Adjustment for Selection Bias in Observational Studies with Application to the Analysis of Autopsy Data

• Published in: Neuroepidemiology Vol. 32; no. 3; pp. 229 - 239
• Main Authors: Haneuse, S., Schildcrout, J., Crane, P., Sonnen, J., Breitner, J., Larson, E.
• Format: Journal Article
• Language: English
• Published: Basel, Switzerland S. Karger AG 01.03.2009
• Subjects: Aged; Aged, 80 and over; Autopsy - statistics & numerical data; Dementia - epidemiology; Dementia - pathology; Female; Follow-Up Studies; Humans; Longitudinal Studies; Male; Methods in Neuroepidemiology; Models, Statistical; Selection Bias; Statistics as Topic - methods; Weighted estimating equations; Autopsy; Selection bias; Bootstrap; Regression analysis; Alzheimer's disease; Dementia
• ISSN: 0251-5350; 1423-0208
• DOI: 10.1159/000197389

Background: The interpretation of neuropathological studies of dementia and Alzheimer’s disease is complicated by potential selection mechanisms that can drive whether or not a study participant is observed to undergo autopsy. Notwithstanding this, there appears to have been little emphasis placed on potential selection bias in published reports from population-based neuropathological studies of dementia. Methods: We provide an overview of methodological issues relating to the identification of and adjustment for selection bias. When information is available on factors that govern selection, inverse-probability weighting provides an analytic approach to adjust for selection bias. The weights help alleviate bias by serving to bridge differences between the population from which the observed data may be viewed as a representative sample and the target population, identified as being of scientific interest. Results: We illustrate the methods with data obtained from the Adult Changes in Thought study. Adjustment for potential selection bias yields substantially strengthened association between neuropathological measurements and risk of dementia. Conclusions: Armed with analytic techniques to adjust for selection bias and to ensure generalizability of results from population-based neuropathological studies, researchers should consider incorporating information related to selection into their data collection schemes.