# New Statistical Methods for Cox Regression with Measurement Errors in Cancer and Nutritional Epidemiology

> **NIH NIH R03** · YALE UNIVERSITY · 2022 · $83,750

## Abstract

Project Summary / Abstract
One of the greatest challenges in cancer and nutritional epidemiology is the uncertainty in covariate
measurements, which is a major source of bias in research aimed at elucidating causal relationships between
diet and cancer incidence and mortality. Dietary intake is usually estimated using a self-report food-frequency
questionnaire, and this dietary assessment is often subject to substantial measurement error. The issue of
exposure uncertainty lead to the potential for considerable bias in estimated health effects, masking our ability
to detect true associations. Right-censored survival outcomes are common in epidemiologic practice, and the
Cox regression model is typically used to model such outcomes. Regression calibration is a simple
measurement error adjustment method in Cox regression, and is often used in cancer and nutritional
epidemiology. However, when the degree of measurement error is large and the regression coefficient of the
error-prone covariate is large, regression calibration is unsatisfactory for bias correction due to exposure
uncertainty. We will develop an improved regression calibration method considering the degree of
measurement error and the coefficient of the error-prone covariate simultaneously in the calibration procedure.
The Expectation-Maximization (EM) algorithm has been remarkably applied for a wide variety of situations for
incomplete data problems. However, EM does not receive much attention to deal with measurement error
problems in survival data. We will develop a complete treatment of EM in main / validation studies to fill an
important gap in the literature. User-friendly publicly available software development will be a central feature
accompanying all new methods to be developed.

## Key facts

- **NIH application ID:** 10409754
- **Project number:** 5R03CA252808-02
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Xin Zhou
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $83,750
- **Award type:** 5
- **Project period:** 2021-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10409754

## Citation

> US National Institutes of Health, RePORTER application 10409754, New Statistical Methods for Cox Regression with Measurement Errors in Cancer and Nutritional Epidemiology (5R03CA252808-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10409754. Licensed CC0.

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