# Improving statistical inference when interest focuses on the identification of extreme random effects in clustered data

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $264,860

## Abstract

PROJECT ABSTRACT
 Statistical models that generate predicted random effects are widely used to evaluate the status of and rank
patients, physicians, hospitals and health plans from longitudinal and clustered data. Predicted random effects
have been proven to outperform simpler approaches such as standard regression models, on average.
 These predicted random effects are often used to identify extreme or outlying values, such as elderly patients
with rapid declines in their health or poorly performing hospitals. When interest focuses on the extremes rather
than performance on average, there has been no systematic investigation of best approaches. We propose to
develop novel methods for prediction of extreme or outlying values and systematically evaluate their performance
using theoretical calculations, simulations and examples.
 Merely predicting extreme or outlying values is rarely sufficient and decision rules for identifying extreme
values in a statistically rigorous manner are also needed. We will develop such approaches and provide easy-
to-use software to implement the recommended methods.

## Key facts

- **NIH application ID:** 10492444
- **Project number:** 5R01AG071535-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Charles E McCulloch
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $264,860
- **Award type:** 5
- **Project period:** 2021-09-30 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10492444

## Citation

> US National Institutes of Health, RePORTER application 10492444, Improving statistical inference when interest focuses on the identification of extreme random effects in clustered data (5R01AG071535-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10492444. Licensed CC0.

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