Effective scientific communication is really hard This summer I met a few cast members from Saturday Night Live, including the comedian Mikey Day. I’m a big fan of the show and have watched it since old enough to stay up until 11:35pm on Saturdays. We talked about the their creative solutions for comedy during lock…
Pulled from the Infectious Economics archives, these articles provide recommendations for the design and development of robust mathematical models of COVID-19 epidemiology and economics to reduce uncertainty in decision-making and inform evidence-based public policy. Associations are easy; valid causal inference is hard If we want to be honest in evaluating the winners and losers in…
Incredible new research from Harvard and Kaiser contributed two substantial leaps forward this summer: first in the useful application of machine learning predictions using electronic health records and second in the accurate targeting of people who can benefit the most from HIV pre-exposure prophylaxis. This paper in Lancet HIV by Marcus and colleagues is so good…
Want to join our book club? We’re reading the new Causal Inference Book by Miguel Hernan and James Robins. The book is forthcoming publication by Chapman & Hall/CRC and it is available for FREE right now to download from Harvard. Every few weeks my colleagues and I meet up to discuss a few chapters. We…
…and learns good practices for reproducible analyses in health economics and outcomes research from software engineers. THE KEYS are functional programming, version control, style guides, and peer code review systems. READ ABOUT IT in the latest Value & Outcomes Spotlight magazine here. LEARN HANDS ON at our short-course Sat, May 18, 8am-Noon at the ISPOR…
Kangho Suh wrote an excellent article on the use of instrumental variables in healthcare. I agree this technique strengthens causal inference for analyses of observational data. This is even more important when a randomized controlled trial is not feasible or ethical. SaveSave
Failure to consider these methods could result in unintended consequences and exacerbate existing inequalities in health between patients who are “average” and “outliers.”
A key to making causal inference with observational data
Let’s keep digging into the association between Google searches and the number of people starting PrEP drugs by fitting a poisson model of the time series in R. We’ll use this work-in-progress to kick off a discussion on validation of models used to forecast utilization. To review: First, we first noticed a similarity in the trends…