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SASInstitute A00-211 : SAS Base Programming for SAS (r) 9 Exam

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Test Number : A00-211
Test Name : SAS Base Programming for SAS (r) 9
Vendor Name : SASInstitute
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Exam ID : A00-211
Exam Name : SAS Base Programming for SAS (r) 9 (SASInstitute)
Questions : 40-45
Duration : 135 min.
Passing Scores : 725
Total Point Scores : 1000

Access data.
Access SAS data sets with the SET statement.
Use PROC IMPORT to access non-SAS data sources.
o Read delimited and Microsoft Excel (.xlsx) files with PROC IMPORT.
o Use PROC IMPORT statement options (OUT=, DBMS=, REPLACE)
o Use the GUESSINGROWS statement
Use the SAS/ACCESS XLSX engine to read a Microsoft Excel workbook.xlsx file.

Combine SAS data sets.
Concatenate data sets.
Merge data sets one-to-one.
Merge data sets one-to-many.
Create and manipulate SAS date values.
Explain how SAS stores date and time values.
Use SAS informats to read common date and time expressions.
Use SAS date and time formats to specify how the values are displayed.
Control which observations and variables in a SAS data set are processed and output.
Use the WHERE statement in the DATA step to select observations to be processed.
Subset variables to be output by using the DROP and KEEP statements.
Use the DROP= and KEEP= data set options to specify columns to be processed and/or output.

Sort observations in a SAS data set.
Use the SORT Procedure to re-order observations in place or output to a new dataset.
Remove duplicate observations with the SORT Procedure.
Conditionally execute SAS statements.
Use IF-THEN/ELSE statements to process data conditionally.
Use DO and END statements to execute multiple statements conditionally.
Use assignment statements in the DATA step.
Create new variables and assign a value.
Assign a new value to an existing variable.
Assign the value of an expression to a variable.
Assign a constant date value to a variable.
Modify variable attributes using options and statements in the DATA step.
Change the names of variables by using the RENAME= data set option.
Use LABEL and FORMAT statements to modify attributes in a DATA step.
Define the length of a variable using the LENGTH statement.
Accumulate sub-totals and totals using DATA step statements.
Use the BY statement to aggregate by subgroups.
User first. and last. processing to identify where groups begin and end.
Use the RETAIN and SUM statements.
Use SAS functions to manipulate character data, numeric data, and SAS date values.
Use SAS functions such as SCAN, SUBSTR, TRIM, UPCASE, and LOWCASE to perform
tasks such as the tasks shown below.
o Replace the contents of a character value.
o Trim trailing blanks from a character value.
o Search a character value and extract a portion of the value.
o Convert a character value to upper or lowercase.
Use SAS arithmetic, financial, and probability functions to create or modify numeric values by using the INT and ROUND functions.
Create SAS date values by using the functions MDY, TODAY, DATE, and TIME.
Extract the month, year, and interval from a SAS date value by using the functions YEAR, QTR, MONTH, and DAY.
Perform calculations with date and datetime values and time intervals by using the functions INTCK, INTNX, DATDIF and YRDIF

Use SAS functions to convert character data to numeric and vice versa.
Explain the automatic conversion that SAS uses to convert values between data types.
Use the INPUT function to explicitly convert character data values to numeric values.
Process data using DO LOOPS.
Explain how iterative DO loops function.
Use DO loops to eliminate redundant code and to perform repetitive calculations.
Use conditional DO loops.
Use nested DO loops.
Restructure SAS data sets with PROC TRANSPOSE.
Select variables to transpose with the VAR statement.
Rename transposed variables with the ID statement.
Process data within groups using the BY statement.
Use PROC TRANSPOSE options (OUT=, PREFIX= and NAME=).
Use macro variables to simplify program maintenance.
Create macro variables with the %LET statement
Use macro variables within SAS programs.
Error Handling
Identify and resolve programming logic errors.
Use the PUTLOG Statement in the Data Step to help identify logic errors.
Use PUTLOG to write the value of a variable, formatted values, or to write values of all variables.
Use PUTLOG with Conditional logic.
Use temporary variables N and ERROR to debug a DATA step.
Recognize and correct syntax errors.
Identify the characteristics of SAS statements.
Define SAS syntax rules including the typical types of syntax errors such as misspelled keywords, unmatched quotation marks, missing semicolons, and invalid options.
Use the log to help diagnose syntax errors in a given program.

Generate list reports using the PRINT procedure.
Modify the default behavior of PROC PRINT by adding statements and options such as
o use the VAR statement to select and order variables.
o calculate totals with a SUM statement.
o select observations with a WHERE statement.
o use the ID statement to identify observations.
o use the BY statement to process groups.
Generate summary reports and frequency tables using base SAS procedures.
Produce one-way and two-way frequency tables with the FREQ procedure.
Enhance frequency tables with options (NLEVELS, ORDER=).
Use PROC FREQ to validate data in a SAS data set.
Calculate summary statistics and multilevel summaries using the MEANS procedure
Enhance summary tables with options.
Identify extreme and missing values with the UNIVARIATE procedure.
Enhance reports system user-defined formats, titles, footnotes and SAS System reporting options.
Use PROC FORMAT to define custom formats.
o VALUE statement
o CNTLIN= option
Use the LABEL statement to define descriptive column headings.
Control the use of column headings with the LABEL and SPLIT=options in Proc Print output.
Generate reports using ODS statements.
Identify the Output Delivery System destinations.
Create HTML, PDF, RTF, and files with ODS statements.
Use the STYLE=option to specify a style template.
Create files that can be viewed in Microsoft Excel.
Export data
Create a simple raw data file by using the EXPORT procedure as an alternative to the DATA step.
Export data to Microsoft Excel using the SAS/ACCESS XLSX engine.



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SASInstitute test contents

Gamma-glutamyl transferase variability can predict the development of conclusion-stage of renal disorder: a nationwide inhabitants-primarily based look at | A00-211 test Questions and PDF Download

selection of look at subjects

We used the medical statistics supplied by using the Korean NHIC, which covers ~ 97% of the Koreans. The NHIC managed via the executive of Korea is the only medical health insurance system and has the fitness advice, together with an eligibility statistics (e.g., age, intercourse, income stage, and socioeconomic suggestions), health determine-up database (normal fitness assessments and questionnaires related to culture), medical medicine database (based on the scientific bills submitted through fitness carrier suppliers), and clinical care establishment database21,22. individuals blanketed within the NHIC are informed to do a standardized health verify-up each 1 to 2 years. The whole database is now open to authorised researchers.

We chose people who had passed through fitness examinations greater than once between January 1, 2009, and December 31, 2012 (observed baseline) and greater than twice within the 5 years automatically before the baseline examination (Fig. 1). amongst them, they excluded those aged younger than forty years and those with previous histories of liver cirrhosis, chronic hepatitis, liver malignancy, ESRD, or ingesting ≥ 30 g/day of alcohol (estimated via self-pronounced questionnaires used in the baseline fitness examination), together with those who had been lacking records in those inclusion standards. After those exclusions, 6,058,995 individuals (2,977,518 men and 3,081,477 women) were analyze subjects of current study (Supplementary Fig. S1).

figure 1

choice of the study subjects. GGT, gamma-glutamyl transferase.

The protocol of this analyze became authorized by the Institutional overview Board of the Korea tuition Ansan health facility (Institutional evaluation Board number 2018AS0161) and the reputable overview committee within the NHIC and was carried out in accordance with the Helsinki declaration of 1975.

Definition of GGT variability and GGT p.c trade

GGT variability become assessed using the average successive variability (ASV), regular deviation (SD), and CV of serial measurements of GGT right through the 5 years earlier than the baseline examination (GGT variability assessment length in Fig. 1). The GGT stage throughout the baseline examination become additionally blanketed within the calculation of GGT variability. the first fitness examination all the way through the 5 years before the baseline is known as the V1 examination.

GGT ASV became calculated the usage of here equation:

$$\textGGT\;\textASV = \fracn-1$$

\(x_i\) = each GGT value, \(n\) = number of GGT measurements (three to five instances per area).

study consequences

The endpoints have been the first analysis of ESRD, defined because the initiation of renal replacement therapy or kidney transplantation beneath international Classification of sickness, tenth Revision (ICD-10) codes N18-19, Z49, Z90, Z94, or Z99.2, and December 31, 201617. All dialysis techniques are reimbursed under registration in Korea, so they could identify all circumstances of renal replacement remedy using the declare codes for hemodialysis (O7011-O7020 or V001), peritoneal dialysis (O7071-O7075 or V003), and kidney transplantation (R3280)17. They excluded acute renal failure circumstances, defined as individuals with transiently undertaken renal replacement therapy, including acute peritoneal dialysis or continuous renal replacement remedy and not using a previous history of CKD. Deceased instances, defied the use of the nationwide demise certificates information from the Korea country wide Statistical workplace, had been considered as dropouts and excluded from the analyses. The time interval between the final examination between 2009 and 2012 and incident ESRD or December 31, 2016, is referred to as the follow-up period (Fig. 1).

