Attrition rates in trials for adolescents and young adults at clinical high‐risk for psychosis: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Treatment of those at clinical high‐risk (CHR) for developing psychosis may lead to preventive strategies. However, attrition in trials may hamper efforts to detect effective changes and lead to bias. Our objective was to synthesize the relative attrition rates in clinical trials conducted in CHR for psychosis samples. Method We searched the following electronic databases: MEDLINE, Embase, PsycINFO, CINAHL and EBM with no restrictions. Inclusion criteria was any treatment‐based randomized controlled trial (RCT) conducted in CHR samples that reported attrition. Relative attrition rates were calculated using random‐effects meta‐analysis, stratified by time, and reported as odds ratios (ORs), proportions, and 95% confidence intervals (CIs). Results Twenty‐one RCTs met our inclusion criteria, including a total of 2260 CHR participants. Attrition rates between all treatment types identified were not statistically different from control treatments at any time‐point. When accessing overall trial attrition, the pooled attrition rate was 29.57% (95% CI = 23.84‐35.63%) with statistically significant heterogeneity (I2 = 88.70%; P < .001). Furthermore, 11 trials had a subsequent follow‐up after the intervention was conducted and the pooled attrition was 33.96% (95% CI = 24.94‐43.59%). When examining predictors of attrition, no statistically significant subgroup differences were observed in attrition rates. Conclusions Almost one third of CHR participants will not complete participation in an RCT, however no predictors were found to be statistically significantly related to attrition. Methods to account for missing data and attrition are warranted in CHR trials to account for potential biases associated with high attrition rates.
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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 | 0.069 | 0.147 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.047 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".