# Cutting Edge Survival Methods for Epidemiological Data

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA-IRVINE · 2021 · $314,577

## Abstract

The main theme of the research is to develop new methodologies for resolving statistical
issues emerging from our team’s long-term collaborations in cohort studies of aging
populations and patients with kidney disease. We focus on developing robust and
efficient estimating procedures for regression parameters from data with delayed entry in
prevalent cohort studies, making appropriate and efficient statistical inference when
covariates are subject to censoring and measurement error, and developing new
strategies that best model the effects of terminal events on longitudinal measurements.
We also plan to develop publicly available statistical software with the goal of
dissemination and generalization.
The proposed approach for delayed entry is based on an augmented conditional
likelihood constructed from truncation times without specifying the truncation distribution,
which leads to a more efficient estimator. The proposed approach for regression models
for longitudinal data with censored covariates addresses some serious issues with the
common nonparametric and parametric methods. For example, the nonparametric
methods cannot recover any tail information for the censored covariates due to limit of
detection, while the parametric methods may yield biased results due to model
misspecification. Our method will facilitate investigation of covariates subject to limit of
detection together with measurement error, reflecting the reality of many lab measures.
The proposed statistical models for longitudinal data with the occurrence of a terminal
event reflect the reality that the relationship between a response variable and covariates
is approximately the same as the usual marginal model (without considering terminal
event) when data are observed far from the terminal event, but becomes heavily
dependent on the terminal event time when data collecting time is close to the terminal
event. These methods are motivated from and will be applied to a wide range of
datasets, including the kidney disease progression data, the kidney transplant registry
data, the women’s health longitudinal data collected from the Michigan Bone Health and
Metabolism Study and the Study of Women's Health Across the Nation, and the
longitudinal end stage renal disease medical cost data. The methods will be widely
applicable to problems in many other fields of biomedical research.

## Key facts

- **NIH application ID:** 10115561
- **Project number:** 5R01AG056764-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** Bin Nan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $314,577
- **Award type:** 5
- **Project period:** 2018-03-15 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10115561

## Citation

> US National Institutes of Health, RePORTER application 10115561, Cutting Edge Survival Methods for Epidemiological Data (5R01AG056764-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10115561. Licensed CC0.

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