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Subgroup Identification with Classification and Regression Tree-Based Algorithms: an Application to the Ball State Adult Fitness Longitudinal Study

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Abstract

Cardiorespiratory fitness (CRF) is not only an objective measure of physical activity, but also a useful diagnostic and prognostic health indicator for patients in clinical settings. There is a well-established inverse relationship between cardiorespiratory fitness (CRF) and mortality. However, the effect of CRF on mortality status might be different on subgroups of individuals and could be higher or lower than the estimated average effect of CRF. Thus, the objective of the study is to identify subgroups with higher or lower impact of CRF on mortality status. In addition, we evaluate and compare both tree-based and non-tree-based algorithms for identifying predictive features and subgroups. A penalized logistic regression with least absolute shrinkage and selection operator (LASSO) penalty is performed to identify the features that may be associated with low CRF and all-cause mortality. The algorithms considered are: virtual twins classification (VT(C)), generalized unbiased interaction detection and estimation (GUIDE) classification (Gc), GUIDE sum (Gs), GUIDE interaction (Gi) to find subgroups of participants where CRF exerts positive or negative association with all-cause mortality from the Ball State Adult Fitness Longitudinal Lifestyle Study (BALL ST) data. The overall result suggests that tree-based (VT and GUIDE) methods naturally define subgroups with fewer predictors and the non-tree-based method (logistic-LASSO) fails to find subgroups, only identify predictors that have impact on mortality status. In terms of predictive variable selection and subgroup identification, Gi is the best method compared to other tree-based and non-tree-based algorithms. Our study identifies subgroups that may be benefited from higher CRF.

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Data Availability

Sample data available in https://github.com/m0sumy01/data-and-materials

Code Availability

R-codes are available in https://github.com/m0sumy01/r-code

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Author information

Authors and Affiliations

  1. Department of Bioinformatics and Biostatistics, University of Louisville, 485 E. Gray St, Louisville, 40202, KY, USA

    Mst Sharmin Akter Sumy & Md Yasin Ali Parh

  2. Department of Mathematical Sciences, Ball State University, Robert Bell Building (RB), Muncie, 47306, IN, USA

    Munni Begum

  3. Clinical Exercise Physiology Program, Ball State University, Human Performance Laboratory HP230, Muncie, 47306, IN, USA

    Matthew P. Harber, Bradley S. Fleenor & Mitchell Whaley

  4. Department of Educational Psychology, Ball State University, Teachers College (TC), Room 505, Muncie, 47306, IN, USA

    W Holmes Finch

  5. Fisher Institute of Health and Well-being, Ball State University, Muncie, 47306, IN, USA

    James Peterman & Leonard Kaminsky

Authors
  1. Mst Sharmin Akter Sumy
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  2. Munni Begum
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  3. Matthew P. Harber
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  5. Md Yasin Ali Parh
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  7. Mitchell Whaley
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  9. Leonard Kaminsky
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Contributions

SAS contributed to the design of the study, analyzed data and drafted the manuscript. MB contributed to design the study, interpreted results, and provided critical review of the manuscript. MYAP contributed to the conceptualization and reviewing the manuscript. MPH, BF, MW, FW, JP, and LK had full access to all data in our study. MPH and FW took the responsibility to interpret the data and checked the accuracy of the data analysis.

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Correspondence to Munni Begum.

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Sumy, M.S.A., Begum, M., Harber, M.P. et al. Subgroup Identification with Classification and Regression Tree-Based Algorithms: an Application to the Ball State Adult Fitness Longitudinal Study. Bull. Malays. Math. Sci. Soc. 45 (Suppl 1), 445–459 (2022). https://doi.org/10.1007/s40840-022-01328-7

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  • DOI: https://doi.org/10.1007/s40840-022-01328-7

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