Lianne Kurina: How controlling confounders makes better epidemiology
Public health studies are among the most challenging to get right, says this expert in epidemiology. But better design can yield greater confidence.
Audio is streamed directly from the publisher (op3.dev) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.
Show Notes
As the world has learned through the recent pandemic, epidemiological studies can be complicated by many unanticipated factors. Lianne Kurina is an expert in the design of epidemiological studies who says that the key to greater confidence is better design.
The gold standard, she says, is the randomized controlled trial—a study that compares groups that are essentially identical by every apparent factor but one— the vaccinated vs. the unvaccinated, for instance. In the case of COVID-19 vaccinations, Kurina stresses that investigators did an exemplary job of this.
In situations where we can't use a randomized controlled trial, achieving a similar balance and specificity is far harder. Kurina says that researchers working with observational data, rather than trial data, must always be attuned to the overlooked factors—“confounders” she calls them—that can muddy the data and render a study moot.
However, Kurina notes, the better one controls the confounders in these observational studies via better design and data collection, the greater confidence we can have in the end results, as she tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here.
Connect With Us:
Episode Transcripts >>> The Future of Everything Website
Connect with Russ >>> Threads / Bluesky / Mastodon
Connect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook
Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.