arXiv 16 Aug 2019 · Statistics — Methodology · 2 citations (OpenAlex)
arXiv:1908.05810 · PDF · DOI · OpenAlex · Extracted main text
I develop a model of a randomized experiment with a binary intervention and a binary outcome. Potential outcomes in the intervention and control groups give rise to four types of participants. Fixing ideas such that the outcome is mortality, some participants would live regardless, others would be saved, others would be killed, and others would die regardless. These potential outcome types are not observable. However, I use the model to develop estimators of the number of participants of each type. The model relies on the randomization within the experiment and on deductive reasoning. I apply the model to an important clinical trial, the PROWESS trial, and I perform a Monte Carlo simulation calibrated to estimates from the trial. The reduced form from the trial shows a reduction in mortality, which provided a rationale for FDA approval. However, I find that the intervention killed two participants for every three it saved.
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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 | Bernard, G. R., J.-L. Vincent, P.-F. Laterre, S. P. LaRosa, J.-F. Dh… (2001) Efficacy and safety of recombinant human activated protein c for severe sepsis | 0.874 | 7 | 2 | 100% |
| 2 | Sahinidis, N. V (2018) BARON 18.8.23: Global Optimization of Mixed-Integer Nonlinear Programs, User's Manual | 0.644 | 2 | 2 | 100% |
| 3 | US Food and Drug Administration and others (2011) FDA drug safety communication: voluntary market withdrawal of xigris due to failure to show a survival benefit | 0.405 | 1 | 1 | 100% |
| 4 | Foot, P (1967) The problem of abortion and the doctrine of double effect | 0.405 | 1 | 1 | 100% |
| 5 | Holland, P. W (1986) Statistics and causal inference | 0.405 | 1 | 1 | 100% |
| 6 | Wolfram Research, Inc (2018) Mathematica, Version 11.3 | 0.405 | 1 | 1 | 100% |
| 7 | The Mathworks, Inc (2016) Matlab, Version r2016a | 0.405 | 1 | 1 | 100% |
| 8 | Rubin, D. B (1974) Estimating causal effects of treatments in randomized and nonrandomized studies | 0.405 | 1 | 1 | 100% |
| 9 | Rubin, D. B (1977) Assignment to treatment group on the basis of a covariate | 0.405 | 1 | 1 | 100% |
| 10 | Siegel, J. P (2002) Assessing the use of activated protein c in the treatment of severe sepsis | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 12 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2212.14105 | 0.585 | 3 | 1 |
| 2 | Counting Defiers | 0.511 | 2 | 1 |