Funt ime Errors: Developing Together as a Virtual Team Jamieson - PowerPoint PPT Presentation

Funt ime Errors: Developing Together as a Virtual Team Jamieson Vaccaro, MA Patrick Racsa, MS June 20, 2018

Introduction • Description of Project – 6 Disease state studies (Lung Cancer, Breast Cancer, Multiple Sclerosis, Rheumatoid Arthritis, Hepatitis C, and HIV) – 3 Versions (Early View, Full Study, Subset Study) – 38 Metrics – 17 Tables (in Excel) • 9 Models – Grand total of : 648 Metrics & 252 Tables • Time period: Over one year balanced against other studies • SAS Enterprise Guide 7.1

Key Takeaways The Good - Efficiencies The Bad - Lessons Learned And The SAS Programmer - Attributes for success Source: https://xkcd.com/1513/

The Good - Macros Balance between automatic & manual Automatic Manual Pro Pro Typically easier to follow Rerunning is easier Code is there to see Reduce error Easier to code Faster maintenance Con Less code Prone to errors especially when doing Con iterative work Sometimes too easy to operate Initial build is time consuming Nuances in disease state

The Good - Macros (cont’d) Macro variables Aggregating key tables and variables in a “Control Panel” Variable names throughout scripting For model arguments Title output Macro functions Flexible - Summary statistics handle (StatsX) Simple - Model macros accept one argument Umbrella models with conditionals

The Good – Alignment Build consistency in: EGP / Excel File naming conventions 1 st description Variable timing SAS Tables Scripting framework cost_ip_ps cost_ip_pst Exceptions 2 nd description

The Good – Alignment (cont’d) Function – Short Description (permanent file name) Program Purpose Data – Building variables, putting data together %Macro - Custom Macros Joiner – Creating an final analytical file Testing – Testing out code, QC, not an essential part – provides a space for messy, unstructured code Analysis – Running statistical tests, output it generated at the end of these files Output – Optional, if there is major rework of the output, it can be put here --- Blank Place holders --- Indicate a shift in purpose within a process flow There is some flexibility here, sometimes it is best to combine 2 of these

The Good – SAS Scripting Framework Joiner Centralized merge script as control point prior to output %StatsX Macro Flexible macro to handle summary statistics for all levels of measurement Ease of uptake for other programmers Stacks all metrics into one table Streamlined efficient standardized output from SAS to Excel

Joiner StatsX macro %macro statsx(datafile,grp,ivar,type,test,whereclause,sigdigs);

StatsX output

The Bad - Lessons Learned Transitioning from loss of programmer Version control lacking Non-central location sharing Keeping track of your own file(s) until final Unspoken or spoken agreement of file gatekeeper

And The SAS Programmer - Attributes All interaction virtual Communication Medium: IM, screen share, phone, email Honesty Withholding pride Time Management Balancing against other workload Transience of focus Developing official documentation as guide up front with definitions Laugh!!

Key Takeaways Revisited The Good - Efficiencies The Bad - Lessons Learned And The SAS Programmer - Attributes for success Please reach out after our presentation and/or on LinkedIn!