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

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

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

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

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

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)

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

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

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.

11/16/2018 Demo

11/16/2018 Menu Interface

11/16/2018 Output: Model fit XXXXXXXX

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

11/16/2018 Web Interface XXXXX

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.

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