Inferring meaningful change in quality of life with posterior predictive distribution: an alternative to standard error of measurement.

ABSTRACT:

Objective

In the absence of population-based information, distribution-based meaningful change metrics have been previously found to perform similarly. Yet, it is unknown how a Bayesian approach derived from Posterior Predictive Distribution (PPD) of anticipated changes would compare against existing metrics.

Methods

PPD defines meaningful change as change scores that exceed the amount expected from the posterior predictive distribution given a previous score. The PPD adjusts for common statistical phenomena that arise in a pre-test-post-test setting, such as regression to the mean and post-test drift. The PPD was compared to Reliable Change Index (RCI) and Gulliksen-Lord-Novick (GLN) methods using published real-world data and simulated hypothetical data, respectively.

R

SUBMITTER: Li Y 

PROVIDER: S-EPMC10202216 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature