Trial Design and Experimentation
Trial Design and Experimentation
Using artificial intelligence to help our society experiment and learn.
New developments in machine learning have the potential to dramatically improve experimentation and clinical trials by making them more efficient, safe, and accessible.
From a business perspective, the massive adoption of the Internet by virtually everyone made it easy to run large-scale online experiments to identify the best ways to communicate and interact with consumers through banners, website design, emails, promotions, product recommendations and others. However, for firms to succeed in their experiments they need reliable and credible machine learning and causal inference methods that allow them to trust the findings and results, understand the extent to which their findings can be expected outside of their sample, and separate causation from correlation. Our researchers have robust statistical and methodological training that helps firms extract the most from their experiments and online investments, improving online and offline metrics and KPIs such as conversion and bouncing.
From a clinical perspective, efficient experimental designs of trials of pharmaceutical drugs reduce the pressure on public and private health research funding, and minimize patient harm because they require smaller samples than RCTs, and allow for shorter trials. We develop and apply leading-edge clinical trial methods, such as multi-armed bandits and Bayesian adaptive designs, to help pharma companies and medical researchers with the statistical- and methodological transition from RCTs to adaptive trials.
The Trial Design and Experimentation practice aims to:
- Facilitate interactions between academia and industry
- Disseminate and increase the impact of academic research
Collaboration opportunities can take different shapes:
- Data sharing and research collaboration
- Contract research/consulting
- Research funding (e.g., PhD projects)
Examples of ongoing projects
- How can we use machine learning to improve clinical trials? (with Erasmus Medical Center and MIT)
- Evaluating the impact of point-based systems on the consumption of digital products
- Managing churn to maximize profits (with Harvard Business School)
- How to predict pre-conception? (with Erasmus Medical Center )
- How to use machine learning to improve predictions in ecommerce?
- How to use machine learning to build personalized online reviews
- Gui Liberali, Professor and Director // clinical trials , multi-armed bandits, reinforcement learning
- Alina Ferecatu, Assistant Professor // experimental economics, decision heuristics, Bayesian models
- Aurelie Lemmens, Associate Professor // targeting optimization, customer acquisition, development and retention
- Dan Shley, Associate Professor // understanding how the mind processes numeric information, judgments and choice
- Jason Roos, Associate Professor // causal models, digital advertising, information goods
- Pieter Schoonees, Assistant Professor // computational statistics, machine learning, psychometrics
- Xi Chen, Associate Professor // digital tools and platforms, quantitative marketing
- Ibrahim Usame Cikrikcioglu , PhD student // Online reviews, personalization algorithms
- Bob Rombach , PhD student // Consumer dynamics, dynamic programming, online experiments