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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.
Background Story : One day, my friend 99 discussed with me if a patient is randomized but not treated has some lab results besides screening, how should we classify the EPOCH variable for him/her in SE domain? So today we will go over a basic example for SE domain: Subject Element. Purpose: The SE stores treatment data linked together with…
Background Story: The clinical study datasets usually have paired values for Character and Number, such as Sex and Sexn. However, sometimes the dataset only show the formatted values. If we don’t have the original code, we need to apply Proc Format procedure to transform format values to the original values and make appropriate selections for analysis purposes .
Background : We finally we over the 5 study design trials, TA, TE, TI, TV, TS. As we mentioned last time, TS acts like the outline of the puzzle, how can we fit the puzzles together? What are the relations between these five datasets? Rather than focusing on individual datasets, today we will connect the 5 trial design datasets together…
Background Story: Recently my friend 77 told me they checked Trial Summary dataset for multiple rounds and fixed many miscellaneous issues. I wasn’t aware of the importance of the Trial Summary dataset until my colleague Jun told me FDA had a meeting emphasized on Trial Summary dataset on May.21.2021. So today we will go over the basics for Trial Summary…
Background: Today we will go over Trial Inclusion/exclusion dataset. The TI datasets contains all the inclusion and exclusion criteria for the trial, such as subject age, lab or other findings, or maybe medical history. We can fetch the info from IE(inclusion/exclusion) dataset specifications or protocol. Note: TI is NOT subject-oriented.
Background Story: So today we will go over the essentials of the Trial Visit dataset, which include the planned visit in a trial in the structure of One record per planned Visit per Arm. It will have effects on SV (study visit) domain.
Background: Last time we went over TA , Today we will go over Trial Element dataset. Trial Element domain contains all the info regarding the Elements included in the study, therefore administration of planned trials use Element as the basic building block to describe the time periods without any gaps.(Screening, Treatment, Followup). Each planned Element will have a corresponding beginning…
Background : Trial Design Datasets include TA (Trial Arms), TE (Trial Elements), TV (Trial Visits), TI (Trial Inclusion/Exclusion Criteria) , TS (Trial Summary) . These 5 Trial Design domains provided clear description of overall plan and design of the study. Today we will go over the basics for Trial Arms, it has the structure of one record per planned element…
Background Story: Once, we need to check a define file, one of my colleagues found many miscellaneous issues, such as punctuation, typos in algorithms, and variable is not on the correct CRF pages. I was surprised by how meticulous he worked. Define file is the critical document while filing an FDA submission, so today we will go over the general…
Background Story: One day, my friend 77 asked me which one is more important ADRG (Analysis Data Reviewer’s Guide) or define.xml? I think define.xml focuses more on the ADaM dataset level, whereas ADRG provides an overview storyline for the study or additional info besides Define.xml. I decided to summarize the general format for the ADRG document.