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Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series

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  • Liangyuan Hu

    (Department of Biostatistics and Epidemiology, Rutgers University, Piscataway, NJ 08854, USA)

  • Lihua Li

    (Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA)

Abstract

Tree-based machine learning methods have gained traction in the statistical and data science fields. They have been shown to provide better solutions to various research questions than traditional analysis approaches. To encourage the uptake of tree-based methods in health research, we review the methodological fundamentals of three key tree-based machine learning methods: random forests, extreme gradient boosting and Bayesian additive regression trees. We further conduct a series of case studies to illustrate how these methods can be properly used to solve important health research problems in four domains: variable selection, estimation of causal effects, propensity score weighting and missing data. We exposit that the central idea of using ensemble tree methods for these research questions is accurate prediction via flexible modeling. We applied ensemble trees methods to select important predictors for the presence of postoperative respiratory complication among early stage lung cancer patients with resectable tumors. We then demonstrated how to use these methods to estimate the causal effects of popular surgical approaches on postoperative respiratory complications among lung cancer patients. Using the same data, we further implemented the methods to accurately estimate the inverse probability weights for a propensity score analysis of the comparative effectiveness of the surgical approaches. Finally, we demonstrated how random forests can be used to impute missing data using the Study of Women’s Health Across the Nation data set. To conclude, the tree-based methods are a flexible tool and should be properly used for health investigations.

Suggested Citation

  • Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16080-:d:990266
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    References listed on IDEAS

    as
    1. Liangyuan Hu & Joseph W. Hogan, 2019. "Causal comparative effectiveness analysis of dynamic continuous‐time treatment initiation rules with sparsely measured outcomes and death," Biometrics, The International Biometric Society, vol. 75(2), pages 695-707, June.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    4. Liangyuan Hu & Jiayi Ji & Hao Liu & Ronald Ennis, 2022. "A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations," IJERPH, MDPI, vol. 19(22), pages 1-6, November.
    5. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    6. Liangyuan Hu & Joseph W. Hogan & Ann W. Mwangi & Abraham Siika, 2018. "Modeling the causal effect of treatment initiation time on survival: Application to HIV/TB co†infection," Biometrics, The International Biometric Society, vol. 74(2), pages 703-713, June.
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