Estimation of a generalized linear mixed model for response‐adaptive designs in multi‐centre clinical trials
Bibliographic record
Abstract
Abstract Response‐adaptive designs are important alternatives to equal allocation in clinical trials because equal treatment allocation has been found to have ethical issues. In this article we discuss the implementation of response‐adaptive designs in multi‐centre clinical trials. We develop a generalized linear mixed model (GLMM) for analyzing data obtained from multi‐centre clinical trials and use the maximum likelihood (ML) approach to estimate the model parameters. We apply influence function techniques to derive the asymptotic properties of our estimators. The advantage of using the influence function approach is that it leads to a closed form expression for the asymptotic covariance of the estimated parameters. To our knowledge such a closed form expression does not currently exist in the literature. The performance of the ML estimator under various response‐adaptive designs is examined through simulation studies. We use our simulation studies to compare the asymptotic covariance matrix, based on the influence function to that based on the inverse of the Hessian matrix obtained from the likelihood function of the observations. The techniques are applied to real data obtained from a multi‐centre clinical trial designed to compare two cream preparations (active drug/control) for treating an infection. The Canadian Journal of Statistics 45: 310–325; 2017 © 2017 Statistical Society of Canada
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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 | 0.305 | 0.369 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".