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SASInstitute A00-240 : SAS Statistical Business Analysis SAS9: Regression and Model Exam

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Exam Number : A00-240
Exam Name : SAS Statistical Business Analysis SAS9: Regression and Model
Vendor Name : SASInstitute
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A00-240 test Format | A00-240 Course Contents | A00-240 Course Outline | A00-240 test Syllabus | A00-240 test Objectives


This test is administered by SAS and Pearson VUE.
60 scored multiple-choice and short-answer questions.
(Must achieve score of 68 percent correct to pass)
In addition to the 60 scored items, there may be up to five unscored items.
Two hours to complete exam.
Use test ID A00-240; required when registering with Pearson VUE.

ANOVA - 10%
Verify the assumptions of ANOVA
Analyze differences between population means using the GLM and TTEST procedures
Perform ANOVA post hoc test to evaluate treatment effect
Detect and analyze interactions between factors

Linear Regression - 20%
Fit a multiple linear regression model using the REG and GLM procedures
Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
Use the REG or GLMSELECT procedure to perform model selection
Assess the validity of a given regression model through the use of diagnostic and residual analysis

Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
Optimize model performance through input selection
Interpret the output of the LOGISTIC procedure
Score new data sets using the LOGISTIC and PLM procedures

Prepare Inputs for Predictive Model Performance - 20%
Identify the potential challenges when preparing input data for a model
Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
Improve the predictive power of categorical inputs
Screen variables for irrelevance and non-linear association using the CORR procedure
Screen variables for non-linearity using empirical logit plots

Measure Model Performance - 25%
Apply the principles of honest assessment to model performance measurement
Assess classifier performance using the confusion matrix
Model selection and validation using training and validation data
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
Establish effective decision cut-off values for scoring

Verify the assumptions of ANOVA
=> Explain the central limit theorem and when it must be applied
=> Examine the distribution of continuous variables (histogram, box -whisker, Q-Q plots)
=> Describe the effect of skewness on the normal distribution
=> Define H0, H1, Type I/II error, statistical power, p-value
=> Describe the effect of demo size on p-value and power
=> Interpret the results of hypothesis testing
=> Interpret histograms and normal probability charts
=> Draw conclusions about your data from histogram, box-whisker, and Q-Q plots
=> Identify the kinds of problems may be present in the data: (biased sample, outliers, extreme values)
=> For a given experiment, verify that the observations are independent
=> For a given experiment, verify the errors are normally distributed
=> Use the UNIVARIATE procedure to examine residuals
=> For a given experiment, verify all groups have equal response variance
=> Use the HOVTEST option of MEANS statement in PROC GLM to asses response variance

Analyze differences between population means using the GLM and TTEST procedures
=> Use the GLM Procedure to perform ANOVA
o CLASS statement
o MODEL statement
o MEANS statement
o OUTPUT statement
=> Evaluate the null hypothesis using the output of the GLM procedure
=> Interpret the statistical output of the GLM procedure (variance derived from MSE, Fvalue, p-value R**2, Levene's test)
=> Interpret the graphical output of the GLM procedure
=> Use the TTEST Procedure to compare means Perform ANOVA post hoc test to evaluate treatment effect

Use the LSMEANS statement in the GLM or PLM procedure to perform pairwise comparisons
=> Use PDIFF option of LSMEANS statement
=> Use ADJUST option of the LSMEANS statement (TUKEY and DUNNETT)
=> Interpret diffograms to evaluate pairwise comparisons
=> Interpret control plots to evaluate pairwise comparisons
=> Compare/Contrast use of pairwise T-Tests, Tukey and Dunnett comparison methods Detect and analyze interactions between factors
=> Use the GLM procedure to produce reports that will help determine the significance of the interaction between factors. MODEL statement
=> LSMEANS with SLICE=option (Also using PROC PLM)
=> ODS SELECT
=> Interpret the output of the GLM procedure to identify interaction between factors:
=> p-value
=> F Value
=> R Squared
=> TYPE I SS
=> TYPE III SS

Linear Regression - 20%

Fit a multiple linear regression model using the REG and GLM procedures
=> Use the REG procedure to fit a multiple linear regression model
=> Use the GLM procedure to fit a multiple linear regression model

Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
=> Interpret REG or GLM procedure output for a multiple linear regression model:
=> convert models to algebraic expressions
=> Convert models to algebraic expressions
=> Identify missing degrees of freedom
=> Identify variance due to model/error, and total variance
=> Calculate a missing F value
=> Identify variable with largest impact to model
=> For output from two models, identify which model is better
=> Identify how much of the variation in the dependent variable is explained by the model
=> Conclusions that can be drawn from REG, GLM, or PLM output: (about H0, model quality, graphics)
Use the REG or GLMSELECT procedure to perform model selection

Use the SELECTION option of the model statement in the GLMSELECT procedure
=> Compare the differentmodel selection methods (STEPWISE, FORWARD, BACKWARD)
=> Enable ODS graphics to display graphs from the REG or GLMSELECT procedure
=> Identify best models by examining the graphical output (fit criterion from the REG or GLMSELECT procedure)
=> Assign names to models in the REG procedure (multiple model statements)
Assess the validity of a given regression model through the use of diagnostic and residual analysis
=> Explain the assumptions for linear regression
=> From a set of residuals plots, asses which assumption about the error terms has been violated
=> Use REG procedure MODEL statement options to identify influential observations (Student Residuals, Cook's D, DFFITS, DFBETAS)
=> Explain options for handling influential observations
=> Identify collinearity problems by examining REG procedure output
=> Use MODEL statement options to diagnose collinearity problems (VIF, COLLIN, COLLINOINT)

Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
=> Identify experiments that require analysis via logistic regression
=> Identify logistic regression assumptions
=> logistic regression concepts (log odds, logit transformation, sigmoidal relationship between p and X)
=> Use the LOGISTIC procedure to fit a binary logistic regression model (MODEL and CLASS statements)

Optimize model performance through input selection
=> Use the LOGISTIC procedure to fit a multiple logistic regression model
=> LOGISTIC procedure SELECTION=SCORE option
=> Perform Model Selection (STEPWISE, FORWARD, BACKWARD) within the LOGISTIC procedure

Interpret the output of the LOGISTIC procedure
=> Interpret the output from the LOGISTIC procedure for binary logistic regression models: Model Convergence section
=> Testing Global Null Hypothesis table
=> Type 3 Analysis of Effects table
=> Analysis of Maximum Likelihood Estimates table

Association of Predicted Probabilities and Observed Responses
Score new data sets using the LOGISTIC and PLM procedures
=> Use the SCORE statement in the PLM procedure to score new cases
=> Use the CODE statement in PROC LOGISTIC to score new data
=> Describe when you would use the SCORE statement vs the CODE statement in PROC LOGISTIC
=> Use the INMODEL/OUTMODEL options in PROC LOGISTIC
=> Explain how to score new data when you have developed a model from a biased sample
Prepare Inputs for Predictive Model

Performance - 20%
Identify the potential challenges when preparing input data for a model
=> Identify problems that missing values can cause in creating predictive models and scoring new data sets
=> Identify limitations of Complete Case Analysis
=> Explain problems caused by categorical variables with numerous levels
=> Discuss the problem of redundant variables
=> Discuss the problem of irrelevant and redundant variables
=> Discuss the non-linearities and the problems they create in predictive models
=> Discuss outliers and the problems they create in predictive models
=> Describe quasi-complete separation
=> Discuss the effect of interactions
=> Determine when it is necessary to oversample data

Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
=> Use ARRAYs to create missing indicators
=> Use ARRAYS, LOOP, IF, and explicit OUTPUT statements

Improve the predictive power of categorical inputs
=> Reduce the number of levels of a categorical variable
=> Explain thresholding
=> Explain Greenacre's method
=> Cluster the levels of a categorical variable via Greenacre's method using the CLUSTER procedure
o METHOD=WARD option
o FREQ, VAR, ID statement

Use of ODS output to create an output data set
=> Convert categorical variables to continuous using smooth weight of evidence

Screen variables for irrelevance and non-linear association using the CORR procedure
=> Explain how Hoeffding's D and Spearman statistics can be used to find irrelevant variables and non-linear associations
=> Produce Spearman and Hoeffding's D statistic using the CORR procedure (VAR, WITH statement)
=> Interpret a scatter plot of Hoeffding's D and Spearman statistic to identify irrelevant variables and non-linear associations Screen variables for non-linearity using empirical logit plots
=> Use the RANK procedure to bin continuous input variables (GROUPS=, OUT= option; VAR, RANK statements)
=> Interpret RANK procedure output
=> Use the MEANS procedure to calculate the sum and means for the target cases and total events (NWAY option; CLASS, VAR, OUTPUT statements)
=> Create empirical logit plots with the SGPLOT procedure
=> Interpret empirical logit plots

