Comparative efficacy of interpersonal psychotherapy and antidepressant medication for adult depression: a systematic review and individual participant data meta-analysis
Bibliographic record
Abstract
Abstract Interpersonal psychotherapy (IPT) and antidepressant medications are both first-line interventions for adult depression, but their relative efficacy in the long term and on outcome measures other than depressive symptomatology is unknown. Individual participant data (IPD) meta-analyses can provide more precise effect estimates than conventional meta-analyses. This IPD meta-analysis compared the efficacy of IPT and antidepressants on various outcomes at post-treatment and follow-up (PROSPERO: CRD42020219891). A systematic literature search conducted May 1st, 2023 identified randomized trials comparing IPT and antidepressants in acute-phase treatment of adults with depression. Anonymized IPD were requested and analyzed using mixed-effects models. The prespecified primary outcome was post-treatment depression symptom severity. Secondary outcomes were all post-treatment and follow-up measures assessed in at least two studies. IPD were obtained from 9 of 15 studies identified (N = 1536/1948, 78.9%). No significant comparative treatment effects were found on post-treatment measures of depression (d = 0.088, p = 0.103, N = 1530) and social functioning (d = 0.026, p = 0.624, N = 1213). In smaller samples, antidepressants performed slightly better than IPT on post-treatment measures of general psychopathology (d = 0.276, p = 0.023, N = 307) and dysfunctional attitudes (d = 0.249, p = 0.029, N = 231), but not on any other secondary outcomes, nor at follow-up. This IPD meta-analysis is the first to examine the acute and longer-term efficacy of IPT v. antidepressants on a broad range of outcomes. Depression treatment trials should routinely include multiple outcome measures and follow-up assessments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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; 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".