Sensitivity Analysis for Binary Outcome Misclassification in Randomization Tests via Integer Programming

Conducting a randomization test is a common method for testing causal null hypotheses in randomized experiments. The popularity of randomization tests is largely because their statistical validity only depends on the randomization design, and no distributional or modeling assumption on the outcome v...

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Veröffentlicht in:Journal of computational and graphical statistics Jg. 34; H. 4; S. 1528 - 1541
Hauptverfasser: Heng, Siyu, Shaw, Pamela A.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Taylor & Francis 02.10.2025
Taylor & Francis Ltd
Schlagworte:
Accuracy
Design-based causal inference
Fisher's sharp null
Integer programming
Matched observational studies
Neyman's weak null
Randomization
Randomization inference
Sensitivity analysis
Warning
randomization inference
Neyman’s weak null
Fisher’s sharp null
integer programming
matched observational studies
design-based causal inference
ISSN:1061-8600, 1537-2715
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Zusammenfassung:Conducting a randomization test is a common method for testing causal null hypotheses in randomized experiments. The popularity of randomization tests is largely because their statistical validity only depends on the randomization design, and no distributional or modeling assumption on the outcome variable is needed. However, randomization tests may still suffer from other sources of bias, among which outcome misclassification is a significant one. We propose a model-free and finite-population sensitivity analysis approach for binary outcome misclassification in randomization tests. A central quantity in our framework is "warning accuracy," defined as the threshold such that a randomization test result based on the measured outcomes may differ from that based on the true outcomes if the outcome measurement accuracy did not surpass that threshold. We show how learning the warning accuracy and related concepts can amplify analyses of randomization tests subject to outcome misclassification without adding additional assumptions. We show that the warning accuracy can be computed efficiently for large datasets by adaptively reformulating a large-scale integer program with respect to the randomization design. We apply the proposed approach to the Prostate Cancer Prevention Trial (PCPT). We also developed an open-source R package for implementation of our approach. Supplementary materials for this article are available online.
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ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2025.2461222