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Table 3 Handling of missing data

From: External validation of multivariable prediction models: a systematic review of methodological conduct and reporting

  Single development & validation articles§(n = 33) Separate development & validation articles
Development cohort Validation cohort Development paper Validation paper**
(n = 66) (n = 45)
Studies with no mention of missing data 13 (39) 21 (64) 30 (45) 21 (47)
Studies reporting number of participants with missing data Information not extracted 5 (15) Information not extracted 18 (40)
Studies reporting number of missing values for each predictor Information not extracted 3 (9) Information not extracted 5 (11)
Studies carrying out complete-case analysis†† 26 (79) 30 (91) 43 (65) 20 (44)
Studies explicitly mentioning carrying out multiple imputation Information not extracted 2 (6) Information not extracted 7 (16)
  1. Percentages are in given parentheses.
  2. §Articles that developed a new model and also evaluated the performance on a separate dataset.
  3. **Articles that only described the evaluation of a previously published prediction model.
  4. ††In the absence of clear reporting, those studies that did not mention how missing data were handled were assumed to have conducted a complete-case analysis.