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The handling of missing data in molecular epidemiology studies
Manisha Desai1, Jessica Kubo, Denise Esserman
1Quantitative Sciences Unit, Department of Medicine, Stanford University, Palo Alto, CA 94304, USA. manishad@stanford.edu
Summary
Molecular epidemiology studies frequently have missing data, yet underutilize appropriate statistical methods. This underuse, particularly complete-case analysis, can bias findings in biomarker and imaging research.
Area of Science:
- Epidemiology
- Biostatistics
- Genetics
Background:
- Molecular epidemiology studies often face challenges with incomplete data, particularly concerning biospecimen or imaging results.
- Data availability, especially for biomarkers, is frequently used as a study entry criterion, leading to inherent missingness.
Purpose of the Study:
- To assess the prevalence of missing data in molecular epidemiology studies.
- To evaluate the methods used to address missing data in these studies.
- To provide guidance on handling missing data in molecular epidemiology research.
Main Methods:
- A systematic review of molecular epidemiology studies published in Cancer Epidemiology, Biomarkers & Prevention between January 1, 2009, and March 31, 2010.
- Analysis of 278 studies to identify the presence of missing data and the statistical approaches employed.
Main Results:
- Nearly all assessed studies (95%) exhibited missing data on key variables.
- A significant proportion of studies (45%) used data availability as an inclusion criterion.
- Only 10% of studies compared included versus excluded subjects, and 88% used complete-case analysis, risking biased results.
Conclusions:
- Missing data methods are underutilized in molecular epidemiology, potentially compromising study interpretations.
- Inappropriate handling of missing data can lead to biased and inefficient estimates.
- Guidelines are needed to improve the analysis and interpretation of molecular epidemiology studies with missing data.
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