Measure Model Performance - 25%
Apply the principles of honest assessment to model performance measurement
=> Explain techniques to honestly assess classifier performance
=> Explain overfitting
=> Explain differences between validation and test data
=> Identify the impact of performing data preparation before data is split Assess classifier performance using the confusion matrix
=> Explain the confusion matrix
=> Define: Accuracy, Error Rate, Sensitivity, Specificity, PV+, PV-
=> Explain the effect of oversampling on the confusion matrix
=> Adjust the confusion matrix for oversampling

Model selection and validation using training and validation data
=> Divide data into training and validation data sets using the SURVEYSELECT procedure
=> Discuss the subset selection methods available in PROC LOGISTIC
=> Discuss methods to determine interactions (forward selection, with bar and @ notation)

Create interaction plot with the results from PROC LOGISTIC
=> Select the model with fit statistics (BIC, AIC, KS, Brier score)
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
=> Explain and interpret charts (ROC, Lift, Gains)
=> Create a ROC curve (OUTROC option of the SCORE statement in the LOGISTIC procedure)
=> Use the ROC and ROCCONTRAST statements to create an overlay plot of ROC curves for two or more models
=> Explain the concept of depth as it relates to the gains chart

Establish effective decision cut-off values for scoring
=> Illustrate a decision rule that maximizes the expected profit
=> Explain the profit matrix and how to use it to estimate the profit per scored customer
=> Calculate decision cutoffs using Bayes rule, given a profit matrix
=> Determine optimum cutoff values from profit plots
=> Given a profit matrix, and model results, determine the model with the highest average profit



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SASInstitute Analysis Real test Questions

Mepolizumab or Placebo for Eosinophilic Granulomatosis with Polyangiitis | A00-240 cheat sheet and PDF Download

Trial Design

We carried out this randomized, placebo-controlled, double-blind, parallel-community, part three trial at 31 tutorial centers and hospitals throughout 9 nations (see the Supplementary Appendix, obtainable with the full text of this text at NEJM.org). The trial became a collaboration between GlaxoSmithKline and the countrywide Institute of allergic reaction and Infectious diseases (NIAID), national Institutes of health. NIAID funded an investigator-initiated clinical-trial application, through which 5 of eight clinical sites in the u.s. have been supported for participation during this trial. assistance related to the authors’ contribution (including the contribution of authors who are employees of GlaxoSmithKline) to the trial design, statistics assortment and analysis, and manuscript development is supplied in the Supplementary Appendix. the entire authors had entry to the information and vouch for the accuracy and integrity of the facts and analyses. Editorial aid (together with suggestions with constructing the initial draft of the manuscript, assembling tables and figures, collating authors’ comments, grammatical enhancing, and referencing) changed into offered by using Fishawack Indicia, funded through GlaxoSmithKline. The trial protocol is attainable at NEJM.org.

determine 1. determine 1. Trial Design and Randomization and follow-up of the individuals.

The continuation criteria (i.e., the standards required for present process randomization) included the following: glucocorticoid and immunosuppressive therapy balance (the dose had to be good for ≥four weeks earlier than randomization); desirable laboratory assessments, hepatitis repute, and liver-characteristic tests (see the Supplementary Appendix); and no clinically gigantic abnormality on electrocardiography. all the way through the intervention length, members in the mepolizumab group got 300 mg of mepolizumab plus regular care, and people within the placebo community bought matching placebo plus average care. Mepolizumab or placebo become administered subcutaneously. The intention-to-deal with population turned into used for the primary analysis.

After screening (which lasted 1 to four weeks), individuals were randomly assigned in a 1:1 ratio to get hold of subcutaneous mepolizumab at a dose of 300 mg or matching placebo each 4 weeks, besides ordinary care (glucocorticoid medication, with or without immunosuppressive therapy), for 52 weeks, followed through eight weeks of comply with-up (determine 1A, and desk S1 in the Supplementary Appendix). The glucocorticoid dose had to remain stable between baseline (randomization, which befell at discuss with 2) and week four and could thereafter be decreased at the investigator’s discretion in response to a standardized counseled tapering schedule. members who have been receiving immunosuppressive remedy have been required to be taking a stable dose before baseline and at some stage in the trial.

