1. 11/16/2018
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
11/16/2018
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
11/16/2018
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. Example: Tumor Xenograft Studies
11/16/2018
Example: Tumor Xenograft Studies
Implant special tumor cell lines in mice, then compare tumor growth under different treatment regimens.
Measure Tumor Volume

5. Example: Tumor Xenograft Studies
11/16/2018
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. Ad hoc analyses and plots using Excel are most widely used approaches
11/16/2018
Ad hoc analyses and plots using Excel are most widely used approaches
DRUG 3
DRUG 1
DRUG 2
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. 11/16/2018
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. R provides a way to meet these challenges
11/16/2018
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:
Review/
Test
Try
Modify

9. 11/16/2018
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. 11/16/2018
Demo

11. 11/16/2018
Menu Interface

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Output: Model fit
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13. Output: Views derived from the model.
11/16/2018
Output: Views derived from the model. X X

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Web Interface
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15. 11/16/2018
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
11/16/2018
Thanks:
Translational Oncology
Bruno Alicke
Steven Gould
Bioinformatics
Dana Caulder
Vivek Ramaswamy
Kathryn Woods