Amanda Coston, Edward H. Kennedy
arXiv 19 Jul 2022 · Statistics — Methodology · 2 citations (OpenAlex)
arXiv:2207.09016 · PDF · DOI · OpenAlex · Extracted main text
Historically used in settings where the outcome is rare or data collection is expensive, outcome-dependent sampling is relevant to many modern settings where data is readily available for a biased sample of the target population, such as public administrative data. Under outcome-dependent sampling, common effect measures such as the average risk difference and the average risk ratio are not identified, but the conditional odds ratio is. Aggregation of the conditional odds ratio is challenging since summary measures are generally not identified. Furthermore, the marginal odds ratio can be larger (or smaller) than all conditional odds ratios. This so-called non-collapsibility of the odds ratio is avoidable if we use an alternative aggregation to the standard arithmetic mean. We provide a new definition of collapsibility that makes this choice of aggregation method explicit, and we demonstrate that the odds ratio is collapsible under geometric aggregation. We describe how to partially identify, estimate, and do inference on the geometric odds ratio under outcome-dependent sampling. Our proposed estimator is based on the efficient influence function and therefore has doubly robust-style properties.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jerome Cornfield et al (1951) A method of estimating comparative rates from clinical data; applications to cancer of the lung, breast, and cervix | 0.737 | 3 | 2 | 100% |
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| 3 | Victor Chernozhukov, Mert Demirer, Esther Duflo, and Ivan Fernandez-… (2018) Generic machine learning inference on heterogenous treatment effects in randomized experiments | 0.511 | 2 | 2 | 50% |
| 4 | Jinyong Hahn (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effects | 0.511 | 2 | 2 | 50% |
| 5 | James Robins, Lingling Li, Eric Tchetgen, Aad van der Vaart, et al (2008) Higher order influence functions and minimax estimation of nonlinear functionals | 0.511 | 2 | 2 | 50% |
| 6 | Mark J Van der Laan, MJ Laan, and James M Robins (2003) Unified methods for censored longitudinal data and causality | 0.511 | 2 | 2 | 50% |
| 7 | Wenjing Zheng and Mark J van der Laan (2010) Asymptotic theory for cross-validated targeted maximum likelihood estimation | 0.511 | 2 | 2 | 50% |
| 8 | Peter J Bickel, Chris AJ Klaassen, Peter J Bickel, Ya’acov Ritov, J… (1993) Efficient and adaptive estimation for semiparametric models, volume 4 | 0.511 | 2 | 1 | 100% |
| 9 | Sander Greenland, Judea Pearl, and James M Robins (1999) Confounding and collapsibility in causal inference | 0.511 | 2 | 1 | 100% |
| 10 | Sung Jae Jun and Sokbae Lee (2020) Causal inference under outcome-based sampling with monotonicity assumptions | 0.511 | 2 | 1 | 100% |
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