Confounding by severity and indication in observational studies of antidepressant effectiveness.
Bibliographic record
Abstract
BACKGROUND: It has been suggested that antidepressants worsen the course of major depressive disorder. Epidemiological data have sometimes been cited in support of this idea, but such estimates are vulnerable to confounding. The objective of this study was to assess episode incidence and recovery in relation to antidepressant use, adjusting for symptom severity. METHODS: Random digit dialing was used to select a sample of n=3304 community residents. Each respondent was then assessed with a baseline interview followed by a series of six subsequent interviews spaced two weeks apart. The brief Patient Health Questionnaire (PHQ-9) was used to detect depressive episodes during follow-up and to provide ratings of symptom severity. Grouped time proportional hazards models were used to assess confounding by producing estimates of the association between antidepressant use and major depression incidence and prognosis adjusted for baseline symptom severity. RESULTS: Antidepressant use in initially non-depressed respondents was associated with a markedly higher incidence of depression (Hazard Ratio, HR = 3.9, 95% CI 1.8 â 8.5). With adjustment for the depression severity score in the two weeks preceding the emergence of a new episode, this effect diminished markedly and was no longer statistically significant (HR = 1.2, 95% CI 0.6 â 2.7, p = 0.57). Antidepressant use was also associated with a lower rate of recovery from major depression (HR = 0.8, 95% CI 0.5 â 1.2, p = 0.27), but this effect also moved towards the null value after adjustment for baseline severity (HR = 0.9, 95% CI 0.6 â 1.5). CONCLUSIONS: Antidepressant medication use is confounded with symptom severity. Observational studies seeming to show harmful effects of antidepressants are subject to bias as a result. Key words: Antidepressive agent; longitudinal studies; epidemiology; methods.
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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 | 0.136 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".