Randomization changed into carried out with using a centralized desktop–generated, permuted-block agenda, stratified according to three subgroups: participation in a mechanistic–biomarker substudy in the united states, recruitment in Japan, and the remainder of recruited contributors. Mepolizumab and placebo were prepared by using team of workers contributors who had been aware of the trial-neighborhood assignments however have been now not concerned in the trial assessments. The mepolizumab and placebo preparations were identical in appearance and have been administered in a blinded fashion. Clinicians who had been treating and evaluating individuals had been unaware of the education of the trial brokers, the trial-group assignments, and the white-cellphone counts and white-cellphone differential counts at some point of the trial.

The trial was performed according to the moral principles of the declaration of Helsinki, the foreign conference on Harmonisation first rate medical observe instructions, and the applicable country-particular regulatory necessities. all the participants provided written recommended consent.

individuals

Enrolled members had been at the least 18 years of age, had got a prognosis of relapsing or refractory eosinophilic granulomatosis with polyangiitis as a minimum 6 months prior to now, and had been taking a reliable dose of prednisolone or prednisone (≥7.5 to ≤50.0 mg per day, with or devoid of additional immunosuppressive therapy) for at the least 4 weeks earlier than the baseline consult with.25 Eosinophilic granulomatosis with polyangiitis became described as a background or presence of bronchial asthma, a blood eosinophil stage of 10% or an absolute eosinophil count of more than one thousand cells per cubic millimeter, and the presence of two or greater criteria which are usual of eosinophilic granulomatosis with polyangiitis (histopathological facts of eosinophilic vasculitis, perivascular eosinophilic infiltration, or eosinophil-wealthy granulomatous irritation; neuropathy; pulmonary infiltrates; sinonasal abnormality; cardiomyopathy; glomerulonephritis; alveolar hemorrhage; palpable purpura; or antineutrophil cytoplasmic antibody [ANCA] positivity, assessed on the Covance laboratory and Q2 options). members who had granulomatosis with polyangiitis or microscopic polyangiitis at screening were excluded, as had been those who had organ-threatening or existence-threatening eosinophilic granulomatosis with polyangiitis within three months earlier than screening. additional information concerning inclusion and exclusion standards, stopping standards, and definitions of relapsing and refractory disorder is equipped within the Supplementary Appendix.

conclusion facets

The trial had two fundamental efficacy end points. the primary basic conclusion point become the entire gathered weeks of remission. Remission turned into defined as a Birmingham Vasculitis pastime rating (BVAS), edition 3, of 0 (on a scale from 0 to sixty three, with larger rankings indicating enhanced disease activity)26 and the receipt of prednisolone or prednisone at a dose of four.0 mg or much less per day over the 52-week period. This conclusion element turned into stated because the proportions of members who had remission for 0 weeks, for more than 0 weeks however less than 12 weeks, for as a minimum 12 weeks however lower than 24 weeks, for at the least 24 weeks but lower than 36 weeks, and for as a minimum 36 weeks (specific quantification). The 2nd basic conclusion factor turned into the proportion of contributors who had remission at both week 36 and week forty eight.

Secondary end aspects had been the percentage of contributors who had remission in the first 24 weeks and persisted to have remission except week 52; the time to first relapse of eosinophilic granulomatosis with polyangiitis; and the proportions of individuals with an average prednisolone or prednisone dose of 0 mg per day, of greater than 0 mg to now not more than four.0 mg per day, of greater than 4.0 mg to now not greater than 7.5 mg per day, and of more than 7.5 mg per day throughout weeks forty eight through fifty two. The dose level of 4.0 mg or much less per day turned into used as part of the remission standards for the simple and secondary conclusion features, so given the eligibility standards, no contributors have been in remission at baseline with recognize to these conclusion facets. On the foundation of the ecu League in opposition t Rheumatism (EULAR) recommendations for clinical studies in systemic vasculitis,27 they used a 2d, much less stringent definition of remission of a BVAS of 0 and a prednisolone or prednisone dose of seven.5 mg or much less per day for here secondary conclusion points: total accrued weeks of remission over the 52-week length, the share of members who had remission at both week 36 and week forty eight, and the share of participants who had remission inside the first 24 weeks and persevered to have remission until week 52. since the individuals had been required to be taking at the least 7.5 mg per day of prednisolone or prednisone before inclusion within the trial, it become feasible for some of the individuals to have remission at baseline with respect to those end facets.

