Comparing Imputation Methods for Clinical Prediction Model Development under Complex Missingness Scenarios: A Simulation Study Using Real-World Cardiac Data

Evidence remains limited on how missing-data strategies affect the stability of clinical prediction models across different predictor-outcome relationships and degrees of missingness.

We conducted a simulation study using a fully observed real-world cardiac cohort of 8,245 patients, equally divided into development and external validation cohorts.

Missing data were induced under a missing-at-random mechanism across 18 scenarios varying by variable type, predictor-outcome relationship, and missingness proportion.

Five strategies were compared: complete case analysis, multiple imputation by chained equations with fully conditional specification, multiple imputation using predictive mean matching, missForest, and k-nearest neighbours.

Logistic regression models were developed using backward stepwise elimination.

Outcomes included optimism-corrected AUC, calibration slope, mean absolute prediction error, external validation performance, and computation time.

When missingness involved isolated linear or categorical variables at 30%-60%, all methods maintained discrimination comparable to the complete-data model, with median AUCs of about 0.75.

When missingness involved isolated non-linear variables or more complex patterns, predictive performance and calibration worsened as missingness increased, especially at 90%.

In complex scenarios, multiple imputation showed greater prediction instability and overfitting, while missForest performed well internally but overfitted externally. k-nearest neighbours showed the most consistent performance, with stable predictions, better external validation results, and the shortest computation time.

The optimal strategy may depend on the characteristics of variables with missing data.

In sufficiently large development samples, k-nearest neighbours may provide a computationally efficient alternative.