Control condition design and implementation features in controlled trials: a meta-analysis of trials evaluating psychotherapy for depression
Bibliographic record
Abstract
ABSTRACT: Control conditions are the primary methodology used to reduce threats to internal validity in randomized controlled trials (RCTs). This meta-analysis examined the effects of control arm design and implementation on outcomes in RCTs examining psychological treatments for depression. A search of MEDLINE, PsycINFO, and EMBASE identified all RCTs evaluating psychological treatments for depression published through June 2009. Data were analyzed using mixed-effects models. One hundred twenty-five trials were identified yielding 188 comparisons. Outcomes varied significantly depending control condition design (p < 0.0001). Significantly smaller effect sizes were seen when control arms used manualization (p = 0.006), therapist training (p = 0.002), therapist supervision (p = 0.009), and treatment fidelity monitoring (p = 0.003). There were no significant effects for differences in therapist experience, level of expertise in the treatment delivered, or nesting vs. crossing therapists in treatment arms. These findings demonstrate the substantial effect that decisions regarding control arm definition and implementation can have on RCT outcomes.
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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 | 0.138 | 0.306 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.025 | 0.048 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| 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".