Anthropometric and laboratory measurements

throughout every seek advice from, individuals accomplished a questionnaire addressing eligibility assistance, subculture habits, and scientific historical past. Cigarette smoking was categorised as on no account, ex-smokers, and existing smokers. Alcohol consuming reputation become stratified by near abstinence or moderate (alcohol < 30 g/day). Doing more than 20 min of full of life- or greater than 30 min of average-depth undertaking as a minimum once per week changed into described as ordinary exercise23.

topics whose body mass index (BMI) turned into greater than including 25 kg/m2 have been categorised as overweight, based on the revised Asia–Pacific standards of obesity24. Waist circumference changed into estimated at the middle aspect between the iliac crest and the rib cage. Blood pressure (BP) become measured the usage of a standardized sphygmomanometer after a 5-min relaxation.

A systolic BP ≥ a hundred and forty mmHg, diastolic BP ≥ ninety mmHg, or as a minimum one claim per 12 months for the prescription of antihypertensive medicinal drugs under ICD-10 codes I10–I15 become stratified as the presence of hypertension. Low-revenue status turned into identified on the lowest 20%.

After overnight fasting of > eight h, venous blood samples have been accumulated within the morning. Serum levels of fasting glucose, lipid profiles, aspartate aminotransferase (AST), alanine aminotransferase (ALT), GGT, creatinine, and hemoglobin had been measured. The estimated glomerular filtration fee (eGFR) the use of the change of weight loss program in Renal disease system turned into calculated25 and individuals whose eGFR became lower than 60 mL/min/1.73 m2 have been categorised as CKD26. The presence of diabetes become defined as a fasting glucose stage ≥ 7 mmol/L, or at least one prescription of anti-glycemic agents per year under ICD-10 codes E10–14 in response to the declare database. Dyslipidemia turned into described as TC ranges ≥ 6.21 mmol/L or at least one prescription historical past of anti-hyperlipidemic agents per yr beneath ICD-10 code E78. The people having nonalcoholic fatty liver disease (NAFLD) become identified the usage of the fatty liver index (FLI) ≥ 60, which is a noninvasive method to identify NAFLD in epidemiologic stories, together with the Asians27,28,29. The history of coronary heart ailment or stroke turned into decided with the aid of self-file. Admission episodes for heart failure or myocardial infarction were identified the usage of claim records for hospital admissions.

in response to the Korean affiliation of Laboratory excellent manage, the nice manage for the laboratory assessments changed into performed.

Statistical analysis

We stratified the subjects in keeping with their quartiles of baseline GGT and GGT ASV. based on each and every quartile and incident ESRD, they in comparison the baseline traits using t-assessments or analysis of variance for continual variables and chi-square checks for express variables. statistics are introduced as suggest ± SD, geometric suggest (ninety five% self belief intervals [CIs]), or number (%). For evaluation, ALT, AST, GGT, and triglyceride tiers were log-changed.

To estimate the risk for the construction of ESRD, they conducted Cox proportional hazard analyses in response to the quartiles of baseline GGT and GGT ASV, the usage of every quartile 1 as the reference. They adjusted for confounders at baseline the usage of three models. In model 1, is age, sex, eGFR, and BMI were adjusted. mannequin 2 is an identical as mannequin 1 plus alterations for reasonable ingesting, existing people who smoke, normal exercise, and the presence of hypertension, diabetes, and dyslipidemia. model three is extra adjusted for hemoglobin and salary within the lowest 20%. in the GGT variability analysis, they also adjusted for baseline GGT degree. moreover, they examined the results of GGT variability on ESRD in response to the baseline GGT quartiles to reveal that their consequences are consistent inspite of the GGT stage.

We carried out subgroup analyses after dividing the subjects by means of age, intercourse, obesity, eGFR, anemia, latest smokers, alcohol ingesting, income fame, presence of hypertension and diabetes. They obtained hazard ratios (HRs) and 95% CIs for ESRD in GGT ASV quartile four versus quartiles 1–3 using mannequin three except for the variables that categorised each and every subgroup.

We did sensitivity analyses via aside from the subjects who developed ESRD within 1 yr. They also repeated the same analyses the use of SD and CV to see whether they showed an identical outcomes. For the Cox proportional risks analyses, they performed the variable inflation element for all covariates of below 2.0, and they discovered that there become no significant multicollinearity among covariates. For statistical analysis, SAS edition 9.three (SAS Institute Inc., Cary, NC, united states of america) became used. A p-price of < 0.05 become regarded to be statistically tremendous.


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