study design
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10 “simple rules” for socially responsible science
Guidelines concerning the potentially harmful effects of scientific studies have historically focused on minimizing risk for participants. However, studies can also indirectly inflict harm on individuals and social groups through how they are designed, reported, and disseminated. As evidenced by recent criticisms and retractions of high-profile studies dealing with a wide variety of social issues, there is a scarcity of resources and guidance on how one can conduct research in a socially responsible manner. As such, even motivated researchers might publish work that has negative social impacts due to a lack of awareness. To address this, we proposed 10 recommendations (“simple rules”) for researchers who wish to conduct more socially responsible science. These recommendations cover major considerations throughout the life cycle of a study from inception to dissemination. They are not aimed to be a prescriptive list or a deterministic code of conduct. Rather, they are meant to help motivated scientists to reflect on their social responsibility as researchers and actively engage with the potential social impact of their research.
Impact evaluation for COVID-19 non-pharmaceutical interventions: what is (un)knowable?
COVID-19 non-pharmaceutical intervention (NPI) policies have been one of the most important and contentious decisions of our time. Beyond even the "normal" inherent difficulties in impact evaluation with observational data, COVID-19 NPI policy evaluation is complicated by additional challenges related to infectious disease dynamics and lags, lack of direct observation of key outcomes, and a multiplicity of interventions occurring on an accelerated time scale. Randomized controlled trials also suffer from what is feasible and ethical to randomize as well as the sheer scale, scope, time, and resources required for an NPI trial to be informative (or at least not misinformative). In this talk, Dr. Haber will discuss the challenges in generating useful evidence for COVID-19 NPIs, the landscape of the literature, and highlight key controversies in several high profile studies over the course of the pandemic. Chasing after unknowables poses major problems for the metascience/replicability movement, institutional research science, and decision makers. If the only choices for informing an important topic are "weak study design" vs "do nothing," when is "do nothing" the best choice?
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