PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
January 22, 2026Biostatistics3 citations

Risk functions with outcome measurement error

View Full Paper
JEJohn EdwardsSCStephen R ColePZPaul N Zivich

Key Points

  • This research aims to refine mortality risk estimates by addressing outcome measurement errors in death data.
  • Extended the Rogan-Gladen estimator to include outcome measurement error.
  • Applied the method to a cohort of HIV patients from 2001 to 2022.
  • Conducted simulation studies to validate the approach against potential biases.
  • The new method accurately adjusted mortality risk estimates despite measurement errors.
  • Simulations showed strong performance even with higher-risk participants involved in validation.
  • Bias adjustments improved the reliability of survival estimates in the cohort.

Abstract

Summary Mortality risk estimated from studies that ascertain date of death through linkage to vital statistics registries may be subject to outcome measurement error. As a result, some deaths among study participants may not be captured, some study participants who are alive may be falsely categorized as deceased, and some deaths may be recorded at incorrect times, leading to bias in estimates of mortality risk and survival. Here, we illustrate an extension of the Rogan-Gladen estimator to account for outcome measurement error in risk and survival functions in settings with right censoring. As a motivating application, we consider and account for outcome measurement error that could be induced by incomplete and/or incorrect linkage to death registries when estimating mortality risk among people entering care for HIV in the University of North Carolina Center for AIDS Research HIV Clinical Cohort between 2001 and 2022. A series of simulation studies demonstrates that the approach performed well even when participants selected into the validation study were at higher mortality risk than the main study. The proposed approach may be parameterized using internal or external validation data or used as a form of quantitative bias analysis.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Edwards et al. (2026) studied this question.

synapsesocial.com/papers/6971bd26642b1836717e1d45https://doi.org/10.1093/biostatistics/kxaf052
Ask AI
Helpful
Bookmark
Share
View Full Paper