The STAR*D Trial: It is Time to Reexamine the Clinical Beliefs That Guide the Treatment of Major Depression
Bibliographic record
Abstract
The STAR*D trial is the largest and most consequential antidepressant study ever conducted, with over 120 journal articles published by study investigators, innumerable citations of STAR*D's findings by other researchers, and extensive coverage in the media, thereby giving it an oversized impact on the of depression, worldwide. Funded at a cost of 35-million US dollars, STAR*D enrolled 4041 patients who screened positive for major depression while seeking routine medical or psychiatric care. In contrast most industry-funded trials, STAR*D included depressed patients with comorbid conditions, thereby increasing the generalizability of its findings and further, provided 12 months of free continuing care monitor the durability of effects.STAR*D provided up 4 steps per patient and was designed give guidance in selecting the best next-step for the many patients who fail get sufficient relief from their initial AD. Each step consisted of a 12-week, open-label trial, with an additional 2 weeks for patients deemed close remission. ADs were administered using a system of measurement-based care that involved assessing symptoms and side effects at each visit guide aggressive medication dosing ensure that the likelihood of achieving remission was maximized and that those who did not reach remission were truly resistant the medication.1, 30STAR*D allowed patients select options for randomization in steps 2 4 to empower patients, strengthen the therapeutic alliance, optimize adherence, and improve outcome2, 483 and evaluated the relative effectiveness of 11 pharmacologically distinct drug-drug combination treatments in 5 head-to-head comparisons. CT was also available as a switch or drug augmentation option in step 2, but too few patients included it as an acceptable treatment, resulting in only 101 contributing data after randomization. Therefore, CT was excluded from the step 2 switch and augmentation analyses.3,4 4Patients who achieved remission during step were encouraged enter the 12 months of free follow-up care, as were responder patients who failed attain remission but did not want continue the next step as would involve a change in medication. Remission was defined as a score of less than 8 on the HRSD, the study's prespecified primary outcome measure, and response as a 50% or greater reduction in depressive symptoms on it. The follow-up protocol strongly recommended that participants continue the previously effective acute medication(s) at the doses used in acute treatment but treating physicians were allowed make any psychotherapy, medication, or medication dose change5, 1908 they deemed necessary sustain a positive outcome during follow-up, including scheduling additional visits if depressive symptoms returned and (or) intolerable side effects emerged.6Prior Criticisms of Apparent Bias in the Reporting of STAR*D FindingsPigott and colleagues7,8 have previously criticized the investigators for the following:1) not reporting in their summary article5 remission and response rates using the prespecified HRSD but instead using the QIDS-SR, a nonblinded, clinic-administered assessment that was excluded from use as a research measure in the Research Protocol9;2) excluding from analysis patients who started on citalopram in their baseline visit and then dropped out without taking the exit HRSD despite the investigators' statement that our primary analyses classified patients with missing exit HRSD scores as nonremitters a priori1, 43 and these early dropout patients therefore should have been counted as failures as prespecified;3) asserting in their summary article a theoretical cumulative remission rate of 67% with the unrealistic provisos that this estimate assumes no dropouts, and it assumes that those who exited the study would have had the same remission rates as those who stayed in the protocol5, and4) other indicators of bias (see eTable 1). …
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".