Bias in the case-only design applied to studies of gene-environment and gene-gene interaction: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The case-only study, proposed as a design specifically for assessing departure from multiplicative gene-environment and gene-gene interactions, is of considerable potential value but there are concerns about its validity. The objective of this study was to evaluate the extent and sources of bias in the case-only design by means of a systematic review and meta-regression analysis. METHODS: The MEDLINE, CINAHL, EMBASE and PUBMED databases were searched through to 7 October 2009. Studies that assessed bias in the case-only design applied to the study of gene-environment and gene-gene interaction were identified. Qualitative comments on the sources and extent of bias were extracted. A meta-regression analysis of the ratio (IOR(CC)/IOR(CO)) of the case-control (IOR(CC)) and case-only (IOR(CO)) interaction odds ratios was conducted based on studies in which both methods were applied to the same data set. RESULTS: The search yielded 365 unique articles of which 38 met the inclusion criteria. Potential sources of bias in the case-only design included non-independence of genotype and exposure in the source population. Meta-regression analysis, based on 24 evaluations, produced a mean IOR(CC)/IOR(CO) of 1.06 [95% confidence interval (95% CI) 0.93-1.22], suggesting that bias in case-only designs is not common in practice. The I(2) statistic indicated that 23.9% (95% uncertainty interval 0-53.9%) of the observed variation was due to heterogeneity between studies, which was not explained by any methodological characteristics of the included studies. CONCLUSION: As understanding of the relationships between genes and environmental exposures in the population improves, the case-only design may prove to be of considerable value.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.213 | 0.408 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.022 | 0.030 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".