Attrition in longitudinal studies. How to deal with missing data.

By J. Twisk, W. de Vente. Journal of Clinical Epidemiology, 2002

523 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/63be52f86aa3b0b70528cc50eb226e0998e94854

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In Statistics and Probability

1. Wentao Bao
2. Hélène Jacqmin-Gadda
3. Christophe Genolini
4. René Écochard
5. Kristin L. Sainani

Citations in the corpus

• Loss to Follow-Up in Cohort Studies: How Much is Too Much? (2003, unknown): “Under this assumption, the probability of being lost to follow-up depends on the outcome to be measured and cannot be completely explained by the covariates [12].”
• Missing data imputation: focusing on single imputation. (2016, methodology): “There are also varieties of methods for longitudinal imputation (Table 1) (9-11).”
• Copy Mean: A New Method to Impute Intermittent Missing Values in Longitudinal Studies (2013, background): “Twisk [8] and Engels [3] compared several imputation methods for longitudinal studies.”
• Dealing With Missing Data (2015, methodology): “Simulations show that multiple imputation along with maximum likelihood methods (described later) are optimal for most common missing data scenarios [1-4].”