Statistical inference for sequential designs of \nrandomized clinical trials with binary responses
Bibliographic record
Abstract
Sequential designs of Randomized Clinical Trials (RCT) allow repeated significance \ntesting based on cumulative data over time. The sequential testing method enables \nearly termination of the study using a pre-defined stopping rule when preliminary \nresults show a clear superiority of one treatment over the other. Over the decades, researchers \nhave presented several techniques for determining the stopping rule, mainly \nfor continuous data. However, clinical trial data are not necessarily continuous. In \ncertain cases, data can be dichotomous, containing only two distinct values. Some researchers \nhave proposed special sequential testing procedures to analyze binary data \nconsidering individual data points at each stage. With the in \nuence of those approaches, \nwe are more focused on a method which can be used to analyse groups of \nbinary data. \nThe thesis considers the implementation of three main approaches, namely, Pocock \n[32, 34], O'Brien and Fleming [29] and Haybittle-Peto [31, 15] methods for computing \nthe critical values required for controlling the size and power of tests at various stages \nof sequential analysis. Critical values are obtained using an iterative Markov chain \napproach to satisfy the alpha spending at each stage. Considering the discrete nature \nof the data, a likelihood ratio test statistic is used for testing the proportions. Examples \nof two-stage and three-stage analysis were used to illustrate the computation \nof the critical values, size and power of tests of proportions, and then the outcomes \nbased on Pocock, O'Brien & Fleming and Haybittle-Peto methods are compared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.223 | 0.501 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".