This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

Brothers And Co Sas Table Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The table presents the percentage of problems where SAS-Pro performed better than, or at par with CE, SSM, and STSA. In addition, the table presents the average improvement in the RMSD, SI, SAS scores for these problems when SAS-Pro is used instead of other solvers.

SAS PROC used to evaluate SSMT data

File List NBvsPoi_FINAL.sas -- SAS code SSEAK98_FINAL.txt -- Harbor seal data used by SAS code Description The NBvsPoi_FINAL SAS program uses a SAS macro to analyze the data in SSEAK98_FINAL.txt. The SAS program and macro are commented for further explanation.

This database is the Third Small Astronomy Satellite (SAS-3) Y-Axis Pointed Observation Log. It identifies possible pointed observations of celestial X-ray sources which were performed with the y-axis detectors of the SAS-3 X-Ray Observatory. This log was compiled (by R. Kelley, P. Goetz and L. Petro) from notes made at the time of the observations and it is expected that it is neither complete nor fully accurate. Possible errors in the log are (i) the misclassification of an observation as a pointed observation when it was either a spinning or dither observation and (ii) inaccuracy of the dates and times of the start and end of an observation. In addition, as described in the HEASARC_Updates section, the HEASARC added some additional information when creating this database. Further information about the SAS-3 detectors and their fields of view can be found at: http://heasarc.gsfc.nasa.gov/docs/sas3/sas3_about.html Disclaimer: The HEASARC is aware of certain inconsistencies between the Start_date, End_date, and Duration fields for a number of rows in this database table. They appear to be errors present in the original table. Except for one entry where the HEASARC corrected an error where there was a near-certainty which parameter was incorrect (as noted in the 'HEASARC_Updates' section of this documentation), these inconsistencies have been left as they were in the original table. This database table was released by the HEASARC in June 2000, based on the SAS-3 Y-Axis pointed Observation Log (available from the NSSDC as dataset ID 75-037A-02B), together with some additional information provided by the HEASARC itself. This is a service provided by NASA HEASARC .

ABSTRACT. Genotype-by-environment interaction refers to the differential response of different genotypes across different environments. This is a general phenomenon in all living organisms and always has been one of the main challenges for biologists and plant breeders. The nonparametric methods based on the rank of original data have been suggested as the alternative methods after parametric methods to analyze data without perquisite assumptions needed for common analysis of variance. But, the lack of statistical software or package, especially for analysis of two-way data, is one of the main reasons that plant breeders have not greatly used the nonparametric methods. Here, we have explained the nonparametric methods and presented a comprehensive two-parts SAS program for calculation of four nonparametric statistical tests (Bredenkamp, Hildebrand, Kubinger and van der Laan-de Kroon) and all of the valid stability statistics including Hühn's parameters (Si(1), Si(2), Si(3), Si(6)), Thennarasu's parameters (NPi(1), NPi(2), NPi(3), NPi(4)), Fox's ranking technique and Kang's rank-sum.

Summary data for the studies used in the meta-analysis of local adaptation (Table 1 from the publication)This table contains the data used in this published meta-analysis. The data were originally extracted from the publications listed in the table. The file corresponds to Table 1 in the original publication.tb1.xlsSAS script used to perform meta-analysesThis file contains the essential elements of the SAS script used to perform meta-analyses published in Hoeksema & Forde 2008. Multi-factor models were fit to the data using weighted maximum likelihood estimation of parameters in a mixed model framework, using SAS PROC MIXED, in which the species traits and experimental design factors were considered fixed effects, and a random between-studies variance component was estimated. Significance (at alpha = 0.05) of individual factors in these models was determined using randomization procedures with 10,000 iterations (performed with a combination of macros in SAS), in which effect sizes a...

One of four dataset to replicate numbers for tables and figures in the article "Mammography screening: eliciting the voices of informed citizens" by Manja D. Jensen, Kasper M. Hansen, Volkert Siersma, and John Brodersen

It is commonly believed that if a two-way analysis of variance (ANOVA) is carried out in R, then reported p-values are correct. This article shows that this is not always the case. Results can vary from non-significant to highly significant, depending on the choice of options. The user must know exactly which options result in correct p-values, and which options do not. Furthermore, it is commonly supposed that analyses in SAS and R of simple balanced experiments using mixed-effects models result in correct p-values. However, the simulation study of the current article indicates that frequency of Type I error deviates from the nominal value. The objective of this article is to compare SAS and R with respect to correctness of results when analyzing small experiments. It is concluded that modern functions and procedures for analysis of mixed-effects models are sometimes not as reliable as traditional ANOVA based on simple computations of sums of squares.

