<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Maringe C</submitter><funding>Cancer Research UK</funding><pagination>1719-1729</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7823243</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>49(5)</volume><pubmed_abstract>Acquiring real-world evidence is crucial to support health policy, but observational studies are prone to serious biases. An approach was recently proposed to overcome confounding and immortal-time biases within the emulated trial framework. This tutorial provides a step-by-step description of the design and analysis of emulated trials, as well as R and Stata code, to facilitate its use in practice. The steps consist in: (i) specifying the target trial and inclusion criteria; (ii) cloning patients; (iii) defining censoring and survival times; (iv) estimating the weights to account for informative censoring introduced by design; and (v) analysing these data. These steps are illustrated with observational data to assess the benefit of surgery among 70-89-year-old patients diagnosed with earl</pubmed_abstract><journal>International journal of epidemiology</journal><pubmed_title>Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data.</pubmed_title><pmcid>PMC7823243</pmcid><funding_grant_id>C7923/A18525</funding_grant_id><funding_grant_id>29018</funding_grant_id><funding_grant_id>18525</funding_grant_id><pubmed_authors>Rachet B</pubmed_authors><pubmed_authors>Smith M</pubmed_authors><pubmed_authors>Benitez Majano S</pubmed_authors><pubmed_authors>Belot A</pubmed_authors><pubmed_authors>Leyrat C</pubmed_authors><pubmed_authors>Exarchakou A</pubmed_authors><pubmed_authors>Maringe C</pubmed_authors></additional><is_claimable>false</is_claimable><name>Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data.</name><description>Acquiring real-world evidence is crucial to support health policy, but observational studies are prone to serious biases. An approach was recently proposed to overcome confounding and immortal-time biases within the emulated trial framework. This tutorial provides a step-by-step description of the design and analysis of emulated trials, as well as R and Stata code, to facilitate its use in practice. The steps consist in: (i) specifying the target trial and inclusion criteria; (ii) cloning patients; (iii) defining censoring and survival times; (iv) estimating the weights to account for informative censoring introduced by design; and (v) analysing these data. These steps are illustrated with observational data to assess the benefit of surgery among 70-89-year-old patients diagnosed with earl</description><dates><release>2020-01-01T00:00:00Z</release><publication>2020 Oct</publication><modification>2025-05-31T22:40:54.396Z</modification><creation>2021-02-21T03:07:18Z</creation></dates><accession>S-EPMC7823243</accession><cross_references><pubmed>32386426</pubmed><doi>10.1093/ije/dyaa057</doi></cross_references></HashMap>