A Framework for Estimating Posttreatment Moderation of Treatment-by-Dosage Effects in Individual-Patient Meta-Analysis: An Illustration Using Project Harmony
Bibliographic record
Abstract
Making causal statements regarding dose-response in treatments for posttraumatic stress disorder (PTSD) and alcohol/other drug use disorders (AODs; PTSD+AOD) is difficult because (a) dosage is rarely randomized and (b) self-selected dosage can be affected by treatment assignment. In the present study, we sought to clarify causal inferences regarding treatment-by-dosage interactions in PTSD+AOD treatment using Project Harmony, an individual-patient meta-analytic data set of behavioral, pharmacological, and combination PTSD+AOD treatments ( k = 36; N = 4,046). Using propensity score weighting and moderated multilevel “net treatment difference” modeling, trauma-focused (TF) treatments, whether integrated or nonintegrated with AOD treatment, outperformed treatment as usual by greater margins on reductions in PTSD and alcohol use as dosage increased. Furthermore, appropriately treating dosage as a posttreatment covariate and moderator revealed effects for TF treatments on drug use that had not been detected in previous studies. Implications for approaches to increasing TF-treatment attendance and greater use of causal-inference methodologies with dose-response analyses are discussed.
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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 | 0.462 | 0.529 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.037 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".