The Prevention and Treatment of Missing Data in Clinical Trials

By Roderick J. A. Little, Ralph B. D’Agostino, Michael L Cohen, K. Dickersin, S. Emerson, John T. Farrar, C. Frangakis, Joseph W. Hogan, Geert Molenberghs, Susan A. Murphy and 6 more. 2012

1,824 citations recorded by the source corpus.

Missing data in clinical trials can have a major effect on the validity of the inferences that can be drawn from the trial. This article reviews methods for preventing missing data and, failing that, dealing with data that are missing.

Subfields: Statistics and Probability

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Citations in the corpus

Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, background): “There is a large literature on testing the validity of the assumptions made in multiple imputation, as well as a variety of sensitivity analyses (Little and Rubin 1987; Robins et al. 1999; Scharfstein et al. 1999; Gelman et al. 2005; Carpenter et al. 2007; NAP 2010).”
Flexible Imputation of Missing Data (2012, background): “Potthoff and Roy (1964) published classic data on a study in 16 boys and 11 girls, who at ages 8, 10, 12, and 14 had the distance (mm) from the center of the pituitary gland to the pteryomaxillary fissure measured.”
A tutorial on sensitivity analyses in clinical trials: the what, why, when and how (2013, methodology): “Bayesian methods using statistical models that assume a prior distribution for the missing data can also be used to impute data [35].”
Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses (2016, background): “For simplicity, suppose there are no losses to follow-up, which would require adjustment for potential selection bias [6].”
When and how should multiple imputation be used for handling missing data in randomised clinical trials – a practical guide with flowcharts (2017, background): “Sensitivity analyses may show how assumptions, different from those made in the primary analysis influence the results obtained [3, 6].”
Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. (2016, methodology): “All analyses will be adjusted for pre- and postbaseline prognostic factors associated with loss to follow-up (57).”
Global sensitivity analysis for repeated measures studies with informative drop‐out: A semi‐parametric approach (2018, background): “For such untestable assumptions, it is important to conduct a sensitivity analysis to evaluate the robustness of the resulting inferences (e.g., ICH, 1998; CHMP, 2009; Little et al., 2010).”
Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings (2019, neutral): “386 Matteo Mantovani, Carlo Combi, and Milos Hauskrecht Unsupervised Learning Inferring Temporal Phenotypes with Topological Data Analysis and Pseudo Time-Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Arianna Dagliati, Nophar Geifman, Niels Peek, John H. Holm”