Impact of independence assumption for joint predictor distributions prior to data collection

Investigate the impact of assuming conditional independence among core predictors when only marginal distributions are available and the joint correlations are unknown during sample size planning, including how this assumption affects individual-level uncertainty intervals and classification instability in clinical prediction models.

Background

When planning studies before data are available, the joint distribution of predictors may be unknown. The authors suggest a pragmatic initial assumption of conditional independence among predictors to simulate synthetic data for their sample size approach.

They explicitly acknowledge that the consequences of this assumption need further investigation to understand its effect on the precision and robustness of individual-level predictions.