Behavior within a Clinical Trial and Implications for Mammography Guidelines

Abstract

and the University of Michigan for helpful comments. NSF CAREER Award 1350132 and NIA Grant P30-AG12810 provided support. I dedicate my research on breast cancer to Elisa Long. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Reconciling Seemingly Contradictory Results from the Oregon Health Insurance Experiment and the Massachusetts Health Reform

“…Applied work that extrapolates to all other policies using the LATE also makes a stronger, implicit assumption that there is no treatment effect heterogeneity, which implies that the MTE function is linear and has zero slope. I impose AA.1 instead of the weak monotonicity assumption from Brinch et al ( 2017) that I make in the context of a clinical trial on mammography in Kowalski (2020b) because the resulting bound on the treatment effect for always takers is uninformative about treatment effect heterogeneity here.…”

“…All of the content from that version has been subsumed here, except for content on bounds that are uninformative in this context. I use that content to provide informative bounds in the context of a mammography trial (Kowalski, 2020b). In my only other closely related work (Kowalski, 2020a), I explicitly do not break any new ground, but I show with stylized examples how recent advances from the treatment effects literature, inclusive of contributions made here, can inform external validity.…”

Mammograms and Mortality: How Has the Evidence Evolved?

“…Advances in machine learning can then be applied to determine which women are most likely to benefit. With this knowledge in hand, examination of which women select into mammography (as in Kim and Lee (2017); Einav et al (2019)) and how these selection patterns translate into heterogeneous treatment effects of mammography (as in Kowalski (2020a)) can help policymakers craft better targeted policies. There has already been careful work that examines the impact of policies on mammography (Mehta et al, 2015;Bitler and Carpenter, 2016;Kadiyala and Strumpf, 2016;Lu and Slusky, 2016;Buchmueller and Goldzahl, 2018;Myerson et al, 2020).…”

Policy Evaluation with Multiple Instrumental Variables

“…1 Introduction Heckman and Vytlacil (1999) introduced the marginal treatment effect (MTE) as a unifying concept for program and policy evaluation. 1 Since then, MTE methods have become a fundamental tool for empirical work, and have been applied in a variety of different settings including the returns to schooling (Moffitt, 2008;Carneiro, Heckman, and Vytlacil, 2011;Carneiro, Lokshin, and Umapathi, 2016;Nybom, 2017) and its impacts on wage inequality (Carneiro and Lee, 2009), discrimination (Arnold, Dobbie, and Yang, 2018;Arnold, Dobbie, and Hull, 2020), the effects of foster care (Doyle Jr., 2007), the impacts of welfare (Moffitt, 2019) and disability insurance (Maestas, Mullen, and Strand, 2013;French and Song, 2014;Autor, Kostøl, Mogstad, and Setzler, 2019) programs on labor supply, the performance of charter schools (Walters, 2018), health care (Kowalski, 2018;Depalo, 2020), the effects of early childhood programs (Kline and Walters, 2016;Cornelissen, Dustmann, Raute, and Schönberg, 2018;Felfe and Lalive, 2018), the efficacy of preventative health products (Mogstad, Santos, and Torgovitsky, 2017), the quantity-quality theory of fertility (Brinch, Mogstad, and Wiswall, 2017), and the effects of incarceration (Bhuller, Dahl, Løken, and Mogstad, 2020), among many others. Mogstad and Torgovitsky (2018) provide a recent review of the MTE methodology and its connection to other instrumental variable (IV) approaches.…”