ADaM day 7 ADDV protocol deviation dataset identifies occurrences of sponsor-defined deviations, which is a list specified for every study. For example, the description of deviation can be Positive Drug Screen, PK labs not drawn in the proper window. A Sample ADDV Dataset key variables：subjid, dvstdt, dvcatcd, dvdeter, dvdesc , dvdact, dvact1, dvtype, dvstype. Key variable indications: dvcatcd=”DV Category Code”…
SAS day 51: Proc Contents Background story: One day, we need to search for a particular variable from 90 datasets. It would be a mission possible without Yajnes’s trick of Proc Contents.
SAS day 50: Background: One day, we need to use some datasets for an old study. The first task is to transfer the x.xpt files into x.sas file.
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.
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
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 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 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 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 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…