160 likes | 172 Vues
Learn how to simplify complex tumor xenograft study analyses in the pharmaceutical industry using R, without the need for advanced statistical expertise. Discover practical strategies and examples to improve decision-making and drug development.
E N D
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 • Example: Tumor Xenograft Study Analysis
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
Measure Tumor Volume Example: Tumor Xenograft Studies • Implant special tumor cell lines in mice, then compare tumor growth under different treatment regimens.
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
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)
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
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:
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.
Output: Model fit XXXXXXXX
Web Interface XXXXX
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 Bioinformatics Dana Caulder Vivek Ramaswamy Kathryn Woods