Handling missing disease information due to death in diseases that need two visits to diagnose.

In studies that assess disease status periodically, time of disease onset is interval censored between visits. Participants who die between two visits may have unknown disease status after their last visit. In this work, we consider an additional scenario where diagnosis requires two consecutive positive tests, such that disease status can also be unknown at the last visit preceding death. We show that this impacts the choice of censoring time for those who die without an observed disease diagnosis. We investigate two classes of models that quantify the effect of risk factors on disease outcome: a Cox proportional hazards model with death as a competing risk and an illness death model that treats disease as a possible intermediate state. We also consider four censoring strategies: participants without observed disease are censored at death (Cox model only), the last visit, the last visit with a negative test, or the second last visit. We evaluate the performance of model and censoring strategy combinations on simulated data with a binary risk factor and illustrate with a real data application. We find that the illness death model with censoring at the second last visit shows the best performance in all simulation settings. Other combinations show bias that varies in magnitude and direction depending on the differential mortality between diseased and disease-free subjects, the gap between visits, and the choice of the censoring time.

Cohort studies were found to be frequently biased by missing disease information due to death.

OBJECTIVES: In epidemiologic cohort studies with missing disease information due to death (MDID), conventional analyses right-censoring death cases at the last observation or at death may yield significant bias in relative risk and hazard ratio estimates. The aim of this study was to investigate susceptibility to this bias and assess its potential direction and magnitude. STUDY DESIGN AND SETTING: Literature review of selected epidemiologic, geriatric, and environmental journals in 2011-2012 and simulation study of various conventional approaches to handling missing disease data. A study was considered susceptible to MDID bias if disease information was collected at follow-up visits only, and a conventional analysis was performed on the data. RESULTS: Of 125 identified studies, 58 (46.4%, 95% confidence interval [CI]: 37.7-55.1%) were classified as susceptible to MDID bias, of which six (10.3%, 95% CI: 2.5-18.2%) attempted to address this in sensitivity analyses. The simulation revealed that depending on the analytic strategy for handling missing disease data, the potential exists for significant under- or over-estimation of risk factor effect estimates. CONCLUSION: Awareness of MDID bias is important as more adequate analysis methods exist permitting an unbiased analysis. Recommendations for better reporting and analysis of MDID are provided.