Bibliographic record
Abstract
Surrogate endpoints: wishful thinking or reality?Generally, before a new drug can be accepted for the use in clinical practice, its efficacy and safety needs to be rigorously assessed in a series of clinical trials.This process of testing a new therapy can (and, in fact, does) take many years.One of the reasons is the use of long-term clinical endpoints like clinical progression or survival.However, recent advances in the understanding of the biological mechanisms of disease development have resulted in the emergence of a large number of potentially effective new agents.There is also increasing public pressure for promising new drugs to receive marketing approval as rapidly as possible, in particular for life threatening diseases such as cancer.For these reasons, there is an urgent need to find ways of shortening the duration of cancer clinical trials.A possible solution to this problem is to replace the endpoint of interest, the 'true' endpoint, by another one, a 'surrogate' endpoint, which might be measured earlier or more frequently.However, before a surrogate can replace a true endpoint, it should be validated.This means that it should be checked whether the use of the surrogate leads to correct conclusions about the effect of the treatment on the true endpoint.The validation of a candidate surrogate endpoint is not straightforward.Merely establishing a correlation between both endpoints is not sufficient. 1 Formal methods, allowing for validation of surrogate endpoints, have become the subject of intensive research over the past decades. 2Until recently, the statistical approaches developed for this purpose were based on the definition of a surrogate proposed by Prentice, 3 according to which a surrogate endpoint is 'a response variable for which a test of the null hypothesis of no relationship to the treatment groups under comparison is also a valid test of the corresponding null hypothesis based on the true endpoint.'4][5] These methods suffer from numerous drawbacks: some of them are too stringent to be of practical value, while others are based on non-testable assumptions. 6][9][10] They are based on an alternate definition, according to which 'a surrogate endpoint is expected to predict clinical benefit (or harm or lack of benefit or harm)'. 11These methods use large databases from multiple randomized clinical trials and aim at measuring directly the association between the treatment effects on the surrogate and the true endpoint.At the 33rd International Biometric Conference, which took place on 16-21 July, 2006, in Montreal, a special Topic Contributed Session 'Surrogate Endpoints: Wishful Thinking or Reality?' was devoted to the issue of surrogate endpoint validation.Each speaker was provided with two datasets, containing data from multiple randomized clinical trials in colorectal cancer.The speakers were asked to evaluate, using different
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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 | 0.192 | 0.296 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.053 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.013 | 0.030 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".