Spectrum Bias and Loss of Statistical Power in Discordant Couple Studies of Sexually Transmitted Infections
Bibliographic record
Abstract
BACKGROUND: Discordant couple studies are frequently used to evaluate preventive interventions for sexually transmitted infections (STI). This study design may be vulnerable to spectrum bias when transmission risk is heterogeneous. METHODS: We used Markov models to assess the effect of heterogeneous transmission risk on the ability to detect effective interventions using a discordant couple study design. We also evaluated the implications that such bias may have for statistical power. Models incorporated potential health states in a population of initially infection-discordant couples, according to infection status with a hypothetical STI and participation in a hypothetical clinical research study. We evaluated the effect of length of discordant relationship at time of study enrollment, the shape of distribution describing transmission risk among couples, and the effect of sex-specific differential transmission probabilities, on model outcomes. RESULTS: The results demonstrate that discordant couple studies are prone to spectrum bias, the degree of which is affected by the shape of the underlying transmission probability density function. CONCLUSIONS: Such bias could lead to unexpected study findings, including gender-specific vaccine effects, and loss of statistical power, making this an important and underrecognized consideration in the design and interpretation of discordant couple studies.
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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 | 0.616 | 0.809 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".