This is the data set with SAS code that yielded the analyses in Table 1 of the paper.

The table CWP 2023 National Survey by Dist: SAs w Irr is part of the dataset Uganda Geodata, available at https://columbia.redivis.com/datasets/2he4-1tf2z5myv. It contains 135 rows across 38 variables.

This workshop takes you on a quick tour of Stata, SPSS, and SAS. It examines a data file using each package. Is one more user friendly than the others? Are there significant differences in the codebooks created? This workshop also looks at creating a frequency and cross-tabulation table in each. Which output screen is easiest to read and interpret? The goal of this workshop is to give you an overview of these products and provide you with the information you need to determine whick package fits the requirements of you and your user.

One of three dataset to replicate numbers for tables and figures in the article "Using a Deliberative Poll on breast cancer screening to assess and improve the decision quality of laypeople" by Manja D. Jensen, Kasper M. Hansen, Volkert Siersma, and John Brodersen

Summary This metadata record provides details of the data supporting the claims of the related manuscript: “Glembatumumab vedotin for patients with metastatic, gpNMB over-expressing, triplenegative breast cancer (“METRIC”): A Randomized Multicenter Study”. The related study evaluated whether glembatumumab vedotin (a gpNMB-specific monoclonal antibody conjugated to the potent cytotoxin monomethyl auristatin E) would improve progression-free survival compared to capecitabine in gpNMB-overexpressing triple-negative breast cancer (TNBC). Data access The data are are available from the company which funded the study--Celldex Therapeutics, Inc (https://www.celldex.com/)--on reasonable request for a period of 3 years following publication. After this period Celldex does not commit to making the data available as the compound has been discontinued from development. The data, along with details of the figures and tables in the manuscript that they underlie, are as follows: Figure 2: CDX011-04_ADEF.sas; CDX011-04_ADEFI.sas; CDX011-04_ADTTE.sas Figure 3: CDX011-04_ADEF.sas; CDX011-04_ADEFI.sas Figure 4: CDX011-04_ADEFI.sas; CDX011-04_ADTTE.sas Table 1: CDX011-04_ADSL.sas; CDX011-04_ADMH.sas Table 2: CDX011-04_ADEF.sas; CDX011-04_ADEFI.sas; CDX011-04_ADTTE.sas Table 3: CDX011-04_RawPK.xls Table 4: CDX011-04_ADAE.sas Supplemental Table 1: CDX011-04_ADCM.sas Supplemental Table 2: CDX011-04_ADEF.sas; CDX011-04_ADEFI.sas; CDX011-04_ADTTE.sas For all data requests please contact Celldex at: info@celldex.com Name of Institutional Review Board or ethics committee that approved the study The study was conducted at 120 sites in the United States, Canada, Australia, United Kingdom, France, Spain, Belgium, Germany, and Italy. The study was conducted at each of the participating institutions according to the Declaration of Helsinki and Good Clinical Practice Guidelines, after approval by local institutional/ethics review boards. Clinical trial registry URL https://www.clinicaltrials.gov/ct2/show/NCT01997333?term=NCT#01997333&draw=2&rank=1

File List Lamb_et_al_SAScode.txt Linumdata.txt Description The file Lamb_et_al_SAScode.txt contains SAS scripts and instructions for conducting nonlinear regression analyses of the Linum data set. The contents of the file can be pasted directly into the script editor in SAS. The file includes a script to import the Linum data set contained in the file Linumdata.txt into SAS. The file Linumdata.txt contains 4 columns and 40 rows (39 data points, one row with column headings). The columns in the data set are as follows: -- TABLE: Please see in attached file. --

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

Protein-Protein, Genetic, and Chemical Interactions for SAS (Drosophila melanogaster) curated by BioGRID (https://thebiogrid.org); DEFINITION: stranded at second

The global situation awareness system SAS market size is projected to grow from USD 33.76 billion in 2025 to USD 55.05 billion by 2033, exhibiting a CAGR of 6.3%.
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