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We formulate several multi-period sensitivity models to relax the corresponding versions of the assumption of sequential non-confounding. The primary sensitivity model involves only counterfactual outcomes, whereas the joint and product sensitivity models involve both counterfactual covariates and outcomes. We establish and compare explicit representations for the sharp and conservative bounds at the population level through convex optimization, depending only on the observed data. These results provide for the first time a satisfactory generalization from the marginal sensitivity model in the cross-sectional setting.<\/jats:p>","DOI":"10.1093\/biomet\/asae044","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T23:24:32Z","timestamp":1724369072000},"source":"Crossref","is-referenced-by-count":3,"title":["Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies"],"prefix":"10.1093","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1780-6839","authenticated-orcid":false,"given":"Zhiqiang","family":"Tan","sequence":"first","affiliation":[{"name":"Department of Statistics, Rutgers University , 110 Frelinghuysen Road , Piscataway, New Jersey 08854,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"2025012017220629600_asae044-B1","author":"Bonvini","year":"2022"},{"key":"2025012017220629600_asae044-B2","doi-asserted-by":"crossref","first-page":"2645","DOI":"10.1080\/01621459.2022.2069572","article-title":"Sharp sensitivity analysis for inverse propensity weighting via quantile balancing","volume":"118","author":"Dorn","year":"2023","journal-title":"J. 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