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Recursive partitioning

Recursive partitioning is a statistical method used in multivariable analysis that generates a decision tree by splitting populations based on independent variables. This recursive splitting continues until a stopping criterion is met. Since the 1980s, popular algorithms like the ID3 algorithm, C4.5, C5.0, and CART have been developed. To address overfitting, ensemble learning methods such as Random Forests combine multiple models. Recursive partitioning is widely applied, including in medical diagnostic tests, where it creates simple classification rules rather than formulas like regression analysis. Variations include Cox linear recursive partitioning.

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Advantages and disadvantages

Compared to other multivariable methods, recursive partitioning has advantages and disadvantages.

  • Advantages are:
    • Generates clinically more intuitive models that do not require the user to perform calculations.3
    • Allows varying prioritizing of misclassifications in order to create a decision rule that has more sensitivity or specificity.4
    • May be more accurate.5
  • Disadvantages are:
    • Does not work well for continuous variables6
    • May overfit data.

Examples

Examples are available of using recursive partitioning in research of diagnostic tests.789101112 Goldman used recursive partitioning to prioritize sensitivity in the diagnosis of myocardial infarction among patients with chest pain in the emergency room.13

See also

References

  1. Breiman, Leo (1984). Classification and Regression Trees. Boca Raton: Chapman & Hall/CRC. ISBN 978-0-412-04841-8. 978-0-412-04841-8 ↩

  2. Cook EF, Goldman L (1984). "Empiric comparison of multivariate analytic techniques: advantages and disadvantages of recursive partitioning analysis". Journal of Chronic Diseases. 37 (9–10): 721–31. doi:10.1016/0021-9681(84)90041-9. PMID 6501544. /wiki/Doi_(identifier) ↩

  3. James KE, White RF, Kraemer HC (2005). "Repeated split sample validation to assess logistic regression and recursive partitioning: an application to the prediction of cognitive impairment". Statistics in Medicine. 24 (19): 3019–35. doi:10.1002/sim.2154. PMID 16149128. /wiki/Doi_(identifier) ↩

  4. Cook EF, Goldman L (1984). "Empiric comparison of multivariate analytic techniques: advantages and disadvantages of recursive partitioning analysis". Journal of Chronic Diseases. 37 (9–10): 721–31. doi:10.1016/0021-9681(84)90041-9. PMID 6501544. /wiki/Doi_(identifier) ↩

  5. Kattan MW, Hess KR, Beck JR (1998). "Experiments to determine whether recursive partitioning (CART) or an artificial neural network overcomes theoretical limitations of Cox proportional hazards regression". Comput. Biomed. Res. 31 (5): 363–73. doi:10.1006/cbmr.1998.1488. PMID 9790741. /wiki/Doi_(identifier) ↩

  6. Lee JW, Um SH, Lee JB, Mun J, Cho H (2006). "Scoring and staging systems using cox linear regression modeling and recursive partitioning". Methods of Information in Medicine. 45 (1): 37–43. doi:10.1055/s-0038-1634034. PMID 16482368. /wiki/Doi_(identifier) ↩

  7. Fonarow GC, Adams KF, Abraham WT, Yancy CW, Boscardin WJ (2005). "Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis". JAMA. 293 (5): 572–80. doi:10.1001/jama.293.5.572. PMID 15687312. https://doi.org/10.1001%2Fjama.293.5.572 ↩

  8. Stiell IG, Wells GA, Vandemheen KL, et al. (2001). "The Canadian C-spine rule for radiography in alert and stable trauma patients". JAMA. 286 (15): 1841–8. doi:10.1001/jama.286.15.1841. PMID 11597285. https://doi.org/10.1001%2Fjama.286.15.1841 ↩

  9. Haydel MJ, Preston CA, Mills TJ, Luber S, Blaudeau E, DeBlieux PM (2000). "Indications for computed tomography in patients with minor head injury". N. Engl. J. Med. 343 (2): 100–5. doi:10.1056/NEJM200007133430204. PMID 10891517. https://doi.org/10.1056%2FNEJM200007133430204 ↩

  10. Edworthy SM, Zatarain E, McShane DJ, Bloch DA (1988). "Analysis of the 1982 ARA lupus criteria data set by recursive partitioning methodology: new insights into the relative merit of individual criteria". J. Rheumatol. 15 (10): 1493–8. PMID 3060613. /wiki/PMID_(identifier) ↩

  11. Stiell IG, Greenberg GH, Wells GA, et al. (1996). "Prospective validation of a decision rule for the use of radiography in acute knee injuries". JAMA. 275 (8): 611–5. doi:10.1001/jama.275.8.611. PMID 8594242. /wiki/Doi_(identifier) ↩

  12. Goldman L, Weinberg M, Weisberg M, et al. (1982). "A computer-derived protocol to aid in the diagnosis of emergency room patients with acute chest pain". N. Engl. J. Med. 307 (10): 588–96. doi:10.1056/NEJM198209023071004. PMID 7110205. /wiki/Doi_(identifier) ↩

  13. Goldman L, Weinberg M, Weisberg M, et al. (1982). "A computer-derived protocol to aid in the diagnosis of emergency room patients with acute chest pain". N. Engl. J. Med. 307 (10): 588–96. doi:10.1056/NEJM198209023071004. PMID 7110205. /wiki/Doi_(identifier) ↩