Jonas Esser, Mateus Maia, Andrew C. Parnell, Judith Bosmans, Hanneke van Dongen, Thomas Klausch, Keefe Murphy
arXiv 2 Apr 2024 · Statistics — Methodology · publishedThe Annals of Applied Statistics (2025) · 1 citations (OpenAlex)
arXiv:2404.02228 · PDF · DOI · OpenAlex · Extracted main text
In recent years, theoretical results and simulation evidence have shown Bayesian additive regression trees to be a highly-effective method for nonparametric regression. Motivated by cost-effectiveness analyses in health economics, where interest lies in jointly modelling the costs of healthcare treatments and the associated health-related quality of life experienced by a patient, we propose a multivariate extension of BART which is applicable in regression analyses with several dependent outcome variables. Our framework allows for continuous or binary outcomes and overcomes some key limitations of existing multivariate BART models by allowing each individual response to be associated with different ensembles of trees, while still handling dependencies between the outcomes. In the case of continuous outcomes, our model is essentially a nonparametric version of seemingly unrelated regression. Likewise, our proposal for binary outcomes is a nonparametric generalisation of the multivariate probit model. We give suggestions for easily interpretable prior distributions, which allow specification of both informative and uninformative priors. We provide detailed discussions of MCMC sampling methods to conduct posterior inference. Our methods are implemented in the R package "subart". We showcase their performance through extensive simulation experiments and an application to an empirical case study from health economics. By also accommodating propensity scores in a manner befitting a causal analysis, we find substantial evidence for a novel trauma care intervention's cost-effectiveness.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chipman, H. A., George, E. I., and McCulloch, R. E (2010) BART: Bayesian additive regression trees | 1.000 | 17 | 4 | 100% |
| 2 | Wiertsema, S. H., Van Dongen, J. M., Geleijn, E., Huijsmans, R. J.,… (2019) Cost-effectiveness of the transmural trauma care model (TTCM) for the rehabilitation of trauma patients | 1.000 | 12 | 4 | 100% |
| 3 | Hahn, P. R., Murray, J. S., and Carvalho, C. M (2020) Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (with discussion) | 1.000 | 8 | 4 | 100% |
| 4 | Um, S., Linero, A. R., Sinha, D., and Bandyopadhyay, D (2023) Bayesian additive regression trees for multivariate skewed responses | 1.000 | 5 | 4 | 100% |
| 5 | Dorie, V., Hill, J. L., Shalit, U., Scott, M., and Cervone, D (2019) Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition | 1.000 | 5 | 3 | 100% |
| 6 | Li, F., Ding, P., and Mealli, F (2023) Bayesian causal inference: a critical review | 1.000 | 5 | 3 | 100% |
| 7 | Zhang, X., Boscardin, W. J., and Belin, T. R (2006) Sampling correlation matrices in Bayesian models with correlated latent variables | 0.928 | 4 | 4 | 100% |
| 8 | Huang, A. and Wand, M. P (2013) Simple marginally noninformative prior distributions for covariance matrices | 0.928 | 4 | 3 | 100% |
| 9 | Kapelner, A. and Bleich, J (2016) bartMachine: machine learning with Bayesian additive regression trees | 0.928 | 4 | 3 | 100% |
| 10 | Gabrio, A., Baio, G., and Manca, A (2019) Bayesian statistical economic evaluation methods for health technology assessment | 0.874 | 5 | 2 | 100% |
Showing the top 10 of 72 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Nonparametric regression for cost-effectiveness analyses with observational data - a tutorial | 0.811 | 4 | 2 |