Strategy for intention to treat analysis in randomised trials with missing outcome data

By Ian R. White, Nicholas J. Horton, James R. Carpenter, reader in medical and social statistics, Stuart J. Pocock. British medical journal, 2011

818 citations recorded by the source corpus.

Loss to follow-up is often hard to avoid in randomised trials. This article suggests a framework for intention to treat analysis that depends on making plausible assumptions about the missing data and including all participants in sensitivity analyses

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c400e38cafae97f9f56ecbeda3e95c9c69f63287

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Citations in the corpus

Accounting for missing data in statistical analyses: multiple imputation is not always the answer (2019, neutral): “There may also be concerns specific to the type of study being analysed: for example, respecting the intention to treat principle in randomized trials.(59) We have considered bias of a CCA when the analysis is a linear or logistic regression model.”
Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis (2012, methodology): “Therefore, estimates obtained by using the missing-indicator method will be more precise than those obtained by complete case analysis,(4) and they will also obey the intention-totreat principle by including all participants randomly assigned to treatment groups.(13) Missingness of baseline covariat”