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However, these effects are likely confounded by unmeasured confounders, and the sensitivity of these effects to unmeasured confounders may be heterogeneous, where one subgroup's treatment effect is more sensitive than that of another subgroup. Unfortunately, compared to the overall treatment effect, there are limited investigations about the sensitivity of heterogeneous treatment effects to unmeasured confounding. The paper presents and compares methods for sensitivity analysis of heterogeneous effects in observational studies based on Rosenbaum's model for sensitivity analysis. We show that, unlike the sensitivity analysis of the overall treatment effect, the sensitivity of heterogeneous treatment effects depends on the variation in the effect sizes across subgroups and the correction for multiple testing. The data analysis further supports our findings where the overall effect of sleep disturbances on cognitive decline is significant (\u2010value = ). Also, the effect is more severe among males (\u2010value = ) and insensitive to a moderate degree of unmeasured confounding. Finally, we offer an easy\u2010to\u2010use R software to carry out the sensitivity analyses for heterogeneous treatment effects.<\/jats:p>","DOI":"10.1002\/sim.70446","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:02:07Z","timestamp":1773799327000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparison of Methods for Sensitivity Analysis of Heterogeneous Treatment Effects in Observational Studies and Application to Alzheimer's Disease and Cognitive Decline"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0511-4655","authenticated-orcid":false,"given":"Jingqi","family":"Duan","sequence":"first","affiliation":[{"name":"Department of Statistics University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Corinne D.","family":"Engelman","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiongshi","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Medical Informatics University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyunseung","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Statistics University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/gps.529"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.wad.0000201850.52707.80"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/gerona\/61.4.405"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2869.2009.00759.x"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.5665\/SLEEP.1276"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2011.1115"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sleep.2012.02.003"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.5665\/sleep.1732"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.5665\/sleep.2802"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.2174\/15672050113109990134"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2015.05.004"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2013.269"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1532-5415.2001.49235.x"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.ajp.2010.09111575"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000007231"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41582-019-0228-7"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-020-00712-4"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13024-022-00574-4"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41582-024-00988-2"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.2307\/2999630"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1005285815569"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-1213-8"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.1657"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mpt006"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.7326\/M16-2607"},{"key":"e_1_2_12_27_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12348"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2012.742018"},{"key":"e_1_2_12_29_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12884"},{"key":"e_1_2_12_30_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781003102670-11"},{"key":"e_1_2_12_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6593"},{"issue":"3","key":"e_1_2_12_32_1","first-page":"338","article-title":"Matching Methods for Observational Studies Derived From Large Administrative Databases","volume":"35","author":"Yu R.","year":"2020","journal-title":"Statistical Science"},{"key":"e_1_2_12_33_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2009.tm08470"},{"key":"e_1_2_12_34_1","unstructured":"J.Tibshirani S.Athey R.Friedberg et al. \u201cPackage \u2018grf\u2019. 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