Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects
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Wiley
2024
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| author | Trang Quynh Nguyen Elizabeth A. Stuart Daniel O. Scharfstein Elizabeth L. Ogburn |
| author_facet | Trang Quynh Nguyen Elizabeth A. Stuart Daniel O. Scharfstein Elizabeth L. Ogburn Trang Quynh Nguyen Elizabeth A. Stuart Daniel O. Scharfstein Elizabeth L. Ogburn |
| collection | Wiley Open Access |
| contents | Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects Trang Quynh Nguyen Elizabeth A. Stuart Daniel O. Scharfstein Elizabeth L. Ogburn Statistics in Medicine An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one‐sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome‐mean‐given‐covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI‐based main analysis methods, including outcome regression, influence function (IF) based and weighting methods. We discuss range selection for the sensitivity parameter. We illustrate the sensitivity analyses with several outcome types from the JOBS II study. This application estimates nuisance functions parametrically – for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF‐based estimators to be asymptotically normal – with a view to inform nonparametric inference. 10.1002/sim.10153 http://onlinelibrary.wiley.com/termsAndConditions#am |
| doi_str_mv | 10.1002/sim.10153 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10153 |
| institution | Wiley Open Access |
| license_str_mv | http://onlinelibrary.wiley.com/termsAndConditions#am |
| publishDate | 2024 |
| publisher | Wiley |
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| spellingShingle | Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects Trang Quynh Nguyen Elizabeth A. Stuart Daniel O. Scharfstein Elizabeth L. Ogburn Statistics in Medicine Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects Trang Quynh Nguyen Elizabeth A. Stuart Daniel O. Scharfstein Elizabeth L. Ogburn Statistics in Medicine An important strategy for identifying principal causal effects (popular estimands in settings with noncompliance) is to invoke the principal ignorability (PI) assumption. As PI is untestable, it is important to gauge how sensitive effect estimates are to its violation. We focus on this task for the common one‐sided noncompliance setting where there are two principal strata, compliers and noncompliers. Under PI, compliers and noncompliers share the same outcome‐mean‐given‐covariates function under the control condition. For sensitivity analysis, we allow this function to differ between compliers and noncompliers in several ways, indexed by an odds ratio, a generalized odds ratio, a mean ratio, or a standardized mean difference sensitivity parameter. We tailor sensitivity analysis techniques (with any sensitivity parameter choice) to several types of PI‐based main analysis methods, including outcome regression, influence function (IF) based and weighting methods. We discuss range selection for the sensitivity parameter. We illustrate the sensitivity analyses with several outcome types from the JOBS II study. This application estimates nuisance functions parametrically – for simplicity and accessibility. In addition, we establish rate conditions on nonparametric nuisance estimation for IF‐based estimators to be asymptotically normal – with a view to inform nonparametric inference. 10.1002/sim.10153 http://onlinelibrary.wiley.com/termsAndConditions#am |
| title | Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10153 |