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- Table 1.
Guidelines for statistical analysis and reporting of microarray studies for epidemiology
Checklist Comment Do Clearly state the causal contrasts being studied and how exposures were defined. Consider and discuss potential exposure misclassification and its impact on findings. Do Identify potential confounders using appropriate causal diagrams and adjust for them using statistical methods. Age may be an important confounder of exposure-gene expression associations. Do Discuss how the genomic methods used may have created selection biases. Do Use class prediction on independent test sets and report sensitivity and specificity of predictors. Don't Misuse standard terminology such as “test set” or unsupervised. Do Report measures of internal reproducibility. Report intraclass correlation coefficients for duplicates. Do Supplement technical validation (e.g., quantitative PCR) with external validation where possible. Use publicly deposited data to confirm pathways affected by exposure. Do Deposit data in public databases at the time of review and before publication. NOTE: This table is formatted as a supplement to a table presented by Dupuy and Simons (13).
Additional Files
Related Article
The following related article is available for this Editorial:
Microarrays and Epidemiology: Not the Beginning of the End but the End of the Beginning...
Penelope M. Webb, Melissa A. Merritt, Glen M. Boyle and Ad�le C. Green
January 2009
Volume 18, Issue 1
Volume 18, Issue 1
Microarrays and Epidemiology: Ensuring the Impact and Accessibility of Research Findings
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- Article
- Introduction
- Causal Contrasts and Genomic Data: Controlling Confounding and Avoiding Biases
- Developing Formal Guidelines for Review of Observational Genomic Studies?
- The Necessity of Public Data Access for Establishing Reproducibility and Increasing Study Impact
- Disclosure of Potential Conflicts of Interest
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