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1. bmcmedresmethodol.biomedcentral.com › articles › 10A four-step strategy for handling missing outcome data in ...

   bmcmedresmethodol.biomedcentral.com › articles › 10

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   Aug 12, 2020 · As well as physically missing data, there will be additional concern for participants providing data during Covid-19 when their outcomes are influenced by it. In this situation, a central consideration is the treatment effect to be obtained from the trial (estimand), as this may sometimes require treating some of the collected data as missing.

   • Author: Suzie Cro, Tim P Morris, Brennan C Kahan, Victoria R Cornelius, James R Carpenter

   • Publish Year: 2020

2. onlinelibrary.wiley.com › doi › 10Dealing with missing outcome data in meta-analysis

   onlinelibrary.wiley.com › doi › 10

   Apr 16, 2019 · When data are MNAR, we need to specify a joint model for the observed and missing outcomes and the missing data pattern. There are two popular models for doing so, selection and pattern mixture models. 29 Pattern-mixture models use the marginal distribution of the missing data pattern and the conditional distribution of the observed and missing ...

   • Author: Dimitris Mavridis, Dimitris Mavridis, Ian R. White

   • Publish Year: 2020

3. onlinelibrary.wiley.com › doi › fullMissing data: A statistical framework for practice

   onlinelibrary.wiley.com › doi › full

   Feb 24, 2021 · When there are only missing data in the outcome (which may be cross sectional or longitudinal), Subsection 5.1 showed that maximising the likelihood of the observed data gives valid inference (provided we choose an appropriate covariance structure).

   • Author: James R Carpenter, Melanie Smuk

   • Publish Year: 2021

4. pmc.ncbi.nlm.nih.gov › articles › PMC7615108Missing data: A statistical framework for practice - PMC

   pmc.ncbi.nlm.nih.gov › articles › PMC7615108

   When there are only missing data in the outcome (which may be cross sectional or longitudinal), Subsection 5.1 showed that maximising the likelihood of the observed data gives valid inference (provided we choose an appropriate covariance structure).

5. academic.oup.com › icvts › articleStatistical primer: how to deal with missing data in ...

   academic.oup.com › icvts › article

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   May 10, 2018 · Missing outcome information: It should be noted that up to this point, this article has focused primarily on missing covariate information. That is because when there are missing outcome data, it has been argued that the complete case analysis is more appropriate as imputed outcome data can lead to misleading results [14, 15]. Single imputation ...

   • Author: Grigorios Papageorgiou, Stuart W Grant, Johanna J M Takkenberg, Mostafa M Mokhles

   • Publish Year: 2018

6. www.sciencedirect.com › science › articleMissing data: Issues, concepts, methods - ScienceDirect

   www.sciencedirect.com › science › article

   Feb 1, 2024 · Missing data are data that we planned to collect to answer a research question, such as participant characteristics at the start of the study or their health outcomes after receiving some treatments, but for some reason we were not able to. In practice there are various ways in which missing data can arise.

7. People also ask

   What percentage of participants are missing outcome data?

   • The median percentage of participants with potential missing outcome data was 9.7% (4.1-19.9%) and with total possible missing data was 11.7% (5.6-23.7%). Only three trials provided a reason for missingness (eg, missing at random).

   Potential impact of missing outcome data on treatment effects in

   www.bmj.com/content/370/bmj.m2898

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   What is missing data?

   • Missing data are data that we planned to collect to answer a research question, such as participant characteristics at the start of the study or their health outcomes after receiving some treatments, but for some reason we were not able to. In practice there are various ways in which missing data can arise.

   Missing data: Issues, concepts, methods - ScienceDirect

   www.sciencedirect.com/science/article/pii/S1073874624000082

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   What percentage of trials have missing outcome data?

   • They found 89% had partly missing outcome data, and in 37 trials with repeated outcome measures, 46% restricted analysis to those with complete records; only 21% reported sensitivity analysis to the missing data assumptions underpinning their primary analysis.

   Missing data: A statistical framework for practice

   onlinelibrary.wiley.com/doi/full/10.1002/bimj.202000196

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   What does missing data mean in a systematic review?

   • The term “missing data” has various meanings in systematic reviews. In this manuscript, we use the term to refer only to missing outcome data and not to missing studies, missing statistics, or whole outcomes not reported in a study. We consider that in some (or all) of the studies, some participants did not provide any outcome data.

   Dealing with missing outcome data in meta-analysis

   onlinelibrary.wiley.com/doi/10.1002/jrsm.1349

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   What happens if participant data is missing in a randomised trial?

   • In randomised trials, participant data can be missing in several ways. Participants may stop taking part in some or all aspects of the trial, after which point they may not provide further data on the outcome of interest. They may also be unable to attend some follow-up visits, resulting in missing outcome data for those visits.

   Missing data: Issues, concepts, methods - ScienceDirect

   www.sciencedirect.com/science/article/pii/S1073874624000082

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   Does maximising the likelihood of missing data give valid inference?

   • When there are only missing data in the outcome (which may be cross sectional or longitudinal), Subsection 5.1 showed that maximising the likelihood of the observed data gives valid inference (provided we choose an appropriate covariance structure).

   Missing data: A statistical framework for practice

   onlinelibrary.wiley.com/doi/full/10.1002/bimj.202000196

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8. www.bmj.com › content › 370Potential impact of missing outcome data on treatment effects ...

   www.bmj.com › content › 370

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   Aug 26, 2020 · One challenge when handling missing data is the lack of clarity in trial reports on whether participants have missing outcome data.12 We recently published guidance for authors of systematic reviews on how to identify and classify participants with missing outcome data in the trial reports.13 The Cochrane Handbook acknowledges that attempts to deal with missing data in systematic reviews are ...