct QSPR models for each of the properties of interest, and final models were validated using leave-one-out cross-validation. The molecular descriptors calculated for the lactone forms were better correlated with the selected properties than that of the carboxylate forms. Reasonable correlations (R(2) range 0.63-0.99) and good predictive performances (Q(2) range 0.45-0.88) were obtained for all seven QSPR models. Molecular descriptors that contribute to each pharmacokinetic property and susceptibility to BCRP mediated efflux were identified. QSPR models were successfully constructed for the pharmacokinetics and the susceptibility to BCRP mediated efflux of the camptothecin analogs. The identified molecular parameters may help guide the synthesis of new camptothecin analogs with improved pharmacokinetic properties and reduced potential for clinical resistance.

A rough set-based multiple criteria linear programming approach for the medical diagnosis and prognosis

It is well known that data mining is a process of discovering unknown, hidden information from a large amount of data, extracting valuable information, and using the information to make important business decisions. And data mining has been developed into a new information technology, including regression, decision tree, neural network, fuzzy set, rough set, and support vector machine. This paper puts forward a rough set-based multiple criteria linear programming (RS-MCLP) approach for solving classification problems in data mining. Firstly, we describe the basic theory and models of rough set and multiple criteria linear programming (MCLP) and analyse their characteristics and advantages in practical applications. Secondly, detailed analysis about their deficiencies are provided, respectively. However, because of the existing mutual complementarities between them, we put forward and build the RS-MCLP methods and models which sufficiently integrate their virtues and overcome the adverse factors simultaneously. In addition, we also develop and implement these algorithm and models in SAS and Windows system platforms. Finally, many experiments show that the RS-MCLP approach is prior to single MCLP model and other traditional classification methods in data mining, and remarkably improve the accuracy of medical diagnosis and prognosis simultaneously.

Holographic and conventional bistatic synthetic aperture sonar imaging of cylinders: Comparison and ray interpretation

When synthetic aperture sonar is used to image elastic targets, subtle features can be present in the images associated with the dynamical response of the target being viewed. In an effort to improve the understanding of such responses as well as to explore alternative image processing methods, a laboratory-based system was developed in which targets were illuminated by a transient acoustic source and bistatic responses were recorded by scanning a hydrophone along a horizontal line. Images were constructed using a relatively conventional bistatic SAS algorithm and were compared with images based on supersonic holography. The holographic method is a simplification of one previously used to view the time evolution of a target’s response [B. T. Hefner and P. L. Marston, ‘‘Acoustics Research Letters Online’’ 2, 55–60 (2001)]. In the holographic method, the time evolution of the cross-range image was used to construct a two-dimensional image with an effective range coordinate. Various features for cylindrical targets were interpreted using quantitative ray theory. This includes contributions from guided surface elastic waves as well as transmitted-wave features. [Work supported by the Office of Naval Research.]