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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.22389 |
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| _version_ | 1866916972053135360 |
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| author | Redd, Andrew Gao, Yujing Smith, Bonnie B. Varadhan, Ravi Apter, Andrea J. Scharfstein, Daniel O. |
| author_facet | Redd, Andrew Gao, Yujing Smith, Bonnie B. Varadhan, Ravi Apter, Andrea J. Scharfstein, Daniel O. |
| contents | This paper introduces an R package SensIAT that implements a sensitivity analysis methodology, based on augmented inverse intensity weighting, for randomized trials with irregular and potentially informative assessment times. Targets of inference involve the population mean outcome in each treatment arm as well as the difference in these means (i.e., treatment effect) at specified times after randomization. This methodology is useful in settings where there is concern that study participants are either more, or less, likely to have assessments at times when their outcomes are worse. In such settings, unadjusted estimates can be biased. The methodology allows researchers to see how inferences are impacted by a range of assumptions about the strength and direction of informative timing in each arm, while incorporating flexible semi-parametric modeling. We describe the functions implemented in SensIAT and illustrate them through an analysis of a synthetic dataset motivated by the HAP2 asthma randomized clinical trial. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22389 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SensIAT: An R Package for Conducting Sensitivity Analysis of Randomized Trials with Irregular Assessment Times Redd, Andrew Gao, Yujing Smith, Bonnie B. Varadhan, Ravi Apter, Andrea J. Scharfstein, Daniel O. Methodology This paper introduces an R package SensIAT that implements a sensitivity analysis methodology, based on augmented inverse intensity weighting, for randomized trials with irregular and potentially informative assessment times. Targets of inference involve the population mean outcome in each treatment arm as well as the difference in these means (i.e., treatment effect) at specified times after randomization. This methodology is useful in settings where there is concern that study participants are either more, or less, likely to have assessments at times when their outcomes are worse. In such settings, unadjusted estimates can be biased. The methodology allows researchers to see how inferences are impacted by a range of assumptions about the strength and direction of informative timing in each arm, while incorporating flexible semi-parametric modeling. We describe the functions implemented in SensIAT and illustrate them through an analysis of a synthetic dataset motivated by the HAP2 asthma randomized clinical trial. |
| title | SensIAT: An R Package for Conducting Sensitivity Analysis of Randomized Trials with Irregular Assessment Times |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.22389 |