1 / 16

SimpleR: Taking on the “Evil Empire” to Build Simple R Applications for Non-Statistical Users

SimpleR: Taking on the “Evil Empire” to Build Simple R Applications for Non-Statistical Users. Nicholas Lewin-Koh Bert Gunter Genentech Nonclinical Statistics. Outline:. Background and Context: The working environment and needs Strategy: The Approach

dagostino
Download Presentation

SimpleR: Taking on the “Evil Empire” to Build Simple R Applications for Non-Statistical Users

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. SimpleR: Taking on the “Evil Empire” to Build Simple R Applications for Non-Statistical Users Nicholas Lewin-Koh Bert Gunter Genentech Nonclinical Statistics

  2. Outline: • Background and Context: The working environment and needs • Strategy: The Approach • Example: Tumor Xenograft Study Analysis

  3. Context: Pharmaceutical industry, but regulation is not an issue • We collaborate on many projects that investigate drug efficacy, toxicity, biomarkers, dose determination, manufacturing methods, assay methods, etc. • Data may be complex, so analyses can be tricky. • We need to provide consistent, clear, interpretable analyses to aid scientific assessment • Complex statistical analyses are unsuitable

  4. Measure Tumor Volume Example: Tumor Xenograft Studies • Implant special tumor cell lines in mice, then compare tumor growth under different treatment regimens.

  5. Example: Tumor Xenograft Studies • Xenograft studies help determine which drugs to work on in which cancers, dosing in human studies, biomarkers that can identify subgroups who may or may not benefit, … • Data are challenging, consists of repeated measures of tumor volume over time per animal. • Nonlinear growth/stasis/shrinkage • dropouts due to toxicity or animal care requirements • left censoring when tumors shrink below LOD

  6. DRUG 1 DRUG 2 DRUG 2 DRUG 2 DRUG 3 Ad hoc analyses and plots using Excel are most widely used approaches Poor analyses compromise scientific decision making and our ability to find and develop good drugs. • Realities: • Scientists/engineers usually have neither the background nor time to learn and use sophisticated statistical methods • Wider audience of decision makers cannot consume fancy statistical results anyway • Not nearly enough of us (statisticians) to handle all of this for them (scientists and engineers)

  7. Context for Solutions • Rapid change – in technologies, needs, methods, computer hardware and software… • Need safe and robust methods: reasonable answers quickly in a variety of real circumstances, alert or failure otherwise. • Searching for statistical “optimality” is waste of time. • Communicate all results via graphs and tables. • Users will treat software as “black box” yielding answers. • User interface, not software documentation is key • Developers need to meet rapidly evolving user needs • Rapid prototyping, development, ease of modification, and feature addition are important factors

  8. Try Modify Review/ Test R provides a way to meet these challenges • Many built-in procedures and packages  rapid prototyping • Graphics packages (lattice, ggplot, …) ,provide framework for informative, flexible graphical displays • Changes the paradigm ! • Close collaboration with customers during development:

  9. Strategy • Initially, Windows desktop application on only very few (1 or 2) desktops • Simple menu interface automatically starts up when user clicks on R icon. • e.g. Use startup options to read in .RData file with all functions and execute code that sets up menus, etc. • We do it with .Rprofile file, but many alternatives are available • Once customers are satisfied and code has stabilized, port to Web-based interface to ease maintenance for larger user base • So far, we haven’t found the extra overhead for converting to packages worthwhile, but this may change. • Remember, for users it’s a black box that provides solutions, not a tool.

  10. Demo

  11. Menu Interface

  12. Output: Model fit XXXXXXXX

  13. Output: Views derived from the model. X X

  14. Web Interface XXXXX

  15. Summary: • Excel is ubiquitous data analysis software, so opportunities for major improvements abound. • To replace it, we need: • rapid development of flexible, robust solutions • “intelligent” graphs and tables to communicate results • Workable user interfaces that shield users from technical details • A way to scale solutions, that does not require a large ongoing effort to support • R and its supporting packages meet these needs.

  16. Thanks: Translational Oncology Bruno Alicke Steven Gould Bioinformatics Dana Caulder Vivek Ramaswamy Kathryn Woods

More Related