Impact of baseline ECG collection on the planning, analysis and interpretation of ‘thorough’ QT trials
Bibliographic record
Abstract
The current guidelines, ICH E14, for the evaluation of non-antiarrhythmic compounds require a 'thorough' QT study (TQT) conducted during clinical development (ICH Guidance for Industry E14, 2005). Owing to the regulatory choice of margin (10 ms), the TQT studies must be conducted to rigorous standards to ensure that variability is minimized. Some of the key sources of variation can be controlled by use of randomization, crossover design, standardization of electrocardiogram (ECG) recording conditions and collection of replicate ECGs at each time point. However, one of the key factors in these studies is the baseline measurement, which if not controlled and consistent across studies could lead to significant misinterpretation. In this article, we examine three types of baseline methods widely used in the TQT studies to derive a change from baseline in QTc (time-matched, time-averaged and pre-dose-averaged baseline). We discuss the impact of the baseline values on the guidance-recommended 'largest time-matched' analyses. Using simulation we have shown the impact of these baseline approaches on the type I error and power for both crossover and parallel group designs. In this article, we show that the power of study decreases as the number of time points tested in TQT study increases. A time-matched baseline method is recommended by several authors (Drug Saf. 2005; 28(2):115-125, Health Canada guidance document: guide for the analysis and review of QT/QTc interval data, 2006) due to the existence of the circadian rhythm in QT. However, the impact of the time-matched baseline method on statistical inference and sample size should be considered carefully during the design of TQT study. The time-averaged baseline had the highest power in comparison with other baseline approaches.
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 | 0.484 | 0.642 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".