Statistical inference for sequential designs of randomized clinical trials with binary responses
Bibliographic record
Abstract
Sequential designs of Randomized Clinical Trials (RCT) allow repeated significance testing based on cumulative data over time. The sequential testing method enables early termination of the study using a pre-defined stopping rule when preliminary results show a clear superiority of one treatment over the other. Over the decades, researchers have presented several techniques for determining the stopping rule, mainly for continuous data. However, clinical trial data are not necessarily continuous. In certain cases, data can be dichotomous, containing only two distinct values. Some researchers have proposed special sequential testing procedures to analyze binary data considering individual data points at each stage. With the in uence of those approaches, we are more focused on a method which can be used to analyse groups of binary data. The thesis considers the implementation of three main approaches, namely, Pocock [32, 34], O'Brien and Fleming [29] and Haybittle-Peto [31, 15] methods for computing the critical values required for controlling the size and power of tests at various stages of sequential analysis. Critical values are obtained using an iterative Markov chain approach to satisfy the alpha spending at each stage. Considering the discrete nature of the data, a likelihood ratio test statistic is used for testing the proportions. Examples of two-stage and three-stage analysis were used to illustrate the computation of the critical values, size and power of tests of proportions, and then the outcomes based on Pocock, O'Brien & Fleming and Haybittle-Peto methods are compared.
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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 | 0.217 | 0.485 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".