# Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complications

arXiv: https://arxiv.org/abs/2601.10089 · HTML: https://mlsanity.com/paper/2601.10089/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 70 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [A Step Toward Quantifying Independently Reproducible Machine Learning Research](https://mlsanity.com/paper/1909.06674/) (2019)
- [The Sensitivity of Counterfactual Fairness to Unmeasured Confounding](https://mlsanity.com/paper/1907.01040/) (2019)
- [Lasso Meets Horseshoe : A Survey](https://mlsanity.com/paper/1706.10179/) (2017)
- [Research Reproducibility as a Survival Analysis](https://mlsanity.com/paper/2012.09932/) (2020)
- [Reproducibility in Multiple Instance Learning: A Case For Algorithmic Unit Tests](https://mlsanity.com/paper/2310.17867/) (2023)
- [A Siren Song of Open Source Reproducibility](https://mlsanity.com/paper/2204.04372/) (2022)
- [Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations](https://mlsanity.com/paper/2310.19978/) (2023)
- [Continuously Generalized Ordinal Regression for Linear and Deep Models](https://mlsanity.com/paper/2202.07005/) (2022)
- [State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study](https://mlsanity.com/paper/2205.04797/) (2022)
- [Differentially Private Iterative Screening Rules for Linear Regression](https://mlsanity.com/paper/2502.18578/) (2025)
- [Fixed-effects model: the most convincing model for meta-analysis with few studies](https://mlsanity.com/paper/2002.04211/) (2020)

## Abstract (arXiv)

The meta-analysis's utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician's decisions was not captured. This results in an unknown effect size and unreliable conclusions. A Bayesian approach may allow analysis to determine if the claim of a positive effect is still warranted, and we build a Bayesian approach to this common medical scenario. To demonstrate its utility, we assist professional OBGYNs in evaluating Trial of Labor After a Cesarean-section (TOLAC) situations where few interventions are available for patients and find the support needed for physicians to advance patient care.