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Comparing the predictive powers of survival models using Harrell’s C or Somers’ D

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  • Roger B. Newson

    (National Heart and Lung Institute)

Abstract

Medical researchers frequently make statements that one model pre- dicts survival better than another, and they are frequently challenged to provide rigorous statistical justification for those statements. Stata provides the estat concordance command to calculate the rank parameters Harrell’s C and Somers’ D as measures of the ordinal predictive power of a model. However, no confidence limits or p-values are provided to compare the predictive power of distinct models. The somersd package, downloadable from Statistical Software Components, can provide such confidence intervals, but they should not be taken seriously if they are calculated in the dataset in which the model was fit. Methods are demonstrated for fitting alternative models to a training set of data, and then measuring and comparing their predictive powers by using out-of-sample prediction and somersd in a test set to produce statistically sensible confidence intervals and p-values for the differences between the predictive powers of different models. Copyright 2010 by StataCorp LP.

Suggested Citation

  • Roger B. Newson, 2010. "Comparing the predictive powers of survival models using Harrell’s C or Somers’ D," Stata Journal, StataCorp LP, vol. 10(3), pages 339-358, September.
    • Handle: RePEc:tsj:stataj:v:10:y:2010:i:3:p:339-358
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    References listed on IDEAS

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    1. Roger Newson, 2002. "Parameters behind "nonparametric" statistics: Kendall's tau,Somers' D and median differences," Stata Journal, StataCorp LP, vol. 2(1), pages 45-64, February.
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    1. Stephen F Weng & Jenna Reps & Joe Kai & Jonathan M Garibaldi & Nadeem Qureshi, 2017. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
    2. Buenstorf, Guido & Costa, Carla, 2018. "Drivers of spin-off performance in industry clusters: Embodied knowledge or embedded firms?," Research Policy, Elsevier, vol. 47(3), pages 663-673.
    3. Kalahasthi, Lokesh Kumar & Sánchez-Díaz, Iván & Pablo Castrellon, Juan & Gil, Jorge & Browne, Michael & Hayes, Simon & Sentís Ros, Carles, 2022. "Joint modeling of arrivals and parking durations for freight loading zones: Potential applications to improving urban logistics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 307-329.
    4. Nikolskiy, Ilya & Furmanov, Kirill, 2023. "Assessing the accuracy of efficiency rankings obtained from a stochastic frontier model," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 71, pages 128-142.
    5. Rumyantseva, Ekaterina & Furmanov, Kirill, 2016. "Modeling mortgage survival," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 41, pages 123-143.

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