Likelihood Adaptively Incorporated External Aggregate Information With Uncertainty for Survival Data

Authors

Ziqi Chen
Yu Shen
Jing Qin
Jing Ning

Language

English

Publication Date

10-3-2024

Journal

Biometrics

DOI

10.1093/biomtc/ujae120

PMID

39468742

PMCID

PMC11518850

PubMedCentral® Posted Date

10-28-2024

PubMedCentral® Full Text Version

Post-print

Abstract

Population-based cancer registry databases are critical resources to bridge the information gap that results from a lack of sufficient statistical power from primary cohort data with small to moderate sample size. Although comprehensive data associated with tumor biomarkers often remain either unavailable or inconsistently measured in these registry databases, aggregate survival information sourced from these repositories has been well documented and publicly accessible. An appealing option is to integrate the aggregate survival information from the registry data with the primary cohort to enhance the evaluation of treatment impacts or prediction of survival outcomes across distinct tumor subtypes. Nevertheless, for rare types of cancer, even the sample sizes of cancer registries remain modest. The variability linked to the aggregated statistics could be non-negligible compared with the sample variation of the primary cohort. In response, we propose an externally informed likelihood approach, which facilitates the linkage between the primary cohort and external aggregate data, with consideration of the variation from aggregate information. We establish the asymptotic properties of the estimators and evaluate the finite sample performance via simulation studies. Through the application of our proposed method, we integrate data from the cohort of inflammatory breast cancer (IBC) patients at the University of Texas MD Anderson Cancer Center with aggregate survival data from the National Cancer Data Base, enabling us to appraise the effect of tri-modality treatment on survival across various tumor subtypes of IBC.

Keywords

Humans, Survival Analysis, Registries, Likelihood Functions, Computer Simulation, Female, Breast Neoplasms, Uncertainty, Models, Statistical, Data Interpretation, Statistical, Cohort Studies, aggregate survival information, cancer registry database, data integration, external information incorporated likelihood, inflammatory breast cancer

Published Open-Access

yes

Recommended Citation

Chen, Ziqi; Shen, Yu; Qin, Jing; et al., "Likelihood Adaptively Incorporated External Aggregate Information With Uncertainty for Survival Data" (2024). Faculty, Staff and Student Publications. 6335.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6335