Analysis of Virological Efficacy in Trials of Antiretroviral Regimens: Drawbacks of Not Including Viral Load Measurements after Premature Discontinuation of Therapy
Bibliographic record
Abstract
OBJECTIVES: To compare two analytic approaches to assess the virological effect of HAART according to the intention-to-treat (ITT) principle. MATERIAL: Data from 2318 patients enrolled in 10 randomised clinical trials (RCTs) and from 3091 patients followed in an observation cohort (EuroSIDA) starting their first HAART regimen. METHODS: Two classifications of defining virological response 48 weeks after starting the therapy to be evaluated were compared: 1) only patients remaining on the therapy and having a plasma viral load (pVL) below a given cut-off level at week 48 were classified as responders (ITT/s=f); and 2) patients with a pVL below a given cut-off at week 48 whether they remained on initial assigned therapy or switched therapy were responders (ITT/s incl). In both analyses, patients with missing data at week 48 were classified as failures (i.e., non-responders). RESULTS: According to ITT/s=f, 22-70% of the patients starting a HAART regimen in a RCT experienced a virological response at week 48. Only two RCTs had complete follow-up data (n=424): between 29 and 62% achieved a virological response at week 48 in the six treatment arms evaluated in the studies according to ITT/s=f, and 41-72% according to ITT/s incl. Among those who discontinued the therapy to be evaluated in these two trials, 13-45% (cohort: 39-74%) subsequently experienced a virological response at week 48. The subsequent response rates were associated with the reason for discontinuation (toxicity versus confirmed virological failure: 63 vs 33%), varied largely across regimens and were not associated with the discontinuation rate. CONCLUSIONS: Discontinuation of follow-up at switch from the therapy to be evaluated remains common in antiretroviral treatment trials, but leads to an imprecise and incomplete assessment of the intrinsic effect of a given regimen. Complete follow-up of all patients should be encouraged strongly as this will allow for several complementary analytic approaches and a focus on optimal treatment strategies rather than specific regimens.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".