Bivariate Copula Model on Fitting Correlated Time‐to‐Event Outcomes: Age at First Sex and Age at First Marriage Among Youth in Tanzania
Bibliographic record
Abstract
Traditionally, age at first sex (AFS) and age at first marriage (AFM) have been analysed independently. While useful for summarising risk factors for each outcome individually, these approaches offer limited insight into the interdependence between these events. This study used an Archimedean copula model for bivariate right‐censored data to jointly model AFS and AFM reported by 9726 young people aged 15–24 years in Kisesa, Tanzania. The dependence structure was identified, the degree of association between these events and their associated factors assessed, and the trends of predicted medians examined. Various Archimedean copulas (Ali–Mikhail–Haq, Clayton, Frank, Gumbel, Copula2, and Joe) were evaluated. Copula function selection was based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), and log‐likelihood values, with the Frank copula and a log‐logistic marginal distribution performing best. The Frank copula’s dependency parameter ( θ ) was highly significant, with an estimated θ of 39.49, translating to a Kendall’s τ of 0.903 in the unadjusted model, which included only sex as a covariate, indicating a strong positive correlation between AFS and AFM. Similar results were observed in the adjusted model (Kendall’s τ = 0.89), which incorporated additional variables such as education and residence area. Trends show a better estimation of AFS and AFM for both females and males over the period 1994–2016 when analysed jointly rather than separately. The strong positive correlation suggests these events are highly correlated; hence, using joint models captures interdependence and provides more accurate estimates. This approach can inform targeted interventions to improve youth health outcomes.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".