Missing predictor data increases the minimum sample size needed for stable, well-calibrated clinical prediction models, with context-specific inflation factors up to 2x, via adaptations to posterior sampling calculations.
(46) Altman, D
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Bootstrap-based comparison on real clinical data shows linear modeling of continuous predictors yields stable predictions at smaller sample sizes than more complex methods.
citing papers explorer
-
Incorporating Missing Data Considerations into Sample Size Calculations for Developing Clinical Prediction Models
Missing predictor data increases the minimum sample size needed for stable, well-calibrated clinical prediction models, with context-specific inflation factors up to 2x, via adaptations to posterior sampling calculations.
-
Influence of continuous predictor modelling methods on prediction stability in clinical prediction model development: an empirical comparison using real clinical data
Bootstrap-based comparison on real clinical data shows linear modeling of continuous predictors yields stable predictions at smaller sample sizes than more complex methods.