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Quite commonly, the normal\u2013normal hierarchical model (NNHM) including a random-effect to account for between-study heterogeneity is utilized for such analyses. The same modeling framework may also be used to not only derive a combined estimate, but also to borrow strength for a particular study from another by deriving a shrinkage estimate. For instance, a small-scale randomized controlled trial could be supported by a non-randomized study, e.g. a clinical registry. This would be particularly attractive in the context of rare diseases. We demonstrate that a meta-analysis still makes sense in this extreme case, effectively based on a synthesis of only two studies, as illustrated using a recent trial and a clinical registry in Creutzfeld-Jakob disease. Derivation of a shrinkage estimate within a Bayesian random-effects meta-analysis may substantially improve a given estimate even based on only a single additional estimate while accounting for potential effect heterogeneity between the studies. Alternatively, inference may equivalently be motivated via a model specification that does not require a common overall mean parameter but considers the treatment effect in one study, and the difference in effects between the studies. The proposed approach is quite generally applicable to combine different types of evidence originating, e.g. from meta-analyses or individual studies. An application of this more general setup is provided in immunosuppression following liver transplantation in children. <\/jats:p>","DOI":"10.1177\/0962280219833079","type":"journal-article","created":{"date-parts":[[2019,3,1]],"date-time":"2019-03-01T11:31:16Z","timestamp":1551439876000},"page":"293-308","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":44,"title":["Dynamically borrowing strength from another study through shrinkage estimation"],"prefix":"10.1177","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6911-698X","authenticated-orcid":false,"given":"Christian","family":"R\u00f6ver","sequence":"first","affiliation":[{"name":"Department of Medical Statistics, University Medical Center G\u00f6ttingen, G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5347-7441","authenticated-orcid":false,"given":"Tim","family":"Friede","sequence":"additional","affiliation":[{"name":"Department of Medical Statistics, University Medical Center G\u00f6ttingen, G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,3,1]]},"reference":[{"key":"bibr1-0962280219833079","doi-asserted-by":"publisher","DOI":"10.1177\/1352458516643399"},{"key":"bibr2-0962280219833079","doi-asserted-by":"publisher","DOI":"10.1016\/j.ahj.2017.02.027"},{"key":"bibr3-0962280219833079","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp-2016-313541"},{"key":"bibr4-0962280219833079","doi-asserted-by":"publisher","DOI":"10.1002\/ajmg.c.31533"},{"key":"bibr5-0962280219833079","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6225"},{"key":"bibr6-0962280219833079","doi-asserted-by":"crossref","unstructured":"Thall PF, Ursino M, Baudouin V, et\u00a0al. Bayesian treatment comparison using parametric mixture priors computed from elicited histograms. 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