Joshua D. Angrist, Bruno Ferman, Carol Gao, Peter Hull, Otavio L. Tecchio, Robert W. Yeh
arXiv 3 Jan 2025 · Econometrics
arXiv:2501.01623 · PDF · DOI · OpenAlex · Extracted main text
The ISCHEMIA Trial randomly assigned patients with ischemic heart disease to an invasive treatment strategy centered on revascularization with a control group assigned non-invasive medical therapy. As is common in such “strategy trials,” many participants assigned to treatment remained untreated while many assigned to control crossed over into treatment. Intention-to-treat (ITT) analyses of strategy trials preserve randomization-based comparisons, but ITT effects are diluted by non-compliance. Conventional per-protocol analyses that condition on treatment received are likely biased by discarding random assignment. In trials where compliance choices are made shortly after assignment, instrumental variables (IV) methods solve both problems -- recovering an undiluted average causal effect of treatment for treated subjects who comply with trial protocol. In ISCHEMIA, however, some controls were revascularized as long as five years after random assignment. This paper extends the IV framework for strategy trials, allowing for such dynamic non-random compliance behavior. IV estimates of long-run revascularization effects on quality of life are markedly larger than previously reported ITT and per-protocol estimates. We also show how to estimate complier characteristics in a dynamic-treatment setting. These estimates reveal increasing selection bias in naive time-varying per-protocol estimates of revascularization effects. Compliers have baseline health similar to that of the study population, while control-group crossovers are far sicker.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | John A Spertus, Philip G Jones, David J Maron, Sean M O’Brien, Harmo… (2020) Health-Status Outcomes with Invasive or Conservative Care in Coronary Disease | 1.000 | 8 | 4 | 100% |
| 2 | Joshua D Angrist \ Guido W Imbens (1995) Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity | 0.928 | 5 | 3 | 80% |
| 3 | Joshua D Angrist \ Peter Hull (2023) Instrumental Variables Methods Reconcile Intention-to-Screen Effects Across Pragmatic Cancer Screening Trials | 0.843 | 3 | 3 | 100% |
| 4 | Joshua D. Angrist, Guido W. Imbens \ Donald B. Rubin (1996) Identification of Causal Effects Using Instrumental Variables self | 0.644 | 2 | 2 | 100% |
| 5 | Jerry A Hausman (1978) Specification Tests in Econometrics | 0.644 | 2 | 2 | 100% |
| 6 | Kirill Borusyak, Xavier Jaravel \ Jann Spiess (2024) Revisiting Event-Study Designs: Robust and Efficient Estimation | 0.511 | 2 | 1 | 100% |
| 7 | Guido W. Imbens \ Joshua D. Angrist (1994) Identification and Estimation of Local Average Treatment Effects | 0.511 | 2 | 1 | 100% |
| 8 | Evan K Rose \ Yotam Shem-Tov On Recoding Ordered Treatments as Binary Indicators | 0.511 | 2 | 1 | 100% |
| 9 | Susan Athey \ Guido W. Imbens (2022) Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption | 0.405 | 1 | 1 | 100% |
| 10 | Alberto Abadie (2003) Semiparametric Instrumental Variable Estimation of Treatment Response Models | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.
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| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Dynamic LATEs with a Static Instrument | 1.000 | 5 | 3 |