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Related Experiment Video

Updated: Apr 13, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Posterior predictive checking of multiple imputation models.

Cattram D Nguyen^1,2, Katherine J Lee^1,2, John B Carlin^1,2

¹Clinical Epidemiology and Biostatistics Unit, Murdoch Childrens Research Institute, The Royal Children's Hospital, Flemington Road Parkville, Victoria, 3052, Australia.

Biometrical Journal. Biometrische Zeitschrift

|May 6, 2015

Summary

This summary is machine-generated.

Posterior predictive p-values can help identify problems with imputation models for missing data. However, extreme values don't always signal issues, and detection worsens with more missing data.

Keywords:

Missing data Model checking Multiple imputation Posterior predictive checking Simulations

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Area of Science:

Statistics
Data Science
Biostatistics

Background:

Multiple imputation is a popular method for handling missing data.
Tools for checking imputation models are scarce, yet model checking is crucial.
Posterior predictive checking (PPC) is a recommended diagnostic for imputation models.

Purpose of the Study:

To evaluate the performance of the posterior predictive p-value as an imputation diagnostic.
To determine if posterior predictive p-values can identify misspecified imputation models.

Main Methods:

Used simulation methods to deliberately misspecify imputation models.
Assessed model fit by comparing analyses from observed data to replicated data from the model.
Calculated posterior predictive p-values as a diagnostic measure.

Main Results:

More extreme posterior predictive p-values (near 0 or 1) were associated with poorer imputation model performance.
Traditional thresholds for classical p-values are not applicable in this context.
The ability of PPC to detect misspecified models decreased as the amount of missing data increased.

Conclusions:

Posterior predictive p-values are a valuable addition to imputation diagnostics, despite limitations.
Graphical checks and examination of other test quantity summaries are recommended alongside p-values.
Imputation model checking is essential for reliable results when handling missing data.