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Time-dependent study entries and exposures in cohort studies can easily be sources of different and avoidable types of bias.

, , , , , and . Journal of clinical epidemiology, 65 (11): 1171-80 (November 2012)6935<m:linebreak></m:linebreak>Anàlisi de supervivència; Bias; Immortal time bias; Introductori.
DOI: 10.1016/j.jclinepi.2012.04.008

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An easy mathematical proof showed that time-dependent bias inevitably leads to biased effect estimation., , , and . Journal of clinical epidemiology, 61 (12): 1216-21 (December 2008)4615<m:linebreak></m:linebreak>JID: 8801383; 2007/08/09 received; 2008/01/10 revised; 2008/02/12 accepted; 2008/07/10 aheadofprint; ppublish;<m:linebreak></m:linebreak>Anàlisi de supervivència.Time-dependent study entries and exposures in cohort studies can easily be sources of different and avoidable types of bias., , , , , and . Journal of clinical epidemiology, 65 (11): 1171-80 (November 2012)6935<m:linebreak></m:linebreak>Anàlisi de supervivència; Bias; Immortal time bias; Introductori.Boosting for high-dimensional time-to-event data with competing risks., , , and . Bioinform., 25 (7): 890-896 (2009)Estimating summary functionals in multistate models with an application to hospital infection data., , and . Comput. Stat., 26 (2): 181-197 (2011)On change in length of stay associated with an intermediate event: estimation within multi-state models and large sample properties (Zur Änderung der Aufenthaltsdauer nach einem intermediären Ereignis: Schätzung innerhalb von Mehrstadienmodellen und Eigenschaften für große Stichproben). University of Freiburg, Germany, (2005)base-search.net (ftunivfreiburg:oai:freidok.uni-freiburg.de:1843).Empirical Transition Matrix of Multi-State Models: TheetmPackage, , and . Journal of Statistical Software, (2011)