Relapse become defined as any of right here classes: energetic vasculitis (BVAS >0), active bronchial asthma symptoms or signs with a corresponding worsening within the score on the asthma control Questionnaire, version 6 (ACQ-6; latitude, 0 to 6 facets, with better rankings indicating worse ailment control; minimal clinically critical difference, 0.5 points),28,29 or lively nasal or sinus ailment with a corresponding worsening in as a minimum some of the sinonasal-symptom items resulting in one in all the following: an increase in the glucocorticoid dose to greater than four.0 mg per day of prednisolone (or equivalent), an initiation of or raise in immunosuppressive remedy, or hospitalization. Relapses can be classified by means of the local investigator as being of multiple type. particulars concerning the contraptions used and other prespecified end features (e.g., the alternate from baseline in the forced expiratory extent in 1 2d, forced vital skill, ACQ-6 score, Sino-nasal result test–22 rating [range, 0 to 110 points, with higher scores indicating worse quality of life; minimal clinically important difference, 8.9 points], vascular harm index score [range, 0 to 63 points, with a score of 0 indicating no damage], and BVAS cost) are supplied within the Supplementary Appendix. The annualized relapse rate (i.e., the estimated expense, defined because the number of relapses per year, from a negative binomial model) became also assessed. safety variables protected antagonistic events and serious adverse movements, including systemic and native injection-site reactions.

Statistical evaluation

The sample-size calculation was in keeping with the basic conclusion factor of the whole accumulated weeks of remission over the 52-week period. They calculated that an estimated demo of one hundred thirty members (sixty five participants in each neighborhood) would provide the trial with at the least 90% vigour to realize a major between-group change (at a two-sided P price of 0.05) of 29 percent elements in the percentage of contributors who had remission for at least 24 weeks. The vigor calculation used a check for proportions and assumed that 25% of the participants within the placebo group would have accumulated remission of at the least 24 weeks, as in comparison with 54% of these within the mepolizumab group. These values are akin to an odds ratio of three.5. To manage for class I error, the two simple end facets had to be effective to ensure that the trial to be regarded a hit. Per-protocol adjustment for distinctive comparisons is described in the Supplementary Appendix. In follow, no adjustment become required.

Efficacy end aspects had been assessed in the intention-to-treat population, which blanketed all of the participants who underwent randomization and received at least one dose of mepolizumab or placebo, and have been analyzed according to the randomized trial businesses. defense conclusion features were analyzed according to the genuine regimen bought. Ordered categorical records had been analyzed with using proportional-odds regression. For remission, an odds ratio of more than 1 would point out a higher share of members within the better remission-period classes and a reduce proportion of individuals in the reduce remission-period classes in the mepolizumab neighborhood than in the placebo group. For the reduction in the oral glucocorticoid dose, an odds ratio of under 1 would point out a better proportion of individuals in the lessen dose classes and a lessen share of contributors in the greater dose classes in the mepolizumab neighborhood than within the placebo community. Binary effects were analyzed with using logistic regression. Time to first relapse and time to first predominant relapse (defined as an organ- or existence-threatening experience or a BVAS ≥6 or an bronchial asthma or sinonasal relapse requiring hospitalization) were analyzed with the use of Cox proportional-hazards regression. Frequency of relapse and frequency of main relapse had been analyzed with using a bad binomial generalized linear model with a log-link function. effects that had been assessed at diverse time elements have been analyzed with using combined-model, repeated-measures strategies. The baseline dose of prednisolone or prednisone, the baseline BVAS, and geographic place (North the us, Europe, or Japan) were used as covariates in the analyses distinctive above. Demographic qualities (in the intention-to-deal with population) and protection conclusion facets (within the as-treated population) have been assessed by means of a t-verify for continuous variables and Fisher’s exact test for categorical variables.

participants who discontinued mepolizumab or placebo endured to be adopted unless the conclusion of the trial, when feasible, and all efficacy records were blanketed in the evaluation. all the analyses were performed with using SAS software, edition 9 (SAS Institute).




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