Collider and Reporting Biases Involved in the Analyses of Cause of Death Associations in Death Certificates: an Illustration with Cancer and Suicide

Abstract

Background: Data from death certificates have been studied to explore causal associations between diseases. However, these analyses are subject to collider and reporting biases (selection and information biases, respectively). Methods: We aimed to assess to what extent associations of causes of death estimated from individual mortality data can be extrapolated to the general population. We used a multistate model to generate populations of individuals and simulate their health states up to death from national health statistics and artificially replicate collider bias. Associations between health states can then be estimated from such simulated deaths by logistic regression and the magnitude of collider bias assessed. Reporting bias can be approximated by comparing the estimates obtained from the observed death certificates (subject to collider and reporting biases) with those obtained from the simulated deaths (subject to collider bias only). Results: As an illustrative example, we estimated the association between cancer and suicide in French death certificates, and found that cancer was negatively associated with suicide. Collider bias, due to conditioning inclusion in the study population on death, increasingly downwarded the associations with cancer site lethality. Reporting bias was much stronger than collider bias and depended on the cancer site, but not prognosis. Conclusions: These results argue for an assessment of the magnitude of both collider and reporting biases before performing analyses of cause of death associations exclusively from death certificates. If these biases cannot be corrected, results from these analyses should not be extrapolated to the general population.

Analysis of Multiple Causes of Death: A Review of Methods and Practices

“… 13 Further, nearly all estimates of the associations between causes mentioned on the death certificates may be influenced by some level of Berkson’s paradox or collider bias. 47 , 106 This is a form of selection bias that occurs when both the exposure and outcome variables (the 2 causes of death) influence the inclusion of participants in a study (death certificate data). 47 Methods for grouping causes of death facilitate the assessment of the complex relationships between causes that may go unnoticed by pairwise analysis.…”

Analysis of multiple causes of death: a review of methods and practices

“…13 Further, nearly all estimates of the associations between causes mentioned on the death certificates may be influenced by some level of Berkson's paradox or collider bias. 101,102 This is a form of selection bias that occurs when both the exposure and outcome variables (the two causes of death) influence the inclusion of participants in a study (death certificate data). 101 Methods for grouping causes of death facilitate assessment of the complex relationships between causes that may go unnoticed by pairwise analysis.…”