Category: SASpphire 蓝宝石

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I have met so many wonderful people along my SAS journey, they are precious like Sapphire to me.

Now I am trying to organize the common SAS functions and procedures I have used on a daily basis, summarize the different CDISC domains that I have worked on and some statistical method I have encountered.

SAS Where Like

SAS day 49: Background story: Where doing safety analysis, sometimes we need to look into the drug relations with Special Interest Adverse Events, which are similar, such as “Glaucoma”  and “Glaucosis”. While investigating into these special interest events, instead of using AESI in (“x”, “y”,”z” ) we prefer a more robust way.

No Observation

SAS Day 48: Challenge: How to create a label for “No Observation” when there is no observation? Approach: We know SQL is the best tool for Counting and macro for generating all 0 counts 

Nadir Value

SAS day 47: Nadir value Definition: Nadir value is the lowest value of best response prior to current assessment or the shortest SLD (sum lesion diameter) along with the tumor measurement progress. Example: Measurements: 28,29,27,30,35. Nadir:28,28,27,27,27 Measurements:28,28,27,27,27.  Nadir:28,28,27,27,27

SAS Noodle Plot

SAS day 46: Noodle plots are line plots that may involve many overlapping lines. The line plot enables us to track the lab result for 1 patient over time, noodle plot shows the lab results for serval patients including the Median (Red Line).

SAS Dummy Dataset by Do Loop

SAS day 45: Do Loop While creating Tables in SAS, generate a dummy dataset for shell is necessary sometimes. All Roads lead to Rome, there are many ways to create the dummy shell dataset, but can we find an elegant way? Desired Dummy Dataset:   DO Loop+ Array Approach: We can use the Do Loop to create dummy observations (Rows)…

SAS Color Waterfall Plot

 SAS Day 44: Last time, we showed an example of Spider Plot for subjects response over time. Waterfall plot visualizes the best overall response in tumor size in each participating subjects oncology studies. Each of the bars in the plot describes the percentage of change (growth or reduction) in the target lesions as compared to the baseline measurements for each…

SAS Spider Plot

SAS Day 43: Spider Plot 🕷 Spider Plot is a powerful graph to visualize the change in percentage for critical values, such as Tumor size, lab values for individual patients over time. In the graph, each leg of the spider represents an individual patient, the horizontal reference time, Y-axis shows the baseline relative values. It demonstrates the percent change across…

Proc Transpose

SAS Day 42: Proc Transpose Proc Transpose is a powerful procedure for reshaping the data structures (i.e.Row observations to Column Variables or Vice Versa).  Key options: VAR, BY, ID, Prefix, name

ADaM. ADLB

ADaM DAY 2 : ADLB   Abstract: ADLB stands for Laboratory Test Result Analysis dataset is an important safety dataset, it captured all the Lab test results for each treatment cycle per patient. We can the Lab dataset to visualize the change of lab test results based on the time change for each patient (Spider Plot) or the overall lab-test distribution…

ADAM.ADAE

ADaM day 1: ADaM.ADAE stands for Adverse Event  Analysis dataset : This dataset is crucial for safety analysis in a new drug development progress as well as in post-market safety updates. it contains the information of ADSL, AE, SUPPAE. The dataset ADAE captured adverse event’s name, length, start and end date, relation to the drugs, actions to resolve the adverse…

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