Minding non-collapsibility of odds ratios when recalibrating risk prediction models

TL;DR

This paper examines how non-collapsibility affects odds ratio recalibration in clinical prediction models and proposes an improved method to correct for it, enhancing calibration accuracy.

Contribution

It introduces an approximate equation to recover the conditional odds-ratio considering non-collapsibility, improving risk recalibration methods.

Findings

The proposed method reduces under-correction of predicted risks.

Simulations and case studies demonstrate improved calibration accuracy.

Provides R code for practical implementation.

Abstract

In clinical prediction modeling, model updating refers to the practice of modifying a prediction model before it is used in a new setting. In the context of logistic regression for a binary outcome, one of the simplest updating methods is a fixed odds-ratio transformation of predicted risks to improve calibration-in-the-large. Previous authors have proposed equations for calculating this odds-ratio based on the discrepancy between the prevalence in the original and the new population, or between the average of predicted and observed risks. We show that this method fails to consider the non-collapsibility of odds-ratio. Consequently, it under-corrects predicted risks, especially when predicted risks are more dispersed (i.e., for models with good discrimination). We suggest an approximate equation for recovering the conditional odds-ratio from the mean and variance of predicted risks. Brief simulations and a case study show that this approach reduces under-correction, sometimes substantially. R code for implementation is provided.