Research

Abstract: This article examines the consequences and causes of low enrollment of Black patients in clinical trials.  We develop a simple model of similarity-based extrapolation that predicts that evidence is more relevant for decision-making by physicians and patients when it is more representative of the group that is being treated. This generates the key result that the perceived benefit of a medicine for a group depends not only on the average benefit from a trial, but also on the share of patients from that group who were enrolled in the trial. In survey experiments, we find that physicians who care for Black patients are more willing to prescribe drugs tested in representative samples, an effect substantial enough to close observed gaps in the prescribing rates of new medicines. Black patients update more on drug efficacy when the sample that the drug is tested on is more representative, reducing Black-White patient gaps in beliefs about whether the drug will work as described. Despite these benefits of representative data, our framework and evidence suggest that those who have benefited more from past medical breakthroughs are less costly to enroll in the present, leading to persistence in who is represented in the evidence base.

Abstract: The study of network formation is pervasive in economics, sociology, and many other fields. In this article, we model network formation as a ‘choice’ that is made by nodes of a network to connect to other nodes. We study these ‘choices’ using discrete-choice models, in which agents choose between two or more discrete alternatives. We employ the ‘repeated-choice’ (RC) model to study network formation. We argue that the RC model overcomes important limitations of the multinomial logit (MNL) model, which gives one framework for studying network formation, and that it is well-suited to study network formation. We also illustrate how to use the RC model to accurately study network formation using both synthetic and real-world networks. Using edge-independent synthetic networks, we also compare the performance of the MNL model and the RC model. We find that the RC model estimates the data-generation process of our synthetic networks more accurately than the MNL model. Using a patent citation network, which forms sequentially, we present a case study of a qualitatively interesting scenario—the fact that new patents are more likely to cite older, more cited, and similar patents—for which employing the RC model yields interesting insights.

Abstract:  Inequity in clinical trial enrollment, particularly the under-enrollment of females, is a critical concern in biomedical sciences, in part because the findings of clinical trials may not be generalizable. One way to address this inequity might be to increase gender diversity of research teams—particularly, study Principal Investigators—but the relationship between gender diversity of research teams and enrollment of females into clinical trials has never been studied. We analyzed how the proportion of female trial participants varied by the sex of the trial Principal Investigator in over 10,000 clinical trials, accounting for the disease being studied and when a trial was conducted. We examined whether specific decisions by investigators that could influence female participant enrollment (e.g., selecting female study staff for patient recruitment or including pregnant patients) varied by sex of the Principal Investigator. Only one-third of trials had a female Principal Investigator, and female enrollment was significantly greater in trials with a female versus male Principal Investigator (54.1% vs 46.9% female enrollment). Trials with a female Principal Investigator also had significantly more female staff and were less likely to exclude pregnant patients, suggesting potential mechanisms behind our findings.

Abstract:  We study the role of political ideology in the diffusion of scientific knowledge by media outlets. We document, using a novel measure of scientist ideology that spans more than 600,000 papers, that outlets are statistically significantly more likely to cover scientists with similar ideology. However, the role of scientist ideology is small in magnitude when compared to the role of scientific quality, as measured by academic citations, journal quality, and research funding, in media coverage of science. On average, outlets are more likely to cover high-quality research written by misaligned scientists than low-quality research written by ideologically aligned scientists.