SDTM in a nutshell

Beautifully explained the practical methods for creating CDISC SDTM tables:

https://support.sas.com/resources/papers/proceedings/pdfs/sgf2008/207-2008.pdf

Golden words: Analyses are "one proc away" from ADaM data. 

Other important SAS related references:

SAS clinical questions and answers: https://tekslate.com/sas-clinical-interview-questions-and-answers

There is a listing of sample graphs and example code: https://support.sas.com/en/knowledge-base/graph-samples-gallery.html

Details of Oncology studies and a relation between SDTM, ADaM and Controlled Terminology: https://www.pharmasug.org/proceedings/2018/DS/PharmaSUG-2018-DS06.pdf

Powerpoint slides details on ADaM tables and RECIST 1.1: https://www.cytel.com/hubfs/0-library-0/pdfs/CDISCJourneyonSolidTumorusingRECIST1.1.pdf

Again, it is a listing of sample graphs: http://support.sas.com/sassamples/graphgallery/PROC_SGPLOT.html

https://www.cdisc.org/system/files/all/event/restricted/2018_US/6B-CDISC-ADaM_Overview_-_Minjoe.pdf

https://www.cdisc.org/system/files/all/event/restricted/2017_International/INTX17%20Session%203%20Track%20A_Soloff.pdf

https://www.lexjansen.com/pharmasug/2018/DS/PharmaSUG-2018-DS24.pdf

https://www.lexjansen.com/pharmasug/2010/HW/HW06.pdf

https://www.quantics.co.uk/blog/an-introduction-to-integrated-summary-of-safety-and-integrated-summary-of-effectiveness-iss-and-ise/

https://blogs.sas.com/content/iml/2011/09/19/count-the-number-of-missing-values-for-each-variable.html

https://blogs.sas.com/content/iml/2012/04/02/count-missing-values-in-observations.html

https://journals.lww.com/anesthesia-analgesia/Fulltext/2018/09000/Survival_Analysis_and_Interpretation_of.32.aspx

https://www.lexjansen.com/pharmasug-cn/2014/CD/PharmaSUG-China-2014-CD03.pdf

https://www.lexjansen.com/phuse/2018/ds/DS03_ppt.pdf

Graphs from Robert Allison: https://robslink.com/SAS/Home.htm

About ADaM flags: https://www.pharmasug.org/proceedings/2013/PO/PharmaSUG-2013-PO11.pdf

http://www.stattutorials.com/SAS/

Oncology Survival Plot: https://support.sas.com/rnd/datavisualization/papers/Annotate_Your_SGPLOT_Graphs.pdfhttps://support.sas.com/rnd/datavisualization/papers/Annotate_Your_SGPLOT_Graphs.pdf

Survival analysis (Time-to-event analysis) is the process of measuring the length of time to the event. The event could be progress-free survival or overall survival or objective response rate. It may not be possible to measure the length of time for some patients because the patient disappeared or a bus hit him, or the study called off, etc. The missing data related to that patient is called the censored data.

Proc lifetest is useful to get the survival plot in a clinical trial. Also it provides multiple survival plots between two treatments. In the TIME statement, the survival time variable, Days, is crossed with the censoring variable, Status, with the value 0 indicating censoring.

The ADSL data structure has one record for one subject and contains subject-level population flags indicating whether subjects are in efficacy, safety, pharmacokinetic, pharmacodynamic, food effect, or dose proportionality analyses. In the Basic Data Structure (BDS) data sets, common record-level analysis flags include: recheck flags, flags for exclusion, baseline flags, early termination identifiers, and treatment-emergent flags.