Recent advances in design and analysis of screening experiments with applications in healthcare

1188 

Invited Paper Session 

Rakhi Singh (1), Rakhi Singh (1), John Stufken (2), Chunfang Lin (3), Jonathan Stallrich (4), Hongquan Xu (5)

(1) Binghamton University, N/A, (2) George Mason University, N/A, (3) Queen's University, N/A, (4) North Carolina State University, N/A, (5) University of California, Los Angeles, N/A

Rakhi Singh  
Binghamton University

Rakhi Singh  
Binghamton University

John Stufken  
George Mason University

Chunfang Lin  
Queen's University

Jonathan Stallrich  
North Carolina State University

Hongquan Xu  
University of California, Los Angeles

With the advancement in technology, science can finally pave the way to design experiments that satisfy human desire to test a large number of potential factors that can potentially affect the response behavior. Therefore, the design and analysis of such experiments (known as screening experiments) is becoming increasingly popular. Screening experiments are used when, with limited resources, essential factors need to be identified from a large pool of potential factors. The proposed talks in this session will discuss the theoretical developments in the design of such experiments, methodological advances in the analysis, as well as an application of such experiments in the field of healthcare, in particular, for choosing the drug combinations. The methods discussed in this session will be of direct use to scientists and domain experts to solve
statistical problems in manufacturing engineering, pharmaceutical industry, and other allied fields. As a result, the focus audience will be scientists working in experimental design, industrial engineering, and medical practitioners. By virtue of designing experiments that can help make the right decisions (instead of trying to analyze lots of arbitrarily collected data), this session can peripherally counter misinformation and is, therefore, connected to the broader JSM theme.

Below is a list of four wonderful speakers in the session, who have agreed to speak in the session if the proposal is accepted, with tentative titles of their talks:

Speakers:
1. John Stufken, "If you have to use a supersaturated design ...," Professor at George Mason University.
2. Chunfang Devon Lin, "Grouped Orthogonal Arrays and Their Applications," Professor at Queen's University.
3. Jonathan Stallrich, "Optimal Supersaturated Designs for Lasso Sign Recovery," Associate Professor at North Carolina State University.
4. Hongquan Xu, "Analysis of order-of-addition screening experiments with applications on drug combinations," Professor at University of California at Los Angeles.

Quality and Productivity Section ¹

Section on Physical and Engineering Sciences ²

Section on Statistical Learning and Data Science ³