Repeatability and intraclass correlations from time-to-event data: towards a standardized approach
Bibliographic record
Abstract
Many biological features are expressed as ‘time-to-event’ traits, such as time to first reproduction or time to first response to some stimulus. The analysis of these traits frequently produces right-censored data in cases where no event has occurred within a certain time frame. The Cox proportional hazards (CPH) model, a type of survival analysis, accounts for censored data by estimating the hazard of an event occurring at each time point. While random effect variances can be estimated in CPH models, it is currently not possible to estimate within-cluster variance. Consequently, we lack a general method for calculating ecologically and evolutionary relevant variances and metrics like repeatability from time-to-event data. We here present a solution to this issue. We first describe the characteristics of CPH models and introduce repeatability as an intraclass correlation coefficient (ICC). We demonstrate how CPH models with discrete time intervals are comparable to binomial generalized linear mixed-effects models (GLMMs) with the complementary log-log link. Through this equivalence, we show how to estimate an ICC using the estimates of the random effects variance component(s) resulting from CPH models and the distribution-specific variance (within-cluster variance) from the binomial GLMM. We provide a case study and online materials to demonstrate how our new method for ICC for time-to-event data can be implemented and used. We conclude that the proposed method will not only generate a standard way to quantify consistent individual differences (ICC) from time-to-event data, but also broaden the use of survival analysis outside of the typical implementation for survivorship studies. • Time-to-event data (e.g. latency to respond) are common in animal behaviour. • Survival analysis should be used to account for censored and ceiling values. • We develop a feasible method for estimating repeatability from survival models. • We provide worked examples and a function for implementation of this method in R.
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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 | 0.081 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".