Evaluating the Validity of a Two-stage Sample in a Birth Cohort Established from Administrative Databases
Bibliographic record
Abstract
BACKGROUND: When using administrative databases for epidemiologic research, a subsample of subjects can be interviewed, eliciting information on undocumented confounders. This article presents a thorough investigation of the validity of a two-stage sample encompassing an assessment of nonparticipation and quantification of the extent of bias. METHODS: Established through record linkage of administrative databases, the Québec Birth Cohort on Immunity and Health (n = 81,496) aims to study the association between Bacillus Calmette-Guérin vaccination and asthma. Among 76,623 subjects classified in four Bacillus Calmette-Guérin-asthma strata, a two-stage sampling strategy with a balanced design was used to randomly select individuals for interviews. We compared stratum-specific sociodemographic characteristics and healthcare utilization of stage 2 participants (n = 1,643) with those of eligible nonparticipants (n = 74,980) and nonrespondents (n = 3,157). We used logistic regression to determine whether participation varied across strata according to these characteristics. The effect of nonparticipation was described by the relative odds ratio (ROR = ORparticipants/ORsource population) for the association between sociodemographic characteristics and asthma. RESULTS: Parental age at childbirth, area of residence, family income, and healthcare utilization were comparable between groups. Participants were slightly more likely to be women and have a mother born in Québec. Participation did not vary across strata by sex, parental birthplace, or material and social deprivation. Estimates were not biased by nonparticipation; most RORs were below one and bias never exceeded 20%. CONCLUSIONS: Our analyses evaluate and provide a detailed demonstration of the validity of a two-stage sample for researchers assembling similar research infrastructures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.206 | 0.607 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".