Economic evaluation of digitally supported therapy for people with psychosis who hear distressing voices: the AVATAR2 trial
Bibliographic record
Abstract
BACKGROUND: AVATAR therapy, a digitally supported intervention, utilises avatars to promote recovery in people who experience distressing auditory hallucinations. This approach was recently evaluated in a multicentre randomised controlled trial comparing brief (AV-BRF) and extended (AV-EXT) forms of therapy with treatment as usual (TAU). There was evidence for the effectiveness of therapy, particularly for AV-EXT. However, value for money needs to be assessed. AIMS: To compare separately the cost utility of the brief and extended forms of AVATAR therapy with TAU. METHOD: In a three-arm randomised controlled trial the use of health services was measured, and costs (2021/2022; pounds sterling) calculated from a health and social care perspective over a 28-week follow-up period. Quality-adjusted life years (QALYs; derived from the 5-level version of the EuroQol 5-Dimension questionnaire) were combined with costs. RESULTS: AV-BRF resulted in extra costs of £319 (95% CI, -£1558 to £2496), and AV-EXT in lower costs of £1965 (95% CI, -£1912 to £1519), compared with TAU. Over the follow-up, AV-BRF resulted in 0.0159 (95% CI, -0.0103 to 0.0422) and AV-EXT in 0.0173 (95% CI, -0.0049 to 0.0395) more QALYs than TAU. The cost per QALY for AV-BRF compared with TAU was £20 016, while AV-EXT dominated TAU (lower costs and more QALYs). CONCLUSIONS: Neither version of AVATAR had a substantial impact on QALYs. However, AV-EXT did result in reduced care costs - albeit not statistically significant - and was potentially cost-effective compared with TAU. AV-BRF had an incremental cost-effectiveness ratio that indicated lower potential cost-effectiveness. These findings are uncertain, but could still inform decision-making regarding interventions in this field.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".