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In practice, these measurements are intermittently observed and are known to be subject to substantial measurement error. Joint modelling of longitudinal and survival data enables us to associate intermittently measured error\u2010prone biomarkers with risks of survival outcomes and thus plays an important role in the analysis of medical data. Most of the joint models available in the literature have been built on the Gaussian assumption. This makes them sensitive to outliers. In this work, we study a range of robust models to address this issue. Of particular interest is the common occurrence in medical data that outliers can occur with different frequencies over time, for example, in the period when patients adjust to treatment changes. Motivated by the analysis of data gathered from patients with primary biliary cirrhosis, a new model with a time\u2010varying robustness is introduced. Through both the motivating example and a simulation study, this research not only stresses the need to account for longitudinal outliers in the analysis of medical data and in joint modelling research but also highlights the bias and inefficiency from not properly estimating the degrees\u2010of\u2010freedom parameter. This work presents a number of methods in addition to the time\u2010varying robustness, and each method can be fitted using the <jats:styled-content>R<\/jats:styled-content> package <jats:styled-content>robjm<\/jats:styled-content>.<\/jats:p>","DOI":"10.1002\/bimj.202000253","type":"journal-article","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T15:27:57Z","timestamp":1627486077000},"page":"1587-1606","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Robust joint modelling of longitudinal and survival data: Incorporating a time\u2010varying degrees\u2010of\u2010freedom parameter"],"prefix":"10.1002","volume":"63","author":[{"given":"Lisa M.","family":"McFetridge","sequence":"first","affiliation":[{"name":"Mathematical Sciences Research Centre School of Mathematics and Physics Queen's University Belfast  Belfast UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0603-1409","authenticated-orcid":false,"given":"\u00d6zg\u00fcr","family":"Asar","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Medical Informatics Faculty of Medicine Ac\u0131badem Mehmet Ali Ayd\u0131nlar University  \u0130stanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Wallin","sequence":"additional","affiliation":[{"name":"Department of Statistics Lund University  Lund Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssc.12405"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1177\/0962280220945352"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyu262"},{"issue":"2","key":"e_1_2_10_5_1","first-page":"187","article-title":"Robust joint modeling of longitudinal measurements and time to event data using normal\/independent distributions: A Bayesian approach","volume":"34","author":"Baghfalaki T.","year":"2013","journal-title":"Biometrical Journal"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/10543406.2014.903657"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.2307\/1390675"},{"key":"e_1_2_10_8_1","first-page":"1","article-title":"Stan: A probabilistic programming language","volume":"1","author":"Carpenter B.","year":"2017","journal-title":"Journal of Statistical Software"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1972.tb00899.x"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.2307\/2533493"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3131"},{"key":"e_1_2_10_12_1","volume-title":"Joint modeling of longitudinal and time\u2010to\u2010event data","author":"Elashoff R. 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