Bayesian Analysis of Time-To-Event Data in a Cluster-Randomized Trial: Major Outcomes With Personalized Dialysate TEMPerature (MyTEMP) Trial
Bibliographic record
Abstract
Background: MyTEMP was a cluster-randomized trial to assess the effect of using a personalized cooler dialysate compared to standard temperature dialysate for potential cardiovascular benefits in patients receiving maintenance hemodialysis in Ontario, Canada. Objective: To conduct Bayesian analyses of the MyTEMP trial, which sought to determine whether adopting a center-wide policy of personalized cooler dialysate is superior to a standard dialysate temperature of 36.5°C in reducing the risk of a composite outcome of cardiovascular-related deaths or hospitalizations. Design: Secondary analysis of a parallel-group cluster-randomized trial. Setting: In total, 84 dialysis centers in Ontario, Canada, were randomly allocated to the 2 groups. Patients: Adult outpatients receiving in-center maintenance hemodialysis from dialysis centers participating in the trial. Measurements: The primary composite outcome was cardiovascular-related death or hospital admission with myocardial infarction, ischemic stroke, or congestive heart failure during the 4-year trial period. Methods: MyTEMP trial data were analyzed using Bayesian cause-specific parametric Weibull methods to model the survival time with 6 pre-defined reference priors of normal distributions on the log hazard ratio for the treatment effect (strongly enthusiastic, moderately enthusiastic, non-informative, moderately skeptical, skeptical, strongly skeptical). For each analysis, we reported the posterior mean, 2nd, 50th, and 98th percentiles of the treatment effects (hazard ratios) and 96% credible interval (CrI). We also reported the estimated posterior probabilities for different magnitudes of treatment effects. Results: Regardless of priors, Bayesian analysis yielded consistent posterior means and a 96% CrI. The posterior distribution of the hazard ratio was concentrated between 0.95 and 1.05, indicating there was probably no substantial difference between the 2 trial arms. Limitations: The interpretation of Bayesian methods highly depends on the prior distributions. In our study, the prior distributions were determined by 2 experts without a formal elicitation method. A formal elicitation is encouraged in future trials to better quantify experts' uncertainty about the treatment effect. In addition, we used cause-specific parametric Weibull methods to model survival time, as semi-parametric methods were not available in the standard Bayesian statistical software package at the time of analysis. Conclusions: Our Bayesian analysis indicated that implementing personalized cooler dialysate as a center-wide policy is unlikely to yield meaningful benefits in reducing the composite outcome of cardiovascular-related deaths and hospitalizations, regardless of prior expectations, whether optimistic or skeptical, about the intervention's effectiveness.
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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 | 0